May 19 2007 12:10:14.833 PM STARPAC_PRB: FORTRAN90 version Tests for the STARPAC statistical package. XACF Test the time series correlation routines. Test of ACF: starpac 2.08s (03/15/90) autocorrelation analysis average of the series = .1450000 standard deviation of the series = 1.277758 number of time points = 100 largest lag value used = 33 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.54 0.34 0.22 0.26 0.27 0.15 0.06 0.03 0.06 0.02 -0.04 -0.11 standard error 0.10 0.12 0.13 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 lag 13 14 15 16 17 18 19 20 21 22 23 24 acf -0.14 -0.10 -0.10 -0.11 -0.12 -0.07 0.03 0.04 0.02 -0.03 0.02 0.06 standard error 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 lag 25 26 27 28 29 30 31 32 33 acf 0.05 -0.03 -0.04 -0.06 -0.06 -0.03 -0.09 -0.16 -0.17 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 the chi square test statistic of the null hypothesis of white noise = 79.54 degrees of freedom = 33 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++++++++++++++++ i .33597 3.0000 i ++++++++++++ i .21955 4.0000 i ++++++++++++++ i .26190 5.0000 i +++++++++++++++ i .27456 6.0000 i +++++++++ i .15137 7.0000 i ++++ i 0.56358E-01 8.0000 i +++ i 0.30065E-01 9.0000 i ++++ i 0.58569E-01 10.000 i ++ i 0.17475E-01 11.000 i +++ i -0.35159E-01 12.000 i +++++++ i -.11314 13.000 i ++++++++ i -.13858 14.000 i ++++++ i -.10145 15.000 i ++++++ i -.10083 16.000 i ++++++ i -.10666 17.000 i +++++++ i -.12337 18.000 i ++++ i -0.65475E-01 19.000 i ++ i 0.26352E-01 20.000 i +++ i 0.37046E-01 21.000 i ++ i 0.24165E-01 22.000 i ++ i -0.27097E-01 23.000 i ++ i 0.15920E-01 24.000 i ++++ i 0.58745E-01 25.000 i ++++ i 0.52299E-01 26.000 i +++ i -0.30013E-01 27.000 i +++ i -0.42630E-01 28.000 i ++++ i -0.61556E-01 29.000 i ++++ i -0.55425E-01 30.000 i ++ i -0.28172E-01 31.000 i ++++++ i -0.91703E-01 32.000 i +++++++++ i -.15668 33.000 i ++++++++++ i -.17497 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) and autoregressive order selection statistics lag 1 2 3 4 5 6 7 8 9 10 11 12 pacf 0.54 0.06 0.02 0.17 0.09 -0.11 -0.05 0.00 0.02 -0.06 -0.03 -0.08 aic 0.00 1.60 3.56 2.53 3.70 4.59 6.32 8.34 10.32 11.94 13.85 15.18 f ratio 40.28 0.39 0.05 2.92 0.80 1.04 0.26 0.00 0.03 0.36 0.10 0.62 f probability 0.00 0.54 0.83 0.09 0.37 0.31 0.61 0.99 0.87 0.55 0.75 0.43 lag 13 14 15 16 17 18 19 20 21 22 23 24 pacf -0.06 0.01 -0.02 -0.01 -0.01 0.07 0.10 0.00 0.01 -0.05 0.02 0.01 aic 16.81 18.83 20.83 22.88 24.94 26.56 27.63 29.72 31.80 33.65 35.72 37.83 f ratio 0.35 0.02 0.04 0.01 0.00 0.36 0.81 0.00 0.01 0.20 0.04 0.01 f probability 0.55 0.89 0.84 0.93 0.94 0.55 0.37 0.98 0.91 0.66 0.84 0.90 lag 25 26 27 28 29 30 31 32 33 pacf -0.03 -0.10 -0.00 -0.08 -0.05 0.04 -0.06 -0.10 -0.03 aic 39.87 41.10 43.26 44.82 46.78 48.80 50.70 52.03 54.21 f ratio 0.07 0.68 0.00 0.44 0.16 0.13 0.22 0.61 0.05 f probability 0.80 0.41 0.97 0.51 0.69 0.72 0.64 0.44 0.83 order autoregressive process selected = 1 one step prediction variance of process selected = 1.16885 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 coefficient value 0.5397 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++ i 0.63018E-01 3.0000 i ++ i 0.22136E-01 4.0000 i ++++++++++ i .17266 5.0000 i ++++++ i 0.91595E-01 6.0000 i ++++++ i -.10523 7.0000 i ++++ i -0.53166E-01 8.0000 i + i 0.93008E-03 9.0000 i ++ i 0.17874E-01 10.000 i ++++ i -0.63464E-01 11.000 i +++ i -0.33574E-01 12.000 i +++++ i -0.84130E-01 13.000 i ++++ i -0.63805E-01 14.000 i ++ i 0.14446E-01 15.000 i ++ i -0.21400E-01 16.000 i + i -0.92887E-02 17.000 i + i -0.76473E-02 18.000 i ++++ i 0.66474E-01 19.000 i ++++++ i .10021 20.000 i + i 0.30542E-02 21.000 i ++ i 0.13109E-01 22.000 i ++++ i -0.50444E-01 23.000 i ++ i 0.22769E-01 24.000 i ++ i 0.13963E-01 25.000 i +++ i -0.30205E-01 26.000 i ++++++ i -0.96142E-01 27.000 i + i -0.47261E-02 28.000 i +++++ i -0.78259E-01 29.000 i +++ i -0.48012E-01 30.000 i +++ i 0.42976E-01 31.000 i ++++ i -0.56478E-01 32.000 i ++++++ i -0.95175E-01 33.000 i ++ i -0.27238E-01 IERR = 0 Test of ACFS: starpac 2.08s (03/15/90) autocorrelation analysis average of the series = .1450000 standard deviation of the series = 1.277758 number of time points = 100 largest lag value used = 20 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.54 0.34 0.22 0.26 0.27 0.15 0.06 0.03 0.06 0.02 -0.04 -0.11 standard error 0.10 0.12 0.13 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 lag 13 14 15 16 17 18 19 20 acf -0.14 -0.10 -0.10 -0.11 -0.12 -0.07 0.03 0.04 standard error 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 the chi square test statistic of the null hypothesis of white noise = 71.37 degrees of freedom = 20 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++++++++++++++++ i .33597 3.0000 i ++++++++++++ i .21955 4.0000 i ++++++++++++++ i .26190 5.0000 i +++++++++++++++ i .27456 6.0000 i +++++++++ i .15137 7.0000 i ++++ i 0.56358E-01 8.0000 i +++ i 0.30065E-01 9.0000 i ++++ i 0.58569E-01 10.000 i ++ i 0.17475E-01 11.000 i +++ i -0.35159E-01 12.000 i +++++++ i -.11314 13.000 i ++++++++ i -.13858 14.000 i ++++++ i -.10145 15.000 i ++++++ i -.10083 16.000 i ++++++ i -.10666 17.000 i +++++++ i -.12337 18.000 i ++++ i -0.65475E-01 19.000 i ++ i 0.26352E-01 20.000 i +++ i 0.37046E-01 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) and autoregressive order selection statistics lag 1 2 3 4 5 6 7 8 9 10 11 12 pacf 0.54 0.06 0.02 0.17 0.09 -0.11 -0.05 0.00 0.02 -0.06 -0.03 -0.08 aic 0.00 1.60 3.56 2.53 3.70 4.59 6.32 8.34 10.32 11.94 13.85 15.18 f ratio 40.28 0.39 0.05 2.92 0.80 1.04 0.26 0.00 0.03 0.36 0.10 0.62 f probability 0.00 0.54 0.83 0.09 0.37 0.31 0.61 0.99 0.87 0.55 0.75 0.43 lag 13 14 15 16 17 18 19 20 pacf -0.06 0.01 -0.02 -0.01 -0.01 0.07 0.10 0.00 aic 16.81 18.83 20.83 22.88 24.94 26.56 27.63 29.72 f ratio 0.35 0.02 0.04 0.01 0.00 0.36 0.81 0.00 f probability 0.55 0.89 0.84 0.93 0.94 0.55 0.37 0.98 order autoregressive process selected = 1 one step prediction variance of process selected = 1.16885 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 coefficient value 0.5397 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++ i 0.63018E-01 3.0000 i ++ i 0.22136E-01 4.0000 i ++++++++++ i .17266 5.0000 i ++++++ i 0.91595E-01 6.0000 i ++++++ i -.10523 7.0000 i ++++ i -0.53166E-01 8.0000 i + i 0.93008E-03 9.0000 i ++ i 0.17874E-01 10.000 i ++++ i -0.63464E-01 11.000 i +++ i -0.33574E-01 12.000 i +++++ i -0.84130E-01 13.000 i ++++ i -0.63805E-01 14.000 i ++ i 0.14446E-01 15.000 i ++ i -0.21400E-01 16.000 i + i -0.92887E-02 17.000 i + i -0.76473E-02 18.000 i ++++ i 0.66474E-01 19.000 i ++++++ i .10021 20.000 i + i 0.30542E-02 IERR = 0 1.61634 0.87240 0.54305 0.35486 0.42332 0.44379 0.24466 0.09109 0.04859 0.09467 0.02825 -0.05683 -0.18286 -0.22400 -0.16397 -0.16298 -0.17239 -0.19941 -0.10583 0.04259 0.05988 0.53974 Test of ACFD: starpac 2.08s (03/15/90) autocorrelation analysis series analyzed is input series differenced by 1 factor(s) of order 1. average of the series = 5.222222 standard deviation of the series = 49.13239 number of time points = 143 largest lag value used = 20 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.19 -0.14 -0.17 -0.27 -0.07 0.07 0.03 -0.13 -0.04 -0.02 0.10 0.40 standard error 0.08 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 lag 13 14 15 16 17 18 19 20 acf 0.18 -0.11 -0.18 -0.23 -0.02 0.06 0.02 -0.11 standard error 0.10 0.10 0.10 0.11 0.11 0.11 0.11 0.11 the chi square test statistic of the null hypothesis of white noise = 72.43 degrees of freedom = 20 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) series analyzed is input series differenced by 1 factor(s) of order 1. -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i +++++++++++ i .19145 2.0000 i ++++++++ i -.13863 3.0000 i ++++++++++ i -.17466 4.0000 i ++++++++++++++ i -.26640 5.0000 i ++++ i -0.68404E-01 6.0000 i ++++ i 0.67063E-01 7.0000 i +++ i 0.31347E-01 8.0000 i +++++++ i -.12789 9.0000 i +++ i -0.41131E-01 10.000 i ++ i -0.17682E-01 11.000 i ++++++ i 0.97165E-01 12.000 i +++++++++++++++++++++ i .40017 13.000 i ++++++++++ i .18362 14.000 i ++++++ i -.10840 15.000 i ++++++++++ i -.17714 16.000 i +++++++++++++ i -.23177 17.000 i ++ i -0.20135E-01 18.000 i ++++ i 0.59169E-01 19.000 i ++ i 0.18533E-01 20.000 i +++++++ i -.11292 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) and autoregressive order selection statistics series analyzed is input series differenced by 1 factor(s) of order 1. lag 1 2 3 4 5 6 7 8 9 10 11 12 pacf 0.19 -0.18 -0.12 -0.25 -0.02 -0.02 -0.06 -0.22 -0.02 -0.08 0.06 0.32 aic 19.08 16.23 16.26 8.96 10.91 12.86 14.28 9.24 11.21 12.34 13.79 0.00 f ratio 5.40 4.83 1.92 9.28 0.05 0.05 0.56 6.77 0.04 0.81 0.52 15.20 f probability 0.02 0.03 0.17 0.00 0.82 0.83 0.46 0.01 0.85 0.37 0.47 0.00 lag 13 14 15 16 17 18 19 20 pacf 0.10 -0.02 0.00 -0.05 0.10 -0.05 -0.05 -0.13 aic 0.65 2.61 4.63 6.28 6.74 8.47 10.09 9.54 f ratio 1.24 0.06 0.00 0.34 1.38 0.26 0.36 2.23 f probability 0.27 0.81 0.99 0.56 0.24 0.61 0.55 0.14 order autoregressive process selected = 12 one step prediction variance of process selected = 1907.60 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 3 4 5 6 7 8 9 10 11 12 coefficient value 0.1354-0.1919-0.0841-0.2403-0.0465-0.0500-0.0008-0.1249 0.0352-0.0163 0.0124 0.3224 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) series analyzed is input series differenced by 1 factor(s) of order 1. -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i +++++++++++ i .19145 2.0000 i ++++++++++ i -.18195 3.0000 i +++++++ i -.11643 4.0000 i ++++++++++++++ i -.25017 5.0000 i ++ i -0.19312E-01 6.0000 i ++ i -0.18502E-01 7.0000 i ++++ i -0.63818E-01 8.0000 i ++++++++++++ i -.21860 9.0000 i ++ i -0.16539E-01 10.000 i +++++ i -0.77770E-01 11.000 i ++++ i 0.62566E-01 12.000 i +++++++++++++++++ i .32244 13.000 i ++++++ i 0.97190E-01 14.000 i ++ i -0.20728E-01 15.000 i + i 0.10793E-02 16.000 i ++++ i -0.51387E-01 17.000 i ++++++ i .10403 18.000 i +++ i -0.45989E-01 19.000 i ++++ i -0.53692E-01 20.000 i ++++++++ i -.13348 1 starpac 2.08s (03/15/90) autocorrelation analysis series analyzed is input series differenced by 1 factor(s) of order 1. and, in addition, differenced by 1 factors of order 12. average of the series = 5.958333 standard deviation of the series = 62.44414 number of time points = 143 largest lag value used = 20 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.30 -0.01 -0.00 -0.10 -0.09 -0.05 0.11 -0.07 -0.01 -0.04 -0.03 0.26 standard error 0.08 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 lag 13 14 15 16 17 18 19 20 acf -0.00 -0.00 0.01 -0.10 -0.08 -0.03 0.09 -0.05 standard error 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 the chi square test statistic of the null hypothesis of white noise = 33.04 degrees of freedom = 20 observed significance level = 0.0334 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) series analyzed is input series differenced by 1 factor(s) of order 1. and, in addition, differenced by 1 factors of order 12. -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++ i .29990 2.0000 i + i -0.85889E-02 3.0000 i + i -0.19820E-02 4.0000 i ++++++ i -.10046 5.0000 i +++++ i -0.86710E-01 6.0000 i ++++ i -0.52851E-01 7.0000 i +++++++ i .11195 8.0000 i +++++ i -0.73567E-01 9.0000 i + i -0.94019E-02 10.000 i +++ i -0.35997E-01 11.000 i +++ i -0.34328E-01 12.000 i ++++++++++++++ i .26274 13.000 i + i -0.18215E-02 14.000 i + i -0.40818E-02 15.000 i + i 0.76256E-02 16.000 i ++++++ i -.10344 17.000 i +++++ i -0.82699E-01 18.000 i ++ i -0.27091E-01 19.000 i ++++++ i 0.91057E-01 20.000 i ++++ i -0.53078E-01 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) and autoregressive order selection statistics series analyzed is input series differenced by 1 factor(s) of order 1. and, in addition, differenced by 1 factors of order 12. lag 1 2 3 4 5 6 7 8 9 10 11 12 pacf 0.30 -0.11 0.04 -0.12 -0.02 -0.04 0.15 -0.20 0.11 -0.14 0.08 0.26 aic 12.87 13.17 14.98 14.72 16.69 18.49 17.00 13.18 13.39 12.75 13.74 5.57 f ratio 14.03 1.67 0.19 2.19 0.04 0.19 3.34 5.58 1.68 2.47 0.94 9.60 f probability 0.00 0.20 0.66 0.14 0.85 0.66 0.07 0.02 0.20 0.12 0.33 0.00 lag 13 14 15 16 17 18 19 20 pacf -0.23 0.11 -0.04 -0.10 0.06 -0.02 -0.01 0.03 aic 0.00 0.33 2.13 2.57 4.08 6.07 8.08 10.00 f ratio 7.04 1.53 0.19 1.41 0.46 0.04 0.02 0.10 f probability 0.01 0.22 0.66 0.24 0.50 0.84 0.88 0.75 order autoregressive process selected = 13 one step prediction variance of process selected = 3019.85 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 3 4 5 6 7 8 9 10 11 12 coefficient value 0.4607-0.1889 0.0974-0.1026-0.0462-0.0584 0.2259-0.2228 0.1080-0.0827-0.0680 0.3523 coefficient number 13 coefficient value -0.2266 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) series analyzed is input series differenced by 1 factor(s) of order 1. and, in addition, differenced by 1 factors of order 12. -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++ i .29990 2.0000 i ++++++ i -.10827 3.0000 i +++ i 0.37071E-01 4.0000 i +++++++ i -.12464 5.0000 i ++ i -0.16582E-01 6.0000 i +++ i -0.37165E-01 7.0000 i +++++++++ i .15482 8.0000 i +++++++++++ i -.19926 9.0000 i +++++++ i .11125 10.000 i ++++++++ i -.13510 11.000 i +++++ i 0.84087E-01 12.000 i ++++++++++++++ i .26129 13.000 i ++++++++++++ i -.22660 14.000 i ++++++ i .10815 15.000 i +++ i -0.38897E-01 16.000 i ++++++ i -.10466 17.000 i ++++ i 0.60134E-01 18.000 i ++ i -0.17982E-01 19.000 i ++ i -0.13653E-01 20.000 i ++ i 0.28568E-01 IERR = 0 Test of ACFM: LACOV = 101 LAGMAX = 33 N = 100 starpac 2.08s (03/15/90) autocorrelation analysis average of the series = .1136082 standard deviation of the series = 1.284516 number of time points = 100 number of missing observations = 3 percentage of observations missing = 3.0000 largest lag value used = 33 missing value code = 1.160000 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.54 0.34 0.23 0.27 0.29 0.17 0.05 0.02 0.04 0.02 -0.06 -0.14 standard error 0.10 0.13 0.14 0.14 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 no. of obs. used 97 93 92 91 90 89 88 88 86 85 85 83 lag 13 14 15 16 17 18 19 20 21 22 23 24 acf -0.17 -0.11 -0.08 -0.11 -0.15 -0.13 -0.01 0.02 -0.00 -0.06 0.00 0.02 standard error 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 no. of obs. used 82 81 80 79 78 78 76 75 74 73 72 71 lag 25 26 27 28 29 30 31 32 33 acf 0.05 -0.03 -0.04 -0.08 -0.06 -0.01 -0.08 -0.17 -0.17 standard error 0.15 0.15 0.15 0.15 0.15 0.14 0.14 0.14 0.14 no. of obs. used 70 69 68 67 66 65 64 63 62 the chi square test statistic of the null hypothesis of white noise = 83.62 degrees of freedom = 33 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .54111 2.0000 i ++++++++++++++++++ i .34454 3.0000 i ++++++++++++ i .22870 4.0000 i +++++++++++++++ i .27427 5.0000 i ++++++++++++++++ i .29121 6.0000 i +++++++++ i .16956 7.0000 i +++ i 0.47261E-01 8.0000 i ++ i 0.21651E-01 9.0000 i +++ i 0.35083E-01 10.000 i ++ i 0.24208E-01 11.000 i ++++ i -0.55412E-01 12.000 i ++++++++ i -.13641 13.000 i ++++++++++ i -.17477 14.000 i ++++++ i -.10690 15.000 i +++++ i -0.81923E-01 16.000 i ++++++ i -.10916 17.000 i +++++++++ i -.15229 18.000 i +++++++ i -.12850 19.000 i + i -0.84649E-02 20.000 i ++ i 0.18726E-01 21.000 i + i -0.61991E-04 22.000 i ++++ i -0.60259E-01 23.000 i + i 0.38860E-02 24.000 i ++ i 0.20153E-01 25.000 i +++ i 0.45633E-01 26.000 i ++ i -0.29248E-01 27.000 i +++ i -0.35953E-01 28.000 i +++++ i -0.83693E-01 29.000 i ++++ i -0.58049E-01 30.000 i ++ i -0.11752E-01 31.000 i +++++ i -0.75615E-01 32.000 i +++++++++ i -.16783 33.000 i ++++++++++ i -.17273 IERR = 0 Test of ACFMS: LACOV = 21 LAGMAX = 20 N = 100 starpac 2.08s (03/15/90) autocorrelation analysis average of the series = .1136082 standard deviation of the series = 1.284516 number of time points = 100 number of missing observations = 3 percentage of observations missing = 3.0000 largest lag value used = 20 missing value code = 1.160000 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.54 0.34 0.23 0.27 0.29 0.17 0.05 0.02 0.04 0.02 -0.06 -0.14 standard error 0.10 0.13 0.14 0.14 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 no. of obs. used 97 93 92 91 90 89 88 88 86 85 85 83 lag 13 14 15 16 17 18 19 20 acf -0.17 -0.11 -0.08 -0.11 -0.15 -0.13 -0.01 0.02 standard error 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 no. of obs. used 82 81 80 79 78 78 76 75 the chi square test statistic of the null hypothesis of white noise = 75.61 degrees of freedom = 20 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .54111 2.0000 i ++++++++++++++++++ i .34454 3.0000 i ++++++++++++ i .22870 4.0000 i +++++++++++++++ i .27427 5.0000 i ++++++++++++++++ i .29121 6.0000 i +++++++++ i .16956 7.0000 i +++ i 0.47261E-01 8.0000 i ++ i 0.21651E-01 9.0000 i +++ i 0.35083E-01 10.000 i ++ i 0.24208E-01 11.000 i ++++ i -0.55412E-01 12.000 i ++++++++ i -.13641 13.000 i ++++++++++ i -.17477 14.000 i ++++++ i -.10690 15.000 i +++++ i -0.81923E-01 16.000 i ++++++ i -.10916 17.000 i +++++++++ i -.15229 18.000 i +++++++ i -.12850 19.000 i + i -0.84649E-02 20.000 i ++ i 0.18726E-01 IERR = 0 1.63348 0.88389 0.56279 0.37357 0.44802 0.47568 0.27697 0.07720 0.03537 0.05731 0.03954 -0.09051 -0.22283 -0.28549 -0.17462 -0.13382 -0.17831 -0.24876 -0.20990 -0.01383 0.03059 97 93 92 91 90 89 88 88 86 85 85 83 82 81 80 79 78 78 76 75 74 Test of ACFF: starpac 2.08s (03/15/90) autocorrelation analysis average of the series = .1450000 standard deviation of the series = 1.277758 number of time points = 100 largest lag value used = 33 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.54 0.34 0.22 0.26 0.27 0.15 0.06 0.03 0.06 0.02 -0.04 -0.11 standard error 0.10 0.12 0.13 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 lag 13 14 15 16 17 18 19 20 21 22 23 24 acf -0.14 -0.10 -0.10 -0.11 -0.12 -0.07 0.03 0.04 0.02 -0.03 0.02 0.06 standard error 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 lag 25 26 27 28 29 30 31 32 33 acf 0.05 -0.03 -0.04 -0.06 -0.06 -0.03 -0.09 -0.16 -0.17 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 the chi square test statistic of the null hypothesis of white noise = 79.54 degrees of freedom = 33 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++++++++++++++++ i .33597 3.0000 i ++++++++++++ i .21955 4.0000 i ++++++++++++++ i .26190 5.0000 i +++++++++++++++ i .27456 6.0000 i +++++++++ i .15137 7.0000 i ++++ i 0.56358E-01 8.0000 i +++ i 0.30065E-01 9.0000 i ++++ i 0.58569E-01 10.000 i ++ i 0.17475E-01 11.000 i +++ i -0.35159E-01 12.000 i +++++++ i -.11314 13.000 i ++++++++ i -.13858 14.000 i ++++++ i -.10145 15.000 i ++++++ i -.10083 16.000 i ++++++ i -.10666 17.000 i +++++++ i -.12337 18.000 i ++++ i -0.65475E-01 19.000 i ++ i 0.26352E-01 20.000 i +++ i 0.37046E-01 21.000 i ++ i 0.24165E-01 22.000 i ++ i -0.27097E-01 23.000 i ++ i 0.15920E-01 24.000 i ++++ i 0.58745E-01 25.000 i ++++ i 0.52299E-01 26.000 i +++ i -0.30013E-01 27.000 i +++ i -0.42630E-01 28.000 i ++++ i -0.61556E-01 29.000 i ++++ i -0.55425E-01 30.000 i ++ i -0.28172E-01 31.000 i ++++++ i -0.91703E-01 32.000 i +++++++++ i -.15668 33.000 i ++++++++++ i -.17497 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) and autoregressive order selection statistics lag 1 2 3 4 5 6 7 8 9 10 11 12 pacf 0.54 0.06 0.02 0.17 0.09 -0.11 -0.05 0.00 0.02 -0.06 -0.03 -0.08 aic 0.00 1.60 3.56 2.53 3.70 4.59 6.32 8.34 10.32 11.94 13.85 15.18 f ratio 40.28 0.39 0.05 2.92 0.80 1.04 0.26 0.00 0.03 0.36 0.10 0.62 f probability 0.00 0.54 0.83 0.09 0.37 0.31 0.61 0.99 0.87 0.55 0.75 0.43 lag 13 14 15 16 17 18 19 20 21 22 23 24 pacf -0.06 0.01 -0.02 -0.01 -0.01 0.07 0.10 0.00 0.01 -0.05 0.02 0.01 aic 16.81 18.83 20.83 22.88 24.94 26.56 27.63 29.72 31.80 33.65 35.72 37.83 f ratio 0.35 0.02 0.04 0.01 0.00 0.36 0.81 0.00 0.01 0.20 0.04 0.01 f probability 0.55 0.89 0.84 0.93 0.94 0.55 0.37 0.98 0.91 0.66 0.84 0.90 lag 25 26 27 28 29 30 31 32 33 pacf -0.03 -0.10 -0.00 -0.08 -0.05 0.04 -0.06 -0.10 -0.03 aic 39.87 41.10 43.26 44.82 46.78 48.80 50.70 52.03 54.21 f ratio 0.07 0.68 0.00 0.44 0.16 0.13 0.22 0.61 0.05 f probability 0.80 0.41 0.97 0.51 0.69 0.72 0.64 0.44 0.83 order autoregressive process selected = 1 one step prediction variance of process selected = 1.16885 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 coefficient value 0.5397 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++ i 0.63018E-01 3.0000 i ++ i 0.22135E-01 4.0000 i ++++++++++ i .17266 5.0000 i ++++++ i 0.91596E-01 6.0000 i ++++++ i -.10523 7.0000 i ++++ i -0.53166E-01 8.0000 i + i 0.92993E-03 9.0000 i ++ i 0.17874E-01 10.000 i ++++ i -0.63464E-01 11.000 i +++ i -0.33574E-01 12.000 i +++++ i -0.84130E-01 13.000 i ++++ i -0.63805E-01 14.000 i ++ i 0.14446E-01 15.000 i ++ i -0.21400E-01 16.000 i + i -0.92888E-02 17.000 i + i -0.76473E-02 18.000 i ++++ i 0.66474E-01 19.000 i ++++++ i .10021 20.000 i + i 0.30542E-02 21.000 i ++ i 0.13109E-01 22.000 i ++++ i -0.50444E-01 23.000 i ++ i 0.22769E-01 24.000 i ++ i 0.13963E-01 25.000 i +++ i -0.30205E-01 26.000 i ++++++ i -0.96142E-01 27.000 i + i -0.47262E-02 28.000 i +++++ i -0.78259E-01 29.000 i +++ i -0.48012E-01 30.000 i +++ i 0.42976E-01 31.000 i ++++ i -0.56478E-01 32.000 i ++++++ i -0.95175E-01 33.000 i ++ i -0.27239E-01 IERR = 0 Test of ACFFS: starpac 2.08s (03/15/90) autocorrelation analysis average of the series = .1450000 standard deviation of the series = 1.277758 number of time points = 100 largest lag value used = 20 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.54 0.34 0.22 0.26 0.27 0.15 0.06 0.03 0.06 0.02 -0.04 -0.11 standard error 0.10 0.12 0.13 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 lag 13 14 15 16 17 18 19 20 acf -0.14 -0.10 -0.10 -0.11 -0.12 -0.07 0.03 0.04 standard error 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 the chi square test statistic of the null hypothesis of white noise = 71.37 degrees of freedom = 20 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++++++++++++++++ i .33597 3.0000 i ++++++++++++ i .21955 4.0000 i ++++++++++++++ i .26190 5.0000 i +++++++++++++++ i .27456 6.0000 i +++++++++ i .15137 7.0000 i ++++ i 0.56358E-01 8.0000 i +++ i 0.30065E-01 9.0000 i ++++ i 0.58569E-01 10.000 i ++ i 0.17475E-01 11.000 i +++ i -0.35159E-01 12.000 i +++++++ i -.11314 13.000 i ++++++++ i -.13858 14.000 i ++++++ i -.10145 15.000 i ++++++ i -.10083 16.000 i ++++++ i -.10666 17.000 i +++++++ i -.12337 18.000 i ++++ i -0.65475E-01 19.000 i ++ i 0.26352E-01 20.000 i +++ i 0.37046E-01 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) and autoregressive order selection statistics lag 1 2 3 4 5 6 7 8 9 10 11 12 pacf 0.54 0.06 0.02 0.17 0.09 -0.11 -0.05 0.00 0.02 -0.06 -0.03 -0.08 aic 0.00 1.60 3.56 2.53 3.70 4.59 6.32 8.34 10.32 11.94 13.85 15.18 f ratio 40.28 0.39 0.05 2.92 0.80 1.04 0.26 0.00 0.03 0.36 0.10 0.62 f probability 0.00 0.54 0.83 0.09 0.37 0.31 0.61 0.99 0.87 0.55 0.75 0.43 lag 13 14 15 16 17 18 19 20 pacf -0.06 0.01 -0.02 -0.01 -0.01 0.07 0.10 0.00 aic 16.81 18.83 20.83 22.88 24.94 26.56 27.63 29.72 f ratio 0.35 0.02 0.04 0.01 0.00 0.36 0.81 0.00 f probability 0.55 0.89 0.84 0.93 0.94 0.55 0.37 0.98 order autoregressive process selected = 1 one step prediction variance of process selected = 1.16885 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 coefficient value 0.5397 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++ i 0.63018E-01 3.0000 i ++ i 0.22135E-01 4.0000 i ++++++++++ i .17266 5.0000 i ++++++ i 0.91596E-01 6.0000 i ++++++ i -.10523 7.0000 i ++++ i -0.53166E-01 8.0000 i + i 0.93006E-03 9.0000 i ++ i 0.17874E-01 10.000 i ++++ i -0.63464E-01 11.000 i +++ i -0.33574E-01 12.000 i +++++ i -0.84131E-01 13.000 i ++++ i -0.63805E-01 14.000 i ++ i 0.14446E-01 15.000 i ++ i -0.21400E-01 16.000 i + i -0.92888E-02 17.000 i + i -0.76473E-02 18.000 i ++++ i 0.66474E-01 19.000 i ++++++ i .10021 20.000 i + i 0.30543E-02 IERR = 0 1.61634 0.87240 0.54305 0.35486 0.42332 0.44379 0.24466 0.09109 0.04859 0.09467 0.02825 -0.05683 -0.18287 -0.22400 -0.16397 -0.16298 -0.17239 -0.19941 -0.10583 0.04259 0.05988 0.53974 XAIMD Demonstrate codes in the ARIMA family. ARIMA test number 1 Normal problem Test of AIM starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- no 0.00000000E+00 + default 0.99999997E-04 2 ma (factor 1) 1 no 0.40000001E+00 + default 0.99986792E-04 3 ma (factor 2) 12 no 0.60000002E+00 + default 0.10003646E-03 number of observations (n) 144 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values .1762 (backforecasts included) residual standard deviation for input parameter values (rsd) 0.3710E-01 based on degrees of freedom 144 - 13 - 3 = 128 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3709E-01 .1761 0.5188E-03 0.4752E-03 y 0.1203E-01 y current parameter values index 1 2 3 value -0.1331977E-03 .3981068 .6146109 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3709E-01 .1761 0.8206E-05 0.6854E-05 y 0.1304E-02 y current parameter values index 1 2 3 value -0.1394698E-03 .3962470 .6158933 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.47186418E+01 0.23331079E-02 -0.14305115E-03 -0.00 2 0.47706847E+01 0.47649231E+01 0.61510731E-02 0.57616234E-02 0.16 3 0.48828020E+01 0.48947701E+01 0.20620674E-02 -0.11968136E-01 -0.32 4 0.48598123E+01 0.48486533E+01 0.24097061E-02 0.11158943E-01 0.30 5 0.47957907E+01 0.48221750E+01 0.78258952E-02 -0.26384354E-01 -0.73 6 0.49052749E+01 0.49267020E+01 0.44785561E-02 -0.21427155E-01 -0.58 7 0.49972124E+01 0.50175648E+01 0.39292825E-02 -0.20352364E-01 -0.55 8 0.49972124E+01 0.50095334E+01 0.36501882E-02 -0.12320995E-01 -0.33 9 0.49126549E+01 0.49085374E+01 0.53531774E-02 0.41174889E-02 0.11 10 0.47791233E+01 0.47746725E+01 0.23936119E-02 0.44507980E-02 0.12 11 0.46443911E+01 0.46444077E+01 0.24618390E-02 -0.16689301E-04 -0.00 12 0.47706847E+01 0.47883644E+01 0.27930622E-02 -0.17679691E-01 -0.48 13 0.47449322E+01 0.47855887E+01 0.39746142E-02 -0.40656567E-01 -1.10 14 0.48362818E+01 0.48095055E+01 0.62055932E-02 0.26776314E-01 0.73 15 0.49487600E+01 0.49464269E+01 0.14908055E-02 0.23331642E-02 0.06 16 0.49052749E+01 0.49149132E+01 0.50641416E-03 -0.96383095E-02 -0.26 17 0.48283138E+01 0.48639064E+01 0.64150966E-02 -0.35592556E-01 -0.97 18 0.50039463E+01 0.49585199E+01 0.46543465E-02 0.45426369E-01 1.23 19 0.51357985E+01 0.50850506E+01 0.37458162E-02 0.50747871E-01 1.38 20 0.51357985E+01 0.51181722E+01 0.54075615E-02 0.17626286E-01 0.48 21 0.50625949E+01 0.50385742E+01 0.43933392E-02 0.24020672E-01 0.65 22 0.48903489E+01 0.49176698E+01 0.33263650E-02 -0.27320862E-01 -0.74 23 0.47361984E+01 0.47673998E+01 0.99117274E-03 -0.31201363E-01 -0.84 24 0.49416423E+01 0.48856010E+01 0.29015543E-02 0.56041241E-01 1.52 25 0.49767337E+01 0.49142694E+01 0.47820876E-02 0.62464237E-01 1.70 26 0.50106354E+01 0.50167794E+01 0.75511970E-02 -0.61440468E-02 -0.17 27 0.51817837E+01 0.51305065E+01 0.24202638E-02 0.51277161E-01 1.39 28 0.50937500E+01 0.51243463E+01 0.52093323E-02 -0.30596256E-01 -0.83 29 0.51474943E+01 0.50483427E+01 0.65928125E-02 0.99151611E-01 2.72 30 0.51817837E+01 0.52470350E+01 0.80235470E-02 -0.65251350E-01 -1.80 31 0.52933049E+01 0.53191829E+01 0.33242132E-02 -0.25877953E-01 -0.70 32 0.52933049E+01 0.53049488E+01 0.33504774E-02 -0.11643887E-01 -0.32 33 0.52149358E+01 0.52140837E+01 0.44298610E-02 0.85210800E-03 0.02 34 0.50875964E+01 0.50649014E+01 0.24115308E-02 0.22695065E-01 0.61 35 0.49836068E+01 0.49368629E+01 0.33301080E-02 0.46743870E-01 1.27 36 0.51119876E+01 0.51282592E+01 0.58332616E-02 -0.16271591E-01 -0.44 37 0.51416636E+01 0.51285930E+01 0.24948644E-02 0.13070583E-01 0.35 38 0.51929569E+01 0.51892748E+01 0.31649566E-02 0.36821365E-02 0.10 39 0.52626901E+01 0.53294263E+01 0.39239945E-02 -0.66736221E-01 -1.81 40 0.51984968E+01 0.52323194E+01 0.60198181E-02 -0.33822536E-01 -0.92 . . . . . . . . . . . . . . . . . . 144 0.60684257E+01 0.60533385E+01 0.64083585E-02 0.15087128E-01 0.41 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - * - 2.25+ * + - * * - - * * - - * * * * * ** - - * * * * * * * * - 0.75+ * * * * ** * * * + - * * * * * * ** * * * * - - ** * * * * * * * * * * *- -* *** * * * * * * * * ** * * * * * * - - * * * * * * ** * ** * * * * * * * * * * - -0.75+ *** * * * * * * * * * * * * * * + - * * * * ** * * ** * * * - - * * * * ** * - - * - - * * * - -2.25+ * + - * - - - - - - * - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 72.5 144.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - *** - - - - **** - - - - * - - *- 6+ ** + 2.25+ * + - * - - ** - - *** - - ** - - *** - - **** - - * - - **** - 11+ * + 0.75+ **** + - * - - *** - - ** - - *** - - * - - *** - - * - - **** - 16+ **** + -0.75+ ***** + - ** - - ***** - - * - - ***** - - * - - * - - *** - - *** - 21+ * + -2.25+ * + - * - - * - - **** - - - - * - - - - * - -* - 26++---------+---------+----*----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 1 0.6233660E-06 -0.3081957E-06 -0.1849463E-06 2 -0.4788138E-02 0.6646250E-02 -0.3214141E-03 3 -0.3342787E-02 -0.5626148E-01 0.4910559E-02 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- no -0.13946980E-03 + 0.78953529E-03 -0.17664796E+00 -0.14467288E-02 0.11677893E-02 2 ma (factor 1) 1 no 0.39624697E+00 + 0.81524536E-01 0.48604627E+01 0.26126415E+00 0.53122979E+00 3 ma (factor 2) 12 no 0.61589330E+00 + 0.70075378E-01 0.87890120E+01 0.49986723E+00 0.73191935E+00 number of observations (n) 144 residual sum of squares .1761310 (backforecasts included) residual standard deviation 0.3709479E-01 based on degrees of freedom 144 - 13 - 3 = 128 approximate condition number 103.2582 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1-0.1394698E-03 -0.1430511E-03 -1.000000 -1.000000 -1.000000 2 .3962470 0.5761623E-02 -1.000000 -1.000000 -1.000000 3 .6158933 -0.1196814E-01 -1.000000 -1.000000 -1.000000 4 0.1115894E-01 -1.000000 -1.000000 -1.000000 5 -0.2638435E-01 -1.000000 -1.000000 -1.000000 6 -0.2142715E-01 -1.000000 -1.000000 -1.000000 7 -0.2035236E-01 -1.000000 -1.000000 -1.000000 8 -0.1232100E-01 -1.000000 -1.000000 -1.000000 9 0.4117489E-02 -1.000000 -1.000000 -1.000000 10 0.4450798E-02 -1.000000 -1.000000 -1.000000 11 -0.1668930E-04 -1.000000 -1.000000 -1.000000 12 -0.1767969E-01 -1.000000 -1.000000 -1.000000 13 -0.4065657E-01 -1.000000 -1.000000 -1.000000 14 0.2677631E-01 -1.000000 -1.000000 -1.000000 15 0.2333164E-02 -1.000000 -1.000000 -1.000000 16 -0.9638309E-02 -1.000000 -1.000000 -1.000000 17 -0.3559256E-01 -1.000000 -1.000000 -1.000000 18 0.4542637E-01 -1.000000 -1.000000 -1.000000 19 0.5074787E-01 -1.000000 -1.000000 -1.000000 20 0.1762629E-01 -1.000000 -1.000000 -1.000000 21 0.2402067E-01 -1.000000 -1.000000 -1.000000 22 -0.2732086E-01 -1.000000 -1.000000 -1.000000 23 -0.3120136E-01 -1.000000 -1.000000 -1.000000 24 0.5604124E-01 -1.000000 -1.000000 -1.000000 25 0.6246424E-01 -1.000000 -1.000000 -1.000000 26 -0.6144047E-02 -1.000000 -1.000000 -1.000000 27 0.5127716E-01 -1.000000 -1.000000 -1.000000 28 -0.3059626E-01 -1.000000 -1.000000 -1.000000 29 0.9915161E-01 -1.000000 -1.000000 -1.000000 30 -0.6525135E-01 -1.000000 -1.000000 -1.000000 31 -0.2587795E-01 -1.000000 -1.000000 -1.000000 32 -0.1164389E-01 -1.000000 -1.000000 -1.000000 33 0.8521080E-03 -1.000000 -1.000000 -1.000000 34 0.2269506E-01 -1.000000 -1.000000 -1.000000 35 0.4674387E-01 -1.000000 -1.000000 -1.000000 36 -0.1627159E-01 -1.000000 -1.000000 -1.000000 37 0.1307058E-01 -1.000000 -1.000000 -1.000000 38 0.3682137E-02 -1.000000 -1.000000 -1.000000 39 -0.6673622E-01 -1.000000 -1.000000 -1.000000 40 -0.3382254E-01 -1.000000 -1.000000 -1.000000 41 0.1251602E-01 -1.000000 -1.000000 -1.000000 42 0.8143330E-01 -1.000000 -1.000000 -1.000000 43 -0.2554417E-01 -1.000000 -1.000000 -1.000000 44 0.4001999E-01 -1.000000 -1.000000 -1.000000 45 -0.4887104E-01 -1.000000 -1.000000 -1.000000 46 0.3182268E-01 -1.000000 -1.000000 -1.000000 47 0.3520966E-01 -1.000000 -1.000000 -1.000000 48 -0.1535559E-01 -1.000000 -1.000000 -1.000000 49 -0.1334333E-01 -1.000000 -1.000000 -1.000000 50 -0.5736351E-01 -1.000000 -1.000000 -1.000000 51 0.5139208E-01 -1.000000 -1.000000 -1.000000 52 0.7590580E-01 -1.000000 -1.000000 -1.000000 53 0.9326458E-02 -1.000000 -1.000000 -1.000000 54 -0.6473541E-01 -1.000000 -1.000000 -1.000000 55 -0.3181410E-01 -1.000000 -1.000000 -1.000000 56 -0.2590656E-02 -1.000000 -1.000000 -1.000000 57 -0.3189182E-01 -1.000000 -1.000000 -1.000000 58 -0.7112980E-02 -1.000000 -1.000000 -1.000000 59 -0.4288197E-01 -1.000000 -1.000000 -1.000000 60 -0.4491854E-01 -1.000000 -1.000000 -1.000000 61 -0.1757145E-01 -1.000000 -1.000000 -1.000000 62 -.1205750 -1.000000 -1.000000 -1.000000 63 0.3543949E-01 -1.000000 -1.000000 -1.000000 64 0.1800060E-01 -1.000000 -1.000000 -1.000000 65 0.5072641E-01 -1.000000 -1.000000 -1.000000 66 0.3938150E-01 -1.000000 -1.000000 -1.000000 67 0.6353807E-01 -1.000000 -1.000000 -1.000000 68 -0.2862263E-01 -1.000000 -1.000000 -1.000000 69 -0.1581430E-01 -1.000000 -1.000000 -1.000000 70 -0.9629250E-02 -1.000000 -1.000000 -1.000000 71 0.1003456E-01 -1.000000 -1.000000 -1.000000 72 -0.2916336E-02 -1.000000 -1.000000 -1.000000 73 0.3952408E-01 -1.000000 -1.000000 -1.000000 74 -0.1039410E-01 -1.000000 -1.000000 -1.000000 75 -0.3965950E-01 -1.000000 -1.000000 -1.000000 76 0.2895975E-01 -1.000000 -1.000000 -1.000000 77 0.1180315E-01 -1.000000 -1.000000 -1.000000 78 0.5021429E-01 -1.000000 -1.000000 -1.000000 79 0.5966187E-01 -1.000000 -1.000000 -1.000000 80 -0.2692938E-01 -1.000000 -1.000000 -1.000000 81 0.3736973E-02 -1.000000 -1.000000 -1.000000 82 -0.7220268E-02 -1.000000 -1.000000 -1.000000 83 -0.1874399E-01 -1.000000 -1.000000 -1.000000 84 0.2751207E-01 -1.000000 -1.000000 -1.000000 85 0.2232552E-02 -1.000000 -1.000000 -1.000000 86 -0.2080441E-02 -1.000000 -1.000000 -1.000000 87 -0.2390146E-01 -1.000000 -1.000000 -1.000000 88 -0.1977444E-02 -1.000000 -1.000000 -1.000000 89 0.1169491E-01 -1.000000 -1.000000 -1.000000 90 0.4087305E-01 -1.000000 -1.000000 -1.000000 91 -0.4563808E-02 -1.000000 -1.000000 -1.000000 92 -0.4546165E-02 -1.000000 -1.000000 -1.000000 93 -0.1823664E-01 -1.000000 -1.000000 -1.000000 94 -0.3110361E-01 -1.000000 -1.000000 -1.000000 95 0.1634121E-02 -1.000000 -1.000000 -1.000000 96 -0.1578903E-01 -1.000000 -1.000000 -1.000000 97 -0.3822327E-02 -1.000000 -1.000000 -1.000000 98 -0.2370691E-01 -1.000000 -1.000000 -1.000000 99 0.9469032E-02 -1.000000 -1.000000 -1.000000 100 -0.1523495E-02 -1.000000 -1.000000 -1.000000 101 0.1128817E-01 -1.000000 -1.000000 -1.000000 102 0.3761482E-01 -1.000000 -1.000000 -1.000000 103 0.9870529E-04 -1.000000 -1.000000 -1.000000 104 0.2234459E-01 -1.000000 -1.000000 -1.000000 105 -0.1427460E-01 -1.000000 -1.000000 -1.000000 106 -0.2377987E-01 -1.000000 -1.000000 -1.000000 107 -0.8233070E-02 -1.000000 -1.000000 -1.000000 108 -0.3791332E-01 -1.000000 -1.000000 -1.000000 109 -0.3053713E-01 -1.000000 -1.000000 -1.000000 110 -0.4706144E-01 -1.000000 -1.000000 -1.000000 111 -0.4511929E-01 -1.000000 -1.000000 -1.000000 112 -0.3770161E-01 -1.000000 -1.000000 -1.000000 113 0.1480961E-01 -1.000000 -1.000000 -1.000000 114 0.3447485E-01 -1.000000 -1.000000 -1.000000 115 0.2874660E-01 -1.000000 -1.000000 -1.000000 116 0.4908991E-01 -1.000000 -1.000000 -1.000000 117 -0.7288265E-01 -1.000000 -1.000000 -1.000000 118 -0.5905151E-02 -1.000000 -1.000000 -1.000000 119 -0.1920509E-01 -1.000000 -1.000000 -1.000000 120 -0.4210043E-01 -1.000000 -1.000000 -1.000000 121 0.2808809E-01 -1.000000 -1.000000 -1.000000 122 0.5337715E-02 -1.000000 -1.000000 -1.000000 123 0.2790117E-01 -1.000000 -1.000000 -1.000000 124 0.1348829E-01 -1.000000 -1.000000 -1.000000 125 0.4044580E-01 -1.000000 -1.000000 -1.000000 126 -0.3043413E-01 -1.000000 -1.000000 -1.000000 127 0.2556944E-01 -1.000000 -1.000000 -1.000000 128 0.2524948E-01 -1.000000 -1.000000 -1.000000 129 -0.1200247E-01 -1.000000 -1.000000 -1.000000 130 -0.1288414E-02 -1.000000 -1.000000 -1.000000 131 0.1882219E-01 -1.000000 -1.000000 -1.000000 132 -0.1775408E-01 -1.000000 -1.000000 -1.000000 133 -0.9339380E-01 -1.000000 -1.000000 -1.000000 134 0.8509684E-01 -1.000000 -1.000000 -1.000000 135 0.2021790E-01 -1.000000 -1.000000 -1.000000 136 -0.1190138E-01 -1.000000 -1.000000 -1.000000 137 0.1997614E-01 -1.000000 -1.000000 -1.000000 138 -0.2856874E-01 -1.000000 -1.000000 -1.000000 139 -0.1271152E-01 -1.000000 -1.000000 -1.000000 140 0.2906847E-01 -1.000000 -1.000000 -1.000000 141 -0.2651739E-01 -1.000000 -1.000000 -1.000000 142 -0.1563406E-01 -1.000000 -1.000000 -1.000000 143 0.1882219E-01 -1.000000 -1.000000 -1.000000 144 0.1508713E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 7 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 column 8 9 10 1 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = 144 npare = -1 ARIMA test number 2 Normal problem Test of AIMC input - ifixed(1) = 1 , stp(1) = 0.34526698E-03, mit = 25, stopss = 0.10000000E-05, stopp = 0.10000000E-05 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- yes 0.00000000E+00 + --- --- 2 ma (factor 1) 1 no 0.40000001E+00 + 0.10000000E+01 0.34526698E-03 3 ma (factor 2) 12 no 0.60000002E+00 + 0.10000000E+01 0.34526698E-03 number of observations (n) 144 maximum number of iterations allowed (mit) 25 maximum number of model subroutine calls allowed 50 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-05 maximum scaled relative change in the parameters (stopp) 0.1000E-05 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values .1762 (backforecasts included) residual standard deviation for input parameter values (rsd) 0.3696E-01 based on degrees of freedom 144 - 13 - 2 = 129 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3695E-01 .1762 0.3316E-03 0.3079E-03 y 0.1179E-01 y current parameter values (only unfixed parameters are listed) index 2 3 value .3977237 .6143165 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 9 0.3695E-01 .1762 0.2538E-06 0.5078E-06 y 0.5723E-03 y current parameter values (only unfixed parameters are listed) index 2 3 value .3951464 .6154956 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.47187839E+01 0.26929260E-02 -0.28514862E-03 -0.01 2 0.47706847E+01 0.47647963E+01 0.69545731E-02 0.58884621E-02 0.16 3 0.48828020E+01 0.48944864E+01 0.12293251E-02 -0.11684418E-01 -0.32 4 0.48598123E+01 0.48483033E+01 0.10941615E-02 0.11508942E-01 0.31 5 0.47957907E+01 0.48217874E+01 0.87206867E-02 -0.25996685E-01 -0.72 6 0.49052749E+01 0.49262953E+01 0.38670984E-02 -0.21020412E-01 -0.57 7 0.49972124E+01 0.50171547E+01 0.33660352E-02 -0.19942284E-01 -0.54 8 0.49972124E+01 0.50091262E+01 0.33150972E-02 -0.11913776E-01 -0.32 9 0.49126549E+01 0.49081674E+01 0.63475915E-02 0.44875145E-02 0.12 10 0.47791233E+01 0.47742982E+01 0.54812367E-03 0.48251152E-02 0.13 11 0.46443911E+01 0.46440396E+01 0.87037368E-03 0.35142899E-03 0.01 12 0.47706847E+01 0.47880073E+01 0.18462659E-02 -0.17322540E-01 -0.47 13 0.47449322E+01 0.47850428E+01 0.25026991E-02 -0.40110588E-01 -1.09 14 0.48362818E+01 0.48092742E+01 0.83839493E-02 0.27007580E-01 0.75 15 0.49487600E+01 0.49463511E+01 0.17768922E-02 0.24089813E-02 0.07 16 0.49052749E+01 0.49148927E+01 0.60561643E-03 -0.96178055E-02 -0.26 17 0.48283138E+01 0.48638535E+01 0.70641842E-02 -0.35539627E-01 -0.98 18 0.50039463E+01 0.49584565E+01 0.48680520E-02 0.45489788E-01 1.24 19 0.51357985E+01 0.50850706E+01 0.50811935E-02 0.50727844E-01 1.39 20 0.51357985E+01 0.51182361E+01 0.75049233E-02 0.17562389E-01 0.49 21 0.50625949E+01 0.50386415E+01 0.48592519E-02 0.23953438E-01 0.65 22 0.48903489E+01 0.49177198E+01 0.43599787E-02 -0.27370930E-01 -0.75 23 0.47361984E+01 0.47673893E+01 0.11754329E-02 -0.31190872E-01 -0.84 24 0.49416423E+01 0.48855648E+01 0.38332716E-02 0.56077480E-01 1.53 25 0.49767337E+01 0.49142056E+01 0.64642024E-02 0.62528133E-01 1.72 26 0.50106354E+01 0.50169969E+01 0.84900754E-02 -0.63614845E-02 -0.18 27 0.51817837E+01 0.51307135E+01 0.29082661E-02 0.51070213E-01 1.39 28 0.50937500E+01 0.51246238E+01 0.68047908E-02 -0.30873775E-01 -0.85 29 0.51474943E+01 0.50485239E+01 0.71466486E-02 0.98970413E-01 2.73 30 0.51817837E+01 0.52473769E+01 0.10222896E-01 -0.65593243E-01 -1.85 31 0.52933049E+01 0.53193955E+01 0.42733354E-02 -0.26090622E-01 -0.71 32 0.52933049E+01 0.53051424E+01 0.41897092E-02 -0.11837482E-01 -0.32 33 0.52149358E+01 0.52143073E+01 0.56462260E-02 0.62847137E-03 0.02 34 0.50875964E+01 0.50651140E+01 0.19627747E-02 0.22482395E-01 0.61 35 0.49836068E+01 0.49371028E+01 0.39034281E-02 0.46504021E-01 1.27 36 0.51119876E+01 0.51285734E+01 0.61793886E-02 -0.16585827E-01 -0.46 37 0.51416636E+01 0.51287856E+01 0.25819477E-02 0.12877941E-01 0.35 38 0.51929569E+01 0.51895995E+01 0.26679568E-02 0.33574104E-02 0.09 39 0.52626901E+01 0.53297977E+01 0.35523043E-02 -0.67107677E-01 -1.82 40 0.51984968E+01 0.52325974E+01 0.66675581E-02 -0.34100533E-01 -0.94 . . . . . . . . . . . . . . . . . . 144 0.60684257E+01 0.60539222E+01 0.65665278E-02 0.14503479E-01 0.40 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - * - 2.25+ * + - * * - - * * - - * * * * * * - - * * * * * * * * * * - 0.75+ * * * * ** * * + - * * * * * * ** * * * * - - ** * * * * * * * * * * *- -* *** * * * * * * * * ** * * * * * * - - * * * * * ** * ** * * * * * * * * * * - -0.75+ *** * * * * * * * * * * * * * * + - * * * * ** * * * ** * * - - * * * * ** * - - * - - * * * - -2.25+ * + - * - - - - - - * - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 72.5 144.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - *** - - - - **** - - - - * - - *- 6+ ** + 2.25+ * + - * - - ** - - *** - - ** - - *** - - *** - - * - - **** - 11+ * + 0.75+ **** + - * - - *** - - ** - - *** - - * - - *** - - * - - **** - 16+ **** + -0.75+ ***** + - ** - - **** - - * - - ***** - - * - - * - - *** - - *** - 21+ * + -2.25+ * + - * - - * - - **** - - - - * - - - - * - -* - 26++---------+---------+----*----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- yes 0.00000000E+00 + --- --- --- --- 2 ma (factor 1) 1 no 0.39514637E+00 + 0.10932407E+00 0.36144500E+01 0.21413498E+00 0.57615775E+00 3 ma (factor 2) 12 no 0.61549562E+00 + 0.80602184E-01 0.76362152E+01 0.48203999E+00 0.74895126E+00 number of observations (n) 144 residual sum of squares .1761643 (backforecasts included) residual standard deviation 0.3695422E-01 based on degrees of freedom 144 - 13 - 2 = 129 approximate condition number 1.160312 output - ifixed(1) = 1 , stp(1) = 0.34526698E-03, mit = 25, stopss = 0.10000000E-05, stopp = 0.10000000E-05 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .0000000 -0.2851486E-03 -1.000000 -1.000000 -1.000000 2 .3951464 0.5888462E-02 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-1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 column 8 9 10 1 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = 144 npare = -1 ARIMA test number 3 Normal problem Test of AIMS input - ifixed(1) = 1 , stp(1) = 0.34526698E-03, mit = 25, stopss = 0.10000000E-05, stopp = 0.10000000E-05 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- yes 0.00000000E+00 + --- --- 2 ma (factor 1) 1 no 0.40000001E+00 + 0.10000000E+01 0.34526698E-03 3 ma (factor 2) 12 no 0.60000002E+00 + 0.10000000E+01 0.34526698E-03 number of observations (n) 144 maximum number of iterations allowed (mit) 25 maximum number of model subroutine calls allowed 50 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-05 maximum scaled relative change in the parameters (stopp) 0.1000E-05 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values .1762 (backforecasts included) residual standard deviation for input parameter values (rsd) 0.3696E-01 based on degrees of freedom 144 - 13 - 2 = 129 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3695E-01 .1762 0.3316E-03 0.3079E-03 y 0.1179E-01 y current parameter values (only unfixed parameters are listed) index 2 3 value .3977237 .6143165 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 9 0.3695E-01 .1762 0.2538E-06 0.5078E-06 y 0.5723E-03 y current parameter values (only unfixed parameters are listed) index 2 3 value .3951464 .6154956 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.47187839E+01 0.26929260E-02 -0.28514862E-03 -0.01 2 0.47706847E+01 0.47647963E+01 0.69545731E-02 0.58884621E-02 0.16 3 0.48828020E+01 0.48944864E+01 0.12293251E-02 -0.11684418E-01 -0.32 4 0.48598123E+01 0.48483033E+01 0.10941615E-02 0.11508942E-01 0.31 5 0.47957907E+01 0.48217874E+01 0.87206867E-02 -0.25996685E-01 -0.72 6 0.49052749E+01 0.49262953E+01 0.38670984E-02 -0.21020412E-01 -0.57 7 0.49972124E+01 0.50171547E+01 0.33660352E-02 -0.19942284E-01 -0.54 8 0.49972124E+01 0.50091262E+01 0.33150972E-02 -0.11913776E-01 -0.32 9 0.49126549E+01 0.49081674E+01 0.63475915E-02 0.44875145E-02 0.12 10 0.47791233E+01 0.47742982E+01 0.54812367E-03 0.48251152E-02 0.13 11 0.46443911E+01 0.46440396E+01 0.87037368E-03 0.35142899E-03 0.01 12 0.47706847E+01 0.47880073E+01 0.18462659E-02 -0.17322540E-01 -0.47 13 0.47449322E+01 0.47850428E+01 0.25026991E-02 -0.40110588E-01 -1.09 14 0.48362818E+01 0.48092742E+01 0.83839493E-02 0.27007580E-01 0.75 15 0.49487600E+01 0.49463511E+01 0.17768922E-02 0.24089813E-02 0.07 16 0.49052749E+01 0.49148927E+01 0.60561643E-03 -0.96178055E-02 -0.26 17 0.48283138E+01 0.48638535E+01 0.70641842E-02 -0.35539627E-01 -0.98 18 0.50039463E+01 0.49584565E+01 0.48680520E-02 0.45489788E-01 1.24 19 0.51357985E+01 0.50850706E+01 0.50811935E-02 0.50727844E-01 1.39 20 0.51357985E+01 0.51182361E+01 0.75049233E-02 0.17562389E-01 0.49 21 0.50625949E+01 0.50386415E+01 0.48592519E-02 0.23953438E-01 0.65 22 0.48903489E+01 0.49177198E+01 0.43599787E-02 -0.27370930E-01 -0.75 23 0.47361984E+01 0.47673893E+01 0.11754329E-02 -0.31190872E-01 -0.84 24 0.49416423E+01 0.48855648E+01 0.38332716E-02 0.56077480E-01 1.53 25 0.49767337E+01 0.49142056E+01 0.64642024E-02 0.62528133E-01 1.72 26 0.50106354E+01 0.50169969E+01 0.84900754E-02 -0.63614845E-02 -0.18 27 0.51817837E+01 0.51307135E+01 0.29082661E-02 0.51070213E-01 1.39 28 0.50937500E+01 0.51246238E+01 0.68047908E-02 -0.30873775E-01 -0.85 29 0.51474943E+01 0.50485239E+01 0.71466486E-02 0.98970413E-01 2.73 30 0.51817837E+01 0.52473769E+01 0.10222896E-01 -0.65593243E-01 -1.85 31 0.52933049E+01 0.53193955E+01 0.42733354E-02 -0.26090622E-01 -0.71 32 0.52933049E+01 0.53051424E+01 0.41897092E-02 -0.11837482E-01 -0.32 33 0.52149358E+01 0.52143073E+01 0.56462260E-02 0.62847137E-03 0.02 34 0.50875964E+01 0.50651140E+01 0.19627747E-02 0.22482395E-01 0.61 35 0.49836068E+01 0.49371028E+01 0.39034281E-02 0.46504021E-01 1.27 36 0.51119876E+01 0.51285734E+01 0.61793886E-02 -0.16585827E-01 -0.46 37 0.51416636E+01 0.51287856E+01 0.25819477E-02 0.12877941E-01 0.35 38 0.51929569E+01 0.51895995E+01 0.26679568E-02 0.33574104E-02 0.09 39 0.52626901E+01 0.53297977E+01 0.35523043E-02 -0.67107677E-01 -1.82 40 0.51984968E+01 0.52325974E+01 0.66675581E-02 -0.34100533E-01 -0.94 . . . . . . . . . . . . . . . . . . 144 0.60684257E+01 0.60539222E+01 0.65665278E-02 0.14503479E-01 0.40 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - * - 2.25+ * + - * * - - * * - - * * * * * * - - * * * * * * * * * * - 0.75+ * * * * ** * * + - * * * * * * ** * * * * - - ** * * * * * * * * * * *- -* *** * * * * * * * * ** * * * * * * - - * * * * * ** * ** * * * * * * * * * * - -0.75+ *** * * * * * * * * * * * * * * + - * * * * ** * * * ** * * - - * * * * ** * - - * - - * * * - -2.25+ * + - * - - - - - - * - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 72.5 144.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - *** - - - - **** - - - - * - - *- 6+ ** + 2.25+ * + - * - - ** - - *** - - ** - - *** - - *** - - * - - **** - 11+ * + 0.75+ **** + - * - - *** - - ** - - *** - - * - - *** - - * - - **** - 16+ **** + -0.75+ ***** + - ** - - **** - - * - - ***** - - * - - * - - *** - - *** - 21+ * + -2.25+ * + - * - - * - - **** - - - - * - - - - * - -* - 26++---------+---------+----*----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- yes 0.00000000E+00 + --- --- --- --- 2 ma (factor 1) 1 no 0.39514637E+00 + 0.10932407E+00 0.36144500E+01 0.21413498E+00 0.57615775E+00 3 ma (factor 2) 12 no 0.61549562E+00 + 0.80602184E-01 0.76362152E+01 0.48203999E+00 0.74895126E+00 number of observations (n) 144 residual sum of squares .1761643 (backforecasts included) residual standard deviation 0.3695422E-01 based on degrees of freedom 144 - 13 - 2 = 129 approximate condition number 1.160312 output - ifixed(1) = 1 , stp(1) = 0.34526698E-03, mit = 25, stopss = 0.10000000E-05, stopp = 0.10000000E-05 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .0000000 -0.2851486E-03 4.718784 0.2692926E-02 -0.7736837E-02 2 .3951464 0.5888462E-02 4.764796 0.6954573E-02 .1622438 3 .6154956 -0.1168442E-01 4.894486 0.1229325E-02 -.3163614 4 0.1150894E-01 4.848303 0.1094162E-02 .3115744 5 -0.2599669E-01 4.821787 0.8720687E-02 -.7239299 6 -0.2102041E-01 4.926295 0.3867098E-02 -.5719634 7 -0.1994228E-01 5.017155 0.3366035E-02 -.5419011 8 -0.1191378E-01 5.009126 0.3315097E-02 -.3236980 9 0.4487514E-02 4.908167 0.6347592E-02 .1232665 10 0.4825115E-02 4.774298 0.5481237E-03 .1305844 11 0.3514290E-03 4.644040 0.8703737E-03 0.9512486E-02 12 -0.1732254E-01 4.788007 0.1846266E-02 -.4693429 13 -0.4011059E-01 4.785043 0.2502699E-02 -1.087911 14 0.2700758E-01 4.809274 0.8383949E-02 .7504063 15 0.2408981E-02 4.946351 0.1776892E-02 0.6526375E-01 16 -0.9617805E-02 4.914893 0.6056164E-03 -.2602977 17 -0.3553963E-01 4.863853 0.7064184E-02 -.9797889 18 0.4548979E-01 4.958457 0.4868052E-02 1.241799 19 0.5072784E-01 5.085071 0.5081194E-02 1.385885 20 0.1756239E-01 5.118236 0.7504923E-02 .4853618 21 0.2395344E-01 5.038641 0.4859252E-02 .6538697 22 -0.2737093E-01 4.917720 0.4359979E-02 -.7458808 23 -0.3119087E-01 4.767389 0.1175433E-02 -.8444681 24 0.5607748E-01 4.885565 0.3833272E-02 1.525716 25 0.6252813E-01 4.914206 0.6464202E-02 1.718540 26 -0.6361485E-02 5.016997 0.8490075E-02 -.1768763 27 0.5107021E-01 5.130713 0.2908266E-02 1.386286 28 -0.3087378E-01 5.124624 0.6804791E-02 -.8499951 29 0.9897041E-01 5.048524 0.7146649E-02 2.729723 30 -0.6559324E-01 5.247377 0.1022290E-01 -1.847069 31 -0.2609062E-01 5.319396 0.4273335E-02 -.7107940 32 -0.1183748E-01 5.305142 0.4189709E-02 -.3224071 33 0.6284714E-03 5.214307 0.5646226E-02 0.1720881E-01 34 0.2248240E-01 5.065114 0.1962775E-02 .6092450 35 0.4650402E-01 4.937103 0.3903428E-02 1.265502 36 -0.1658583E-01 5.128573 0.6179389E-02 -.4552306 37 0.1287794E-01 5.128786 0.2581948E-02 .3493373 38 0.3357410E-02 5.189600 0.2667957E-02 0.9109094E-01 39 -0.6710768E-01 5.329798 0.3552304E-02 -1.824417 40 -0.3410053E-01 5.232597 0.6667558E-02 -.9381749 41 0.1219463E-01 5.197291 0.8220567E-02 .3384739 42 0.8109570E-01 5.303400 0.4347702E-02 2.209838 43 -0.2599812E-01 5.464077 0.8621065E-02 -.7234853 44 0.3964472E-01 5.449293 0.9509217E-03 1.073162 45 -0.4930305E-01 5.391637 0.4463932E-02 -1.344007 46 0.3148174E-01 5.220792 0.3818326E-02 .8564963 47 0.3480530E-01 5.112689 0.2394828E-02 .9438331 48 -0.1578283E-01 5.283641 0.4385786E-02 -.4301315 49 -0.1369572E-01 5.291811 0.2691821E-02 -.3716002 50 -0.5777073E-01 5.335886 0.2812721E-02 -1.567853 51 0.5103493E-01 5.412797 0.6675177E-02 1.404128 52 0.7544565E-01 5.384140 0.5985667E-02 2.068918 53 0.8759022E-02 5.424963 0.8932349E-02 .2442667 54 -0.6526041E-01 5.558322 0.5008423E-02 -1.782426 55 -0.3220892E-01 5.608158 0.5127003E-02 -.8801013 56 -0.3008842E-02 5.608811 0.6971241E-02 -0.8290943E-01 57 -0.3231525E-01 5.500375 0.2926499E-02 -.8772223 58 -0.7542610E-02 5.359401 0.6081311E-02 -.2069280 59 -0.4333639E-01 5.236293 0.5462630E-02 -1.185731 60 -0.4531956E-01 5.348624 0.5658473E-02 -1.241005 61 -0.1794052E-01 5.336061 0.7531134E-02 -.4958867 62 -.1209936 5.357436 0.5048163E-02 -3.305133 63 0.3507853E-01 5.424507 0.1595956E-01 1.052453 64 0.1749611E-01 5.407454 0.4479450E-02 .4769708 65 0.5018997E-01 5.405131 0.2511799E-02 1.361314 66 0.3882313E-01 5.537126 0.7367736E-02 1.072098 67 0.6297827E-01 5.647449 0.8786061E-02 1.754535 68 -0.2923965E-01 5.709412 0.9149850E-02 -.8166690 69 -0.1631451E-01 5.573143 0.4288149E-02 -.4444815 70 -0.1013374E-01 5.443856 0.2222441E-02 -.2747215 71 0.9541988E-02 5.303664 0.1709640E-02 .2584879 72 -0.3415585E-02 5.437138 0.4044272E-02 -0.9298599E-01 73 0.3903103E-01 5.449907 0.1062954E-02 1.056637 74 -0.1090670E-01 5.461945 0.1372465E-01 -.3178771 75 -0.4022503E-01 5.627474 0.4063664E-02 -1.095151 76 0.2844048E-01 5.566271 0.6214323E-02 .7807320 77 0.1121426E-01 5.587208 0.5400050E-02 .3067563 78 0.4964256E-01 5.702930 0.1871668E-02 1.345079 79 0.5904055E-01 5.838113 0.5883237E-02 1.618308 80 -0.2755928E-01 5.876884 0.9425160E-02 -.7712758 81 0.3202915E-02 5.739800 0.4011560E-02 0.8718776E-01 82 -0.7772923E-02 5.620901 0.5830857E-03 -.2103654 83 -0.1928616E-01 5.487346 0.6974652E-03 -.5219862 84 0.2700186E-01 5.600619 0.2991660E-02 .7330902 85 0.1660824E-02 5.647314 0.2645703E-02 0.4505837E-01 86 -0.2606392E-02 5.626624 0.8539895E-02 -0.7249255E-01 87 -0.2446222E-01 5.783364 0.7044362E-03 -.6620805 88 -0.2538681E-02 5.748742 0.5794503E-02 -0.6955843E-01 89 0.1111937E-01 5.750932 0.4953014E-02 .3036354 90 0.4027510E-01 5.883981 0.4640692E-02 1.098561 91 -0.5209446E-02 6.028657 0.6615224E-02 -.1432847 92 -0.5111694E-02 6.008999 0.3712377E-02 -.1390284 93 -0.1879358E-01 5.890912 0.2085805E-02 -.5093759 94 -0.3164721E-01 5.755232 0.1977745E-02 -.8576186 95 0.1116753E-02 5.601002 0.4081179E-02 0.3040589E-01 96 -0.1634359E-01 5.739929 0.1798725E-02 -.4427908 97 -0.4368782E-02 5.756941 0.3328563E-02 -.1187040 98 -0.2424002E-01 5.731350 0.5013280E-02 -.6620679 99 0.8935928E-02 5.865995 0.3376040E-02 .2428262 100 -0.2112389E-02 5.854315 0.2623578E-02 -0.5730692E-01 101 0.1069403E-01 5.861424 0.3720173E-02 .2908634 102 0.3699350E-01 6.008012 0.5973072E-02 1.014402 103 -0.5435944E-03 6.142581 0.4739765E-02 -0.1483245E-01 104 0.2175856E-01 6.124571 0.3277401E-02 .5911270 105 -0.1487160E-01 6.016286 0.4733271E-02 -.4057753 106 -0.2434158E-01 5.873666 0.2872202E-02 -.6606942 107 -0.8780956E-02 5.729093 0.2694011E-02 -.2382511 108 -0.3846788E-01 5.855579 0.2014034E-02 -1.042510 109 -0.3106737E-01 5.860013 0.5282311E-02 -.8494215 110 -0.4756498E-01 5.809617 0.6246401E-02 -1.305924 111 -0.4562807E-01 5.937272 0.7139973E-02 -1.258431 112 -0.3821611E-01 5.890419 0.8410588E-02 -1.062019 113 0.1427984E-01 5.880123 0.8739505E-02 .3977016 114 0.3386688E-01 6.041479 0.7314717E-02 .9349539 115 0.2811670E-01 6.168327 0.3230501E-02 .7637763 116 0.4845667E-01 6.176102 0.4245481E-02 1.320002 117 -0.7352686E-01 6.074942 0.8379970E-02 -2.042893 118 -0.6421566E-02 5.889744 0.5474844E-02 -.1757099 119 -0.1975918E-01 5.756331 0.2676811E-02 -.5361018 120 -0.4263735E-01 5.862720 0.4132055E-02 -1.161070 121 0.2757263E-01 5.858531 0.5686004E-02 .7551216 122 0.4775524E-02 5.830035 0.7257788E-02 .1317950 123 0.2732182E-01 5.979032 0.4438667E-02 .7447339 124 0.1287413E-01 5.968540 0.4505274E-02 .3509987 125 0.3981066E-01 6.000444 0.3408638E-02 1.081909 126 -0.3111553E-01 6.188095 0.7384515E-02 -.8593342 127 0.2497196E-01 6.281303 0.4112225E-02 .6799772 128 0.2461386E-01 6.301536 0.4032178E-02 .6700642 129 -0.1259327E-01 6.150321 0.9450397E-02 -.3525018 130 -0.1867294E-02 6.010681 0.2863995E-02 -0.5068238E-01 131 0.1824284E-01 5.873401 0.2283835E-02 .4946059 132 -0.1833391E-01 6.022221 0.4130489E-02 -.4992534 133 -0.9396887E-01 6.127055 0.1726987E-02 -2.545627 134 0.8461380E-01 5.884094 0.1089137E-01 2.396124 135 0.1954794E-01 6.018323 0.5895500E-02 .5358401 136 -0.1254654E-01 6.145945 0.3918415E-02 -.3414406 137 0.1935720E-01 6.137622 0.4133967E-02 .5271245 138 -0.2920914E-01 6.311476 0.4380317E-02 -.7960261 139 -0.1325512E-01 6.446195 0.5531090E-02 -.3627768 140 0.2850580E-01 6.378374 0.2548458E-02 .7732224 141 -0.2713490E-01 6.257617 0.2170790E-02 -.7355543 142 -0.1619673E-01 6.149595 0.2639534E-02 -.4394140 143 0.1824284E-01 5.947904 0.2283835E-02 .4946059 144 0.1450348E-01 6.053922 0.6566528E-02 .3988184 variance covariance matrix column 1 2 3 4 5 6 7 1 0.11951752E-01 -.26744776E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.26744776E-02 0.64967126E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 column 8 9 10 1 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3695422E-01 nnzw = 144 npare = 2 ARIMA test number 4 Normal problem Test of AIMF starpac 2.08s (03/15/90) +*********************** * arima forecasting * *********************** model summary ------------- model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 ------parameter --------parameter description ------estimates index ---------type --order ----------(par) 1 mu --- 0.00000000E+00 2 ma (factor 1) 1 0.40000001E+00 3 ma (factor 2) 12 0.60000002E+00 number of observations (n) 144 residual sum of squares .1762238 (backforecasts included) residual standard deviation 0.3710457E-01 based on degrees of freedom 144 - 13 - 3 = 128 1 starpac 2.08s (03/15/90) +arima forecasting, continued forecasts for origin 1 --------------------95 percent 5.8665977 6.1720271 6.4774561 --------------confidence limits ---------actual 6.0193124 6.4774561 6.6301708 ------forecasts ----------lower ----------upper -------if known i---------i---------i---------i---------i---------i ------------[x] ------------[(] ------------[)] ------------[*] 140 i * i 140 6.4068799 141 i * i 141 6.2304816 142 i * i 142 6.1333981 143 i * i 143 5.9661469 144 i.............*.....................................i 144 6.0684257 145 i (----x----) i 145 6.1435261 6.0701728 6.2168794 146 i (----x-----) i 146 6.0060730 5.9205294 6.0916166 147 i (-----x------) i 147 6.0841560 5.9879541 6.1803579 148 i (------x------) i 148 6.1917963 6.0860047 6.2975879 149 i (-------x------) i 149 6.2011905 6.0866089 6.3157721 150 i (-------x-------) i 150 6.3489223 6.2261786 6.4716659 151 i (-------x--------)i 151 6.4997754 6.3693800 6.6301708 152 i (--------x--------) i 152 6.4535151 6.3158922 6.5911379 153 i (--------x---------) i 153 6.3002486 6.1557593 6.4447379 154 i (---------x---------) i 154 6.2061906 6.0551472 6.3572340 155 i(---------x----------) i 155 6.0239229 5.8665977 6.1812482 156 i (----------x---------) i 156 6.1212778 5.9579124 6.2846432 157 i (-----------x-----------) i 157 6.2001410 6.0210629 6.3792191 158 i(------------x-----------) i 158 6.0626879 5.8733058 6.2520700 159 i (------------x------------) i 159 6.1407709 5.9416175 6.3399243 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 ARIMA test number 5 Normal problem Test of AIMFS input - ifixed(1) = 1 , stp(1) = 0.34526698E-03, mit = 25, stopss = 0.10000000E-05, stopp = 0.10000000E-05 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************** * arima forecasting * *********************** model summary ------------- model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 ------parameter --------parameter description ------estimates index ---------type --order ----------(par) 1 mu --- 0.00000000E+00 2 ma (factor 1) 1 0.40000001E+00 3 ma (factor 2) 12 0.60000002E+00 number of observations (n) 144 residual sum of squares .1762238 (backforecasts included) residual standard deviation 0.3710457E-01 based on degrees of freedom 144 - 13 - 3 = 128 1 starpac 2.08s (03/15/90) +arima forecasting, continued forecasts for origin 1 --------------------95 percent 5.6327066 6.1530747 6.6734428 --------------confidence limits ---------actual 5.8928909 6.6734428 6.9336271 ------forecasts ----------lower ----------upper -------if known i---------i---------i---------i---------i---------i ------------[x] ------------[(] ------------[)] ------------[*] 99 i * i 99 5.8749309 100 i * i 100 5.8522024 101 i * i 101 5.8721180 102 i * i 102 6.0450053 103 i....................*..............................i 103 6.1420374 104 i (--x*-) i 104 6.1240616 6.0507083 6.1974149 6.1463294 105 i (--2--) i 105 6.0022659 5.9167223 6.0878096 6.0014148 106 i (--*x---) i 106 5.8682756 5.7720737 5.9644775 5.8493247 107 i(--*x---) i 107 5.7384982 5.6327066 5.8442898 5.7203116 108 i (-*-x----) i 108 5.8701706 5.7555890 5.9847522 5.8171110 109 i (--*-x----) i 109 5.8980641 5.7753205 6.0208077 5.8289456 110 i (*---x----) i 110 5.8654575 5.7350621 5.9958529 5.7620516 111 i *----x----) i 111 6.0221200 5.8844972 6.1597428 5.8916440 112 i *(----x-----) i 112 6.0031676 5.8586783 6.1476569 5.8522024 113 i (*----x-----) i 113 6.0164557 5.8654122 6.1674991 5.8944030 114 i (-*---x-----) i 114 6.1702318 6.0129066 6.3275571 6.0753460 115 i (---*--x-----) i 115 6.2764707 6.1131053 6.4398360 6.1964440 116 i (-----*x------) i 116 6.2582541 6.0791759 6.4373322 6.2245584 117 i (-*----x-------) i 117 6.1364584 5.9470763 6.3258405 6.0014148 118 i (--*---x-------) i 118 6.0024681 5.8033147 6.2016215 5.8833222 119 i (--*----x-------) i 119 5.8726912 5.6642237 6.0811586 5.7365723 120 i (*------x--------) i 120 6.0043635 5.7869806 6.2217464 5.8200831 121 i (--*----x--------) i 121 6.0322571 5.8063102 6.2582040 5.8861041 122 i (--*-----x--------) i 122 5.9996505 5.7654529 6.2338481 5.8348107 123 i (--*-----x--------) i 123 6.1563129 5.9141450 6.3984809 6.0063534 124 i (--*-----x---------) i 124 6.1373610 5.8874774 6.3872447 5.9814143 125 i (-----*---x---------) i 125 6.1506486 5.8932800 6.4080172 6.0402546 126 i (---*-----x---------) i 126 6.3044252 6.0397840 6.5690665 6.1569791 127 i (------*---x---------) i 127 6.4106646 6.1389446 6.6823845 6.3062754 128 i (--------*-x----------) i 128 6.3924479 6.1059079 6.6789880 6.3261495 129 i (-----*-----x----------) i 129 6.2706518 5.9733620 6.5679417 6.1377273 130 i (-----*----x-----------) i 130 6.1366615 5.8289976 6.4443254 6.0088134 131 i (-------*---x------------) i 131 6.0068846 5.6891847 6.3245845 5.8916440 132 i (------*----x------------) i 132 6.1385565 5.8111281 6.4659848 6.0038872 133 i (------*-----x-----------) i 133 6.1664500 5.8295746 6.5033255 6.0330863 134 i (------*-----x-------------) i 134 6.1338434 5.7877779 6.4799089 5.9687076 135 i (---*--------x-------------) i 135 6.2905059 5.9354887 6.6455231 6.0378709 136 i (-------*-----x-------------) i 136 6.2715540 5.9078050 6.6353030 6.1333981 137 i (--------*----x-------------) i 137 6.2848420 5.9125662 6.6571178 6.1569791 138 i (--------*-----x--------------) i 138 6.4386191 6.0580072 6.8192310 6.2822666 139 i (----------*---x--------------)i 139 6.5448580 6.1560888 6.9336271 6.4329400 1 starpac 2.08s (03/15/90) +arima forecasting, continued forecasts for origin 2 --------------------95 percent 5.7565393 6.2375517 6.7185636 --------------confidence limits ---------actual 5.9970455 6.7185636 6.9590697 ------forecasts ----------lower ----------upper -------if known i---------i---------i---------i---------i---------i ------------[x] ------------[(] ------------[)] ------------[*] 140 i * i 140 6.4068799 141 i * i 141 6.2304816 142 i * i 142 6.1333981 143 i * i 143 5.9661469 144 i.............*.....................................i 144 6.0684257 145 i (--x--) i 145 6.1435261 6.0701728 6.2168794 146 i (--x---) i 146 6.0060730 5.9205294 6.0916166 147 i (---x---) i 147 6.0841560 5.9879541 6.1803579 148 i (---x---) i 148 6.1917963 6.0860047 6.2975879 149 i (---x----) i 149 6.2011905 6.0866089 6.3157721 150 i (----x----) i 150 6.3489223 6.2261786 6.4716659 151 i (-----x----) i 151 6.4997754 6.3693800 6.6301708 152 i (-----x-----) i 152 6.4535151 6.3158922 6.5911379 153 i (-----x-----) i 153 6.3002486 6.1557593 6.4447379 154 i (------x-----) i 154 6.2061906 6.0551472 6.3572340 155 i (-----x------) i 155 6.0239229 5.8665977 6.1812482 156 i (------x------) i 156 6.1212778 5.9579124 6.2846432 157 i (------x-------) i 157 6.2001410 6.0210629 6.3792191 158 i (-------x-------) i 158 6.0626879 5.8733058 6.2520700 159 i (-------x-------) i 159 6.1407709 5.9416175 6.3399243 160 i (-------x--------) i 160 6.2484112 6.0399437 6.4568787 161 i (--------x--------) i 161 6.2578058 6.0404229 6.4751887 162 i (--------x--------) i 162 6.4055371 6.1795902 6.6314840 163 i (--------x---------) i 163 6.5563903 6.3221927 6.7905879 164 i (---------x---------) i 164 6.5101295 6.2679615 6.7522974 165 i (---------x---------) i 165 6.3568630 6.1069794 6.6067467 166 i (----------x----------) i 166 6.2628045 6.0054359 6.5201731 167 i (----------x----------) i 167 6.0805373 5.8158960 6.3451786 168 i (-----------x----------) i 168 6.1778917 5.9061718 6.4496117 169 i (-----------x-----------) i 169 6.2567549 5.9702148 6.5432949 170 i (-----------x-----------) i 170 6.1193018 5.8220119 6.4165916 171 i (-----------x------------) i 171 6.1973848 5.8897209 6.5050488 172 i (------------x------------) i 172 6.3050251 5.9873252 6.6227250 173 i (------------x-------------) i 173 6.3144197 5.9869914 6.6418481 174 i (-------------x-------------) i 174 6.4621515 6.1252756 6.7990274 175 i (--------------x-------------)i 175 6.6130042 6.2669387 6.9590697 176 i (--------------x-------------) i 176 6.5667434 6.2117262 6.9217606 177 i (--------------x--------------) i 177 6.4134774 6.0497284 6.7772264 178 i (--------------x---------------) i 178 6.3194189 5.9471431 6.6916947 179 i(---------------x---------------) i 179 6.1371512 5.7565393 6.5177631 180 i (---------------x---------------) i 180 6.2345052 5.8457360 6.6232743 output - ifixed(1) = 1 , stp(1) = 0.34526698E-03, mit = 25, stopss = 0.10000000E-05, stopp = 0.10000000E-05 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .0000000 -1.000000 6.124062 6.143526 -1.000000 2 .4000000 -1.000000 6.002266 6.006073 -1.000000 3 .6000000 -1.000000 5.868276 6.084156 -1.000000 4 -1.000000 5.738498 6.191796 -1.000000 5 -1.000000 5.870171 6.201190 -1.000000 6 -1.000000 5.898064 6.348922 -1.000000 7 -1.000000 5.865458 6.499775 -1.000000 8 -1.000000 6.022120 6.453515 -1.000000 9 -1.000000 6.003168 6.300249 -1.000000 10 -1.000000 6.016456 6.206191 -1.000000 11 -1.000000 6.170232 6.023923 -1.000000 12 -1.000000 6.276471 6.121278 -1.000000 13 -1.000000 6.258254 6.200141 -1.000000 14 -1.000000 6.136458 6.062688 -1.000000 15 -1.000000 6.002468 6.140771 -1.000000 16 -1.000000 5.872691 6.248411 -1.000000 17 -1.000000 6.004364 6.257806 -1.000000 18 -1.000000 6.032257 6.405537 -1.000000 19 -1.000000 5.999650 6.556390 -1.000000 20 -1.000000 6.156313 6.510129 -1.000000 21 -1.000000 6.137361 6.356863 -1.000000 22 -1.000000 6.150649 6.262805 -1.000000 23 -1.000000 6.304425 6.080537 -1.000000 24 -1.000000 6.410665 6.177892 -1.000000 25 -1.000000 6.392448 6.256755 -1.000000 26 -1.000000 6.270652 6.119302 -1.000000 27 -1.000000 6.136662 6.197385 -1.000000 28 -1.000000 6.006885 6.305025 -1.000000 29 -1.000000 6.138556 6.314420 -1.000000 30 -1.000000 6.166450 6.462152 -1.000000 31 -1.000000 6.133843 6.613004 -1.000000 32 -1.000000 6.290506 6.566743 -1.000000 33 -1.000000 6.271554 6.413477 -1.000000 34 -1.000000 6.284842 6.319419 -1.000000 35 -1.000000 6.438619 6.137151 -1.000000 36 -1.000000 6.544858 6.234505 -1.000000 37 -1.000000 -1.000000 -1.000000 -1.000000 38 -1.000000 -1.000000 -1.000000 -1.000000 39 -1.000000 -1.000000 -1.000000 -1.000000 40 -1.000000 -1.000000 -1.000000 -1.000000 41 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-1.000000 -1.000000 -1.000000 -1.000000 134 -1.000000 -1.000000 -1.000000 -1.000000 135 -1.000000 -1.000000 -1.000000 -1.000000 136 -1.000000 -1.000000 -1.000000 -1.000000 137 -1.000000 -1.000000 -1.000000 -1.000000 138 -1.000000 -1.000000 -1.000000 -1.000000 139 -1.000000 -1.000000 -1.000000 -1.000000 140 -1.000000 -1.000000 -1.000000 -1.000000 141 -1.000000 -1.000000 -1.000000 -1.000000 142 -1.000000 -1.000000 -1.000000 -1.000000 143 -1.000000 -1.000000 -1.000000 -1.000000 144 -1.000000 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 7 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 column 8 9 10 1 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 7 -1.0000000 -1.0000000 -1.0000000 8 -1.0000000 -1.0000000 -1.0000000 9 -1.0000000 -1.0000000 -1.0000000 10 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = 144 npare = -1 1test of arima estimation routines starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- yes 0.00000000E+00 + --- --- 2 ma (factor 1) 1 no 0.40000001E+00 + default 0.99986792E-04 3 ma (factor 2) 12 no 0.60000002E+00 + default 0.10003646E-03 number of observations (n) 144 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values .1762 (backforecasts included) residual standard deviation for input parameter values (rsd) 0.3696E-01 based on degrees of freedom 144 - 13 - 2 = 129 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3695E-01 .1762 0.3310E-03 0.3108E-03 y 0.1185E-01 y current parameter values (only unfixed parameters are listed) index 2 3 value .3978395 .6143954 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3695E-01 .1762 0.7190E-05 0.5423E-05 y 0.1415E-02 y current parameter values (only unfixed parameters are listed) index 2 3 value .3958244 .6151285 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.47187681E+01 0.21558066E-02 -0.26941299E-03 -0.01 2 0.47706847E+01 0.47648263E+01 0.60863523E-02 0.58584213E-02 0.16 3 0.48828020E+01 0.48944783E+01 0.89806574E-03 -0.11676311E-01 -0.32 4 0.48598123E+01 0.48483057E+01 0.10091885E-02 0.11506557E-01 0.31 5 0.47957907E+01 0.48217435E+01 0.73709176E-02 -0.25952816E-01 -0.72 6 0.49052749E+01 0.49262934E+01 0.36973995E-02 -0.21018505E-01 -0.57 7 0.49972124E+01 0.50171647E+01 0.30339733E-02 -0.19952297E-01 -0.54 8 0.49972124E+01 0.50091429E+01 0.27047198E-02 -0.11930466E-01 -0.32 9 0.49126549E+01 0.49082065E+01 0.48736250E-02 0.44484138E-02 0.12 10 0.47791233E+01 0.47743015E+01 0.43279387E-03 0.48217773E-02 0.13 11 0.46443911E+01 0.46440344E+01 0.68571611E-03 0.35667419E-03 0.01 12 0.47706847E+01 0.47880149E+01 0.16298241E-02 -0.17330170E-01 -0.47 13 0.47449322E+01 0.47850542E+01 0.21114196E-02 -0.40122032E-01 -1.09 14 0.48362818E+01 0.48093300E+01 0.60616829E-02 0.26951790E-01 0.74 15 0.49487600E+01 0.49463401E+01 0.13491899E-02 0.24199486E-02 0.07 16 0.49052749E+01 0.49148912E+01 0.55256154E-03 -0.96163750E-02 -0.26 17 0.48283138E+01 0.48638258E+01 0.62988740E-02 -0.35511971E-01 -0.98 18 0.50039463E+01 0.49584646E+01 0.45600482E-02 0.45481682E-01 1.24 19 0.51357985E+01 0.50850368E+01 0.36743137E-02 0.50761700E-01 1.38 20 0.51357985E+01 0.51181865E+01 0.53735338E-02 0.17611980E-01 0.48 21 0.50625949E+01 0.50386286E+01 0.44295774E-02 0.23966312E-01 0.65 22 0.48903489E+01 0.49176936E+01 0.33136446E-02 -0.27344704E-01 -0.74 23 0.47361984E+01 0.47673950E+01 0.96146401E-03 -0.31196594E-01 -0.84 24 0.49416423E+01 0.48855877E+01 0.28916076E-02 0.56054592E-01 1.52 25 0.49767337E+01 0.49141626E+01 0.46875230E-02 0.62571049E-01 1.71 26 0.50106354E+01 0.50169683E+01 0.75428812E-02 -0.63328743E-02 -0.18 27 0.51817837E+01 0.51306939E+01 0.20867446E-02 0.51089764E-01 1.38 28 0.50937500E+01 0.51245804E+01 0.49859481E-02 -0.30830383E-01 -0.84 29 0.51474943E+01 0.50484953E+01 0.63586007E-02 0.98999023E-01 2.72 30 0.51817837E+01 0.52473168E+01 0.78422092E-02 -0.65533161E-01 -1.81 31 0.52933049E+01 0.53194246E+01 0.30534551E-02 -0.26119709E-01 -0.71 32 0.52933049E+01 0.53051696E+01 0.30519150E-02 -0.11864662E-01 -0.32 33 0.52149358E+01 0.52143435E+01 0.42252070E-02 0.59223175E-03 0.02 34 0.50875964E+01 0.50651116E+01 0.18719829E-02 0.22484779E-01 0.61 35 0.49836068E+01 0.49370775E+01 0.29170262E-02 0.46529293E-01 1.26 36 0.51119876E+01 0.51285567E+01 0.56339242E-02 -0.16569138E-01 -0.45 37 0.51416636E+01 0.51287966E+01 0.22941451E-02 0.12866974E-01 0.35 38 0.51929569E+01 0.51896029E+01 0.25340698E-02 0.33540726E-02 0.09 39 0.52626901E+01 0.53298087E+01 0.32726547E-02 -0.67118645E-01 -1.82 40 0.51984968E+01 0.52326279E+01 0.55931653E-02 -0.34131050E-01 -0.93 . . . . . . . . . . . . . . . . . . 144 0.60684257E+01 0.60538840E+01 0.52532773E-02 0.14541626E-01 0.40 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - * - 2.25+ * + - * * - - * * - - * * * * * * - - * * * * * * * * * - 0.75+ * * * * ** * * + - * * * * * * * ** * * * * - - ** * * * * * * * * * * *- -* *** * * * * * * * * ** * * * * * * - - * * * * * ** * ** * * * * * * * * * * - -0.75+ *** * * * * * * * * * * * * * * + - * * * * ** * * * ** * * * - - * * * * ** * - - * - - * * * - -2.25+ * + - * - - - - - - * - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 72.5 144.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - *** - - - - **** - - - - * - - *- 6+ ** + 2.25+ * + - * - - ** - - *** - - ** - - *** - - *** - - * - - **** - 11+ * + 0.75+ *** + - * - - *** - - ** - - *** - - * - - *** - - * - - **** - 16+ **** + -0.75+ ***** + - ** - - ***** - - * - - ***** - - * - - * - - *** - - *** - 21+ * + -2.25+ * + - * - - * - - **** - - - - * - - - - * - -* - 26++---------+---------+----*----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 3 2 0.6620122E-02 -0.3288454E-03 3 -0.5770656E-01 0.4905317E-02 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- yes 0.00000000E+00 + --- --- --- --- 2 ma (factor 1) 1 no 0.39582437E+00 + 0.81364132E-01 0.48648510E+01 0.26110715E+00 0.53054160E+00 3 ma (factor 2) 12 no 0.61512852E+00 + 0.70037968E-01 0.87827864E+01 0.49916440E+00 0.73109263E+00 number of observations (n) 144 residual sum of squares .1761643 (backforecasts included) residual standard deviation 0.3695422E-01 based on degrees of freedom 144 - 13 - 2 = 129 approximate condition number 1.163654 1test of arima forecasting routines starpac 2.08s (03/15/90) +*********************** * arima forecasting * *********************** model summary ------------- model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 ------parameter --------parameter description ------estimates index ---------type --order ----------(par) 1 mu --- 0.00000000E+00 2 ma (factor 1) 1 0.39500001E+00 3 ma (factor 2) 12 0.61500001E+00 number of observations (n) 144 residual sum of squares .1761643 (backforecasts included) residual standard deviation 0.3709829E-01 based on degrees of freedom 144 - 13 - 3 = 128 1 starpac 2.08s (03/15/90) +arima forecasting, continued forecasts for origin 1 --------------------95 percent 5.8659396 6.1720076 6.4780750 --------------confidence limits ---------actual 6.0189734 6.4780750 6.6311092 ------forecasts ----------lower ----------upper -------if known i---------i---------i---------i---------i---------i ------------[x] ------------[(] ------------[)] ------------[*] 140 i * i 140 6.4068799 141 i * i 141 6.2304816 142 i * i 142 6.1333981 143 i * i 143 5.9661469 144 i.............*.....................................i 144 6.0684257 145 i (----x----) i 145 6.1452231 6.0718822 6.2185640 146 i (----x-----) i 146 6.0059705 5.9202518 6.0916891 147 i (-----x------) i 147 6.0836940 5.9871721 6.1802158 148 i (------x------) i 148 6.1916871 6.0854549 6.2979193 149 i (-------x------) i 149 6.2003279 6.0852013 6.3154545 150 i (--------x-------) i 150 6.3482938 6.2249126 6.4716749 151 i (-------x--------)i 151 6.4999924 6.3688755 6.6311092 152 i (--------x--------) i 152 6.4531808 6.3147593 6.5916023 153 i (--------x---------) i 153 6.3004007 6.1550417 6.4457598 154 i (---------x---------) i 154 6.2066751 6.0546947 6.3586555 155 i(---------x----------) i 155 6.0242648 5.8659396 6.1825900 156 i (----------x---------) i 156 6.1220388 5.9576135 6.2864642 157 i (-----------x-----------) i 157 6.2023668 6.0226240 6.3821096 158 i(------------x-----------) i 158 6.0631146 5.8731565 6.2530727 159 i (------------x------------) i 159 6.1408381 5.9411864 6.3404899 1test of arima estimation routines starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 2 0 0 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- yes 0.00000000E+00 + --- --- 2 ma (factor 1) 1 no 0.39500001E+00 + 0.10000000E-06 0.99969810E-04 3 ma (factor 2) 12 no 0.61500001E+00 + 0.10000000E-06 0.10001950E-03 number of observations (n) 144 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 10.15 (backforecasts included) residual standard deviation for input parameter values (rsd) .2683 based on degrees of freedom 144 - 1 - 2 = 141 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 .1542 3.352 .6697 .6851 y .3245 y current parameter values (only unfixed parameters are listed) index 2 3 value .2990500 .2642983 iteration number 6 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 7 0.7159E-01 .7226 0.1344E-04 0.8142E-05 y 0.6337E-03 y current parameter values (only unfixed parameters are listed) index 2 3 value -.1128493 -.8452832 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.47742949E+01 0.72694463E-02 -0.55796146E-01 -0.78 2 0.47706847E+01 0.47127743E+01 0.51632663E-02 0.57910442E-01 0.81 3 0.48828020E+01 0.48773022E+01 0.14831118E-01 0.54998398E-02 0.08 4 0.48598123E+01 0.48778052E+01 0.32172834E-02 -0.17992973E-01 -0.25 5 0.47957907E+01 0.48621345E+01 0.16897860E-02 -0.66343784E-01 -0.93 6 0.49052749E+01 0.46964855E+01 0.23647286E-01 0.20878935E+00 3.09 7 0.49972124E+01 0.49552298E+01 0.18299986E-01 0.41982651E-01 0.61 8 0.49972124E+01 0.49940133E+01 0.16208121E-02 0.31991005E-02 0.04 9 0.49126549E+01 0.49966378E+01 0.96499752E-02 -0.83982944E-01 -1.18 10 0.47791233E+01 0.48641305E+01 0.77496823E-02 -0.85007191E-01 -1.19 11 0.46443911E+01 0.47289343E+01 0.64825760E-02 -0.84543228E-01 -1.19 12 0.47706847E+01 0.46408386E+01 0.67637945E-02 0.12984610E+00 1.82 13 0.47449322E+01 0.47393107E+01 0.13373927E-01 0.56214333E-02 0.08 14 0.48362818E+01 0.47891951E+01 0.98383264E-03 0.47086716E-01 0.66 15 0.49487600E+01 0.48517685E+01 0.12105926E-01 0.96991539E-01 1.37 16 0.49052749E+01 0.49450212E+01 0.80045238E-02 -0.39746284E-01 -0.56 17 0.48283138E+01 0.48429937E+01 0.51137046E-02 -0.14679909E-01 -0.21 18 0.50039463E+01 0.49968147E+01 0.26276914E-01 0.71315765E-02 0.11 19 0.51357985E+01 0.50601549E+01 0.81322779E-03 0.75643539E-01 1.06 20 0.51357985E+01 0.51510434E+01 0.63216258E-02 -0.15244961E-01 -0.21 21 0.50625949E+01 0.50633941E+01 0.11113307E-01 -0.79917908E-03 -0.01 22 0.48903489E+01 0.49826384E+01 0.22590537E-03 -0.92289448E-01 -1.29 23 0.47361984E+01 0.48003621E+01 0.86724823E-02 -0.64163685E-01 -0.90 24 0.49416423E+01 0.48306494E+01 0.75082169E-02 0.11099291E+00 1.56 25 0.49767337E+01 0.49713054E+01 0.12391170E-01 0.54283142E-02 0.08 26 0.50106354E+01 0.50176845E+01 0.12297017E-02 -0.70490837E-02 -0.10 27 0.51817837E+01 0.50963168E+01 0.12869353E-01 0.85466862E-01 1.21 28 0.50937500E+01 0.51670837E+01 0.78587402E-02 -0.73333740E-01 -1.03 29 0.51474943E+01 0.50692739E+01 0.72273030E-02 0.78220367E-01 1.10 30 0.51817837E+01 0.51609492E+01 0.23112187E-01 0.20834446E-01 0.31 31 0.52933049E+01 0.52487555E+01 0.30389554E-02 0.44549465E-01 0.62 32 0.52933049E+01 0.52926617E+01 0.37864810E-02 0.64325333E-03 0.01 33 0.52149358E+01 0.52912478E+01 0.92084808E-02 -0.76312065E-01 -1.07 34 0.50875964E+01 0.51282368E+01 0.70155212E-02 -0.40640354E-01 -0.57 35 0.49836068E+01 0.50199699E+01 0.29241915E-02 -0.36363125E-01 -0.51 36 0.51119876E+01 0.50672030E+01 0.31682409E-02 0.44784546E-01 0.63 37 0.51416636E+01 0.51322174E+01 0.65463400E-02 0.94461441E-02 0.13 38 0.51929569E+01 0.51372890E+01 0.11398286E-02 0.55667877E-01 0.78 39 0.52626901E+01 0.52708106E+01 0.92654843E-02 -0.81205368E-02 -0.11 40 0.51984968E+01 0.52079387E+01 0.12294892E-02 -0.94418526E-02 -0.13 . . . . . . . . . . . . . . . . . . 144 0.60684257E+01 0.59808998E+01 0.44404501E-02 0.87525845E-01 1.22 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - * - - * - 2.25+ * * + - * * * - - * * - - * * * * * - - * * * * * * * * * * *- 0.75+ * * * * * * * + - * * * * * * ** * ** * * * * - - * * * * * * ** * * - - * * * * * ** * *** * * * * ** * * * * - - * * * * * * * * * * * * * * * * * * - -0.75+ * ** * * * ** * ** * * * ** + -* * * * * * ** * * * * * * * * - - *** * * * * * * * - - * * * - - * - -2.25+ + - * - - - - - - - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 72.5 144.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - ***** - - - - ***** - - *- - *** - - * - 6+ * + 2.25+ ** + - ** - - *** - - **** - - ** - - * - - *** - - ** - - **** - 11+ *** + 0.75+ *** + - ******** - - **** - - ***** - - *** - - *** - - *** - - * - - **** - 16+ ****** + -0.75+ **** + - ** - - ****** - - * - - ****** - - ** - - *** - - ***** - - * - 21+ * + -2.25+ + - * - -* - - **** - - - - ************* - - - - * - - - 26++---------+---------+---**----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 2 0 0 1 12 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 3 2 0.7065124E-02 0.2305913E-04 3 0.8349026E-02 0.1079679E-02 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- yes 0.00000000E+00 + --- --- --- --- 2 ma (factor 1) 1 no -0.11284934E+00 + 0.84054291E-01 -0.13425767E+01 -0.25202075E+00 0.26322067E-01 3 ma (factor 2) 12 no -0.84528321E+00 + 0.32858469E-01 -0.25724974E+02 -0.89968807E+00 -0.79087836E+00 number of observations (n) 144 residual sum of squares .7226323 (backforecasts included) residual standard deviation 0.7158947E-01 based on degrees of freedom 144 - 1 - 2 = 141 approximate condition number 2.558160 1test of arima estimation routines starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 2 0 0 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- yes 0.00000000E+00 + --- --- 2 ma (factor 1) 1 no -0.11284934E+00 + 0.10000000E-06 0.10002388E-03 3 ma (factor 2) 12 no -0.84528321E+00 + 0.10000000E-06 0.99989433E-04 number of observations (n) 20 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values .1041 (backforecasts included) residual standard deviation for input parameter values (rsd) 0.7826E-01 based on degrees of freedom 20 - 1 - 2 = 17 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.7654E-01 0.9960E-01 0.4346E-01 0.3128E-01 y .4192 y current parameter values (only unfixed parameters are listed) index 2 3 value -.2757433 -.8138896 iteration number 4 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 5 0.7631E-01 0.9900E-01 .0000 0.3087E-05 y 0.2349E-02 y current parameter values (only unfixed parameters are listed) index 2 3 value -.3534011 -.7990012 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.47942266E+01 0.39359841E-01 -0.75727940E-01 -1.16 2 0.47706847E+01 0.46917362E+01 0.29087529E-01 0.78948498E-01 1.12 3 0.48828020E+01 0.47985849E+01 0.26633294E-01 0.84217072E-01 1.18 4 0.48598123E+01 0.49125643E+01 0.82127685E-02 -0.52752018E-01 -0.70 5 0.47957907E+01 0.48411698E+01 0.13885616E-01 -0.45379162E-01 -0.60 6 0.49052749E+01 0.47797537E+01 0.45625982E-02 0.12552118E+00 1.65 7 0.49972124E+01 0.49496341E+01 0.27785094E-01 0.47578335E-01 0.67 8 0.49972124E+01 0.50367913E+01 0.26043627E-01 -0.39578915E-01 -0.55 9 0.49126549E+01 0.49473863E+01 0.24880374E-01 -0.34731388E-01 -0.48 10 0.47791233E+01 0.47211819E+01 0.41672092E-01 0.57941437E-01 0.91 11 0.46443911E+01 0.46664286E+01 0.27084680E-01 -0.22037506E-01 -0.31 12 0.47706847E+01 0.47132435E+01 0.19472290E-01 0.57441235E-01 0.78 13 0.47449322E+01 0.47423353E+01 0.17497985E-01 0.25968552E-02 0.03 14 0.48362818E+01 0.48623157E+01 0.85289599E-02 -0.26033878E-01 -0.34 15 0.49487600E+01 0.49653673E+01 0.78785811E-02 -0.16607285E-01 -0.22 16 0.49052749E+01 0.48112512E+01 0.20373123E-01 0.94023705E-01 1.28 17 0.48283138E+01 0.48031478E+01 0.20776371E-01 0.25166035E-01 0.34 18 0.50039463E+01 0.50128398E+01 0.30839583E-02 -0.88934898E-02 -0.12 19 0.51357985E+01 0.50882201E+01 0.27785094E-01 0.47578335E-01 0.67 20 0.51357985E+01 0.51526127E+01 0.10008746E-03 -0.16814232E-01 -0.22 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - - 2.25+ + - - - - - * - - * * * - 0.75+ * * + - * * - - * - - * * - - * * * *- -0.75+ * * * * + - - -* - - - - - -2.25+ + - - - - - - - - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 10.5 20.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+---**----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ********* - - - - *** - - - - ******* - - - - *** - - - 6+ * + 2.25+ + - ** - - - - *** - - - - ** - - * - - ***** - - * * * - 11+ *** + 0.75+ * * + - *** - - ** - - **** - - * - - ** - - * - - ** - - *** * - 16+ * + -0.75+ * * * * + - * - - - - ** - - * - - * - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 2 0 0 1 12 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 3 2 0.4351040E-01 -0.4334470E-02 3 -.1310455 0.2514401E-01 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- yes 0.00000000E+00 + --- --- --- --- 2 ma (factor 1) 1 no -0.35340109E+00 + 0.20859146E+00 -0.16942260E+01 -0.71627790E+00 0.94757080E-02 3 ma (factor 2) 12 no -0.79900122E+00 + 0.15856864E+00 -0.50388350E+01 -0.10748557E+01 -0.52314675E+00 number of observations (n) 20 residual sum of squares 0.9900361E-01 (backforecasts included) residual standard deviation 0.7631344E-01 based on degrees of freedom 20 - 1 - 2 = 17 approximate condition number 1.326908 1test of arima estimation routines starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 0 1 1 2 0 0 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- yes 0.00000000E+00 + --- --- 2 ma (factor 1) 1 no -0.35340109E+00 + 0.10000000E-06 0.94112307E-04 3 ma (factor 2) 12 no -0.79900122E+00 + 0.10000000E-06 0.50354287E-04 number of observations (n) 144 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 809.3 (backforecasts included) residual standard deviation for input parameter values (rsd) 2.387 based on degrees of freedom 144 - 0 - 2 = 142 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 4 2.342 778.6 0.3788E-01 .9866 y 0.1668E-01 n current parameter values (only unfixed parameters are listed) index 2 3 value -.3609480 -.8261147 iteration number 10 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 20 1.590 358.9 0.3282E-04 0.4247E-04 y 0.2700E-03 y current parameter values (only unfixed parameters are listed) index 2 3 value -.9839591 -.8811027 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.24277158E+01 0.29712719E+00 0.22907829E+01 1.47 2 0.47706847E+01 0.37064140E+01 0.23684973E+00 0.10642707E+01 0.68 3 0.48828020E+01 0.26163495E+01 0.28660595E+00 0.22664526E+01 1.45 4 0.48598123E+01 0.37907469E+01 0.23926561E+00 0.10690653E+01 0.68 5 0.47957907E+01 0.26171806E+01 0.28684962E+00 0.21786101E+01 1.39 6 0.49052749E+01 0.35888097E+01 0.23753864E+00 0.13164651E+01 0.84 7 0.49972124E+01 0.27654440E+01 0.28593168E+00 0.22317684E+01 1.43 8 0.49972124E+01 0.36579878E+01 0.23886882E+00 0.13392246E+01 0.85 9 0.49126549E+01 0.27921190E+01 0.27622196E+00 0.21205359E+01 1.35 10 0.47791233E+01 0.35198319E+01 0.23902079E+00 0.12592914E+01 0.80 11 0.46443911E+01 0.26344242E+01 0.27297390E+00 0.20099669E+01 1.28 12 0.47706847E+01 0.35889409E+01 0.19511078E+00 0.11817439E+01 0.75 13 0.47449322E+01 0.40441933E+01 0.31065288E+00 0.70073891E+00 0.45 14 0.48362818E+01 0.36132679E+01 0.18408987E+00 0.12230139E+01 0.77 15 0.49487600E+01 0.41230626E+01 0.28930351E+00 0.82569742E+00 0.53 16 0.49052749E+01 0.37193532E+01 0.17370085E+00 0.11859217E+01 0.75 17 0.48283138E+01 0.40133243E+01 0.27915284E+00 0.81498957E+00 0.52 18 0.50039463E+01 0.38506448E+01 0.16411890E+00 0.11533015E+01 0.73 19 0.51357985E+01 0.42425532E+01 0.27404583E+00 0.89324522E+00 0.57 20 0.51357985E+01 0.39937854E+01 0.15704829E+00 0.11420131E+01 0.72 21 0.50625949E+01 0.41531706E+01 0.25486562E+00 0.90942430E+00 0.58 22 0.48903489E+01 0.38428407E+01 0.15013574E+00 0.10475082E+01 0.66 23 0.47361984E+01 0.38934593E+01 0.24417102E+00 0.84273911E+00 0.54 24 0.49416423E+01 0.36130376E+01 0.19169852E+00 0.13286047E+01 0.84 25 0.49767337E+01 0.29492507E+01 0.26084557E+00 0.20274830E+01 1.29 26 0.50106354E+01 0.36800799E+01 0.19255322E+00 0.13305554E+01 0.84 27 0.51817837E+01 0.30970516E+01 0.24684931E+00 0.20847321E+01 1.33 28 0.50937500E+01 0.38120639E+01 0.19225471E+00 0.12816861E+01 0.81 29 0.51474943E+01 0.30073736E+01 0.24450488E+00 0.21401207E+01 1.36 30 0.51817837E+01 0.38285389E+01 0.18964060E+00 0.13532448E+01 0.86 31 0.52933049E+01 0.31184552E+01 0.24382284E+00 0.21748497E+01 1.38 32 0.52933049E+01 0.39206100E+01 0.19166896E+00 0.13726950E+01 0.87 33 0.52149358E+01 0.31420622E+01 0.23371764E+00 0.20728736E+01 1.32 34 0.50875964E+01 0.37510281E+01 0.19258934E+00 0.13365684E+01 0.85 35 0.49836068E+01 0.29658256E+01 0.23072919E+00 0.20177813E+01 1.28 36 0.51119876E+01 0.38866801E+01 0.15862975E+00 0.12253075E+01 0.77 37 0.51416636E+01 0.41439323E+01 0.28072351E+00 0.99773121E+00 0.64 38 0.51929569E+01 0.39118478E+01 0.15150948E+00 0.12811091E+01 0.81 39 0.52626901E+01 0.42509727E+01 0.26344684E+00 0.10117173E+01 0.65 40 0.51984968E+01 0.39321842E+01 0.14381549E+00 0.12663126E+01 0.80 . . . . . . . . . . . . . . . . . . 144 0.60684257E+01 0.44420295E+01 0.50822157E-01 0.16263962E+01 1.02 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - - 2.25+ + - - - - -* ** ** ** - - * * * ** ** * ** * * ** * ** ** **** * ******* * ** *** - 0.75+ ** * * * ** ** ** ** * ** ** ** ** ** ** **** ** ** ** ********** ** * *********** * *** ** * *+ - ** * ******* ** ** ** * ** * * - - * - - - - - -0.75+ + - - - - - - - - -2.25+ + - - - - - - - - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 72.5 144.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----***********************--++ 3.75++---------+---------+----+----+---------+---------++ - ************************ - - - - *********************** - - - - *********************** - - - - *********************** - - - 6+ *********************** + 2.25+ + - ********************** - - - - ********************** - - - - ********************** - - ***** * *- - ********************* - - ************* - 11+ ********************* + 0.75+ ************** + - ********************* - - * *********** - - ********************* - -* - - ********************* - - - - ********************* - - - 16+ ********************* + -0.75+ + - ********************* - - - - ********************* - - - - ********************* - - - - ********************* - - - 21+ ******************** + -2.25+ + - ********************* - - - - ******************** - - - - ********************* - - - - ******************** - - - 26++---------+---------+----********************-----++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 0 1 1 2 0 0 1 12 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 3 2 0.9982253E-04 -0.2964786E-04 3 -0.9956544E-01 0.8882613E-03 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- yes 0.00000000E+00 + --- --- --- --- 2 ma (factor 1) 1 no -0.98395908E+00 + 0.99911224E-02 -0.98483337E+02 -0.10005018E+01 -0.96741647E+00 3 ma (factor 2) 12 no -0.88110274E+00 + 0.29803714E-01 -0.29563522E+02 -0.93044972E+00 -0.83175576E+00 number of observations (n) 144 residual sum of squares 358.8580 (backforecasts included) residual standard deviation 1.589707 based on degrees of freedom 144 - 0 - 2 = 142 approximate condition number 2.997916 the residual sum of squares after the least squares fit is greater than the sum of squares about the mean y observation. the model is less representative of the data than a simple average. data and model should be checked to be sure that they are compatible. 1test of arima estimation routines starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- yes 0.00000000E+00 + --- --- 2 ma (factor 1) 1 no 0.50000000E+00 + 0.10000000E-06 0.10001659E-03 number of observations (n) 10 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 1016. (backforecasts included) residual standard deviation for input parameter values (rsd) 11.27 based on degrees of freedom 10 - 1 - 1 = 8 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 8.478 575.1 .4342 .4076 y 1.000 y current parameter values (only unfixed parameters are listed) index 2 value -0.6510198E-02 iteration number 7 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 9 6.330 320.6 0.1047E-05 0.7903E-06 y 0.1460E-03 y current parameter values (only unfixed parameters are listed) index 2 value -.8852314 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.46000000E+03 0.45874460E+03 0.19617532E+01 0.12554016E+01 0.21 2 0.45700000E+03 0.46111133E+03 0.15356779E+01 -0.41113281E+01 -0.67 3 0.45200000E+03 0.45336053E+03 0.70288450E+00 -0.13605347E+01 -0.22 4 0.45900000E+03 0.45079562E+03 0.83967602E+00 0.82043762E+01 1.31 5 0.46200000E+03 0.46626279E+03 0.20535929E+01 -0.42627869E+01 -0.71 6 0.45900000E+03 0.45822647E+03 0.24988103E+01 0.77352905E+00 0.13 7 0.46300000E+03 0.45968475E+03 0.23357790E+01 0.33152466E+01 0.56 8 0.47900000E+03 0.46593475E+03 0.15382589E+01 0.13065247E+02 2.13 9 0.49300000E+03 0.49056577E+03 0.34481776E+01 0.24342346E+01 0.46 10 0.49000000E+03 0.49515485E+03 0.26644228E+01 -0.51548462E+01 -0.90 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - - 2.25+ + - * - - - - - - * - 0.75+ + - * * - -* - - * - - * - -0.75+ * * + - *- - - - - - - -2.25+ + - - - - - - - - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 5.5 10.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----**---+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******* - - - - ** - - - - ******* - - - - * - - - 6+ ******** + 2.25+ + - ** - - * - - ** - - - - * - - - - - - * - 11+ + 0.75+ + - - - * * - - - - * - - - - * - - - - * - 16+ + -0.75+ * * + - - - * - - - - - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.2550332E-01 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- yes 0.00000000E+00 + --- --- --- --- 2 ma (factor 1) 1 no -0.88523144E+00 + 0.15969759E+00 -0.55431733E+01 -0.11823480E+01 -0.58811492E+00 number of observations (n) 10 residual sum of squares 320.6011 (backforecasts included) residual standard deviation 6.330492 based on degrees of freedom 10 - 1 - 1 = 8 approximate condition number 1.000000 1test of arima estimation routines starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 1 0 1 1 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 ar (factor 1) 1 no 0.30000001E+00 + 0.10000000E-06 0.10003646E-03 2 mu --- yes 0.00000000E+00 + --- --- 3 ma (factor 1) 1 no 0.69999999E+00 + 0.10000000E-06 0.99965509E-04 number of observations (n) 12 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 89.16 (backforecasts included) residual standard deviation for input parameter values (rsd) 2.986 based on degrees of freedom 12 - 0 - 2 = 10 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 2.051 42.05 .5284 .4644 y .5032 y current parameter values (only unfixed parameters are listed) index 1 3 value .9076104 .6921584 iteration number 9 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 14 2.012 40.50 0.9232E-05 0.6799E-05 y 0.3337E-03 y current parameter values (only unfixed parameters are listed) index 1 3 value .9911920 .8248583 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.20000000E+01 0.64458203E+00 0.33758968E+00 0.13554180E+01 0.68 2 0.80000001E+00 0.86435622E+00 0.52753991E+00 -0.64356208E-01 -0.03 3 -0.30000001E+00 0.84603840E+00 0.43451321E+00 -0.11460384E+01 -0.58 4 -0.30000001E+00 0.64796168E+00 0.23311917E+00 -0.94796169E+00 -0.47 5 -0.19000000E+01 0.48457634E+00 0.31211543E+00 -0.23845763E+01 -1.20 6 0.30000001E+00 0.83672583E-01 0.80148804E+00 0.21632743E+00 0.12 7 0.32000000E+01 0.11891818E+00 0.61027592E+00 0.30810819E+01 1.61 8 0.16000000E+01 0.63035870E+00 0.30570316E+00 0.96964133E+00 0.49 9 -0.69999999E+00 0.78609055E+00 0.48216805E+00 -0.14860905E+01 -0.76 10 0.30000000E+01 0.53197980E+00 0.13316157E+00 0.24680202E+01 1.23 11 0.43000002E+01 0.93780899E+00 0.63160807E+00 0.33621912E+01 1.76 12 0.11000000E+01 0.14887947E+01 0.13442510E+01 -0.38879466E+00 -0.26 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - - 2.25+ + - - - * - - * - - * - 0.75+ + -* * - - - - * * - - *- -0.75+ * * + - * - - * - - - - - -2.25+ + - - - - - - - - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 6.5 12.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----******---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ********* - - - - *** - - - - **** - - - - **** - - - 6+ *** + 2.25+ + - *** - - - - **** - - * - - *** - - * - - *** - - * - 11+ * + 0.75+ + - - - * * - - - - - - - - * * - - - - * - 16+ + -0.75+ * * + - - - * - - - - * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 1 0 1 1 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 3 1 0.9677834E-03 0.1602147E-02 3 .2050058 0.6310944E-01 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 ar (factor 1) 1 no 0.99119204E+00 + 0.31109218E-01 0.31861683E+02 0.93414694E+00 0.10482372E+01 2 mu --- yes 0.00000000E+00 + --- --- --- --- 3 ma (factor 1) 1 no 0.82485831E+00 + 0.25121590E+00 0.32834637E+01 0.36420262E+00 0.12855140E+01 number of observations (n) 12 residual sum of squares 40.49526 (backforecasts included) residual standard deviation 2.012343 based on degrees of freedom 12 - 0 - 2 = 10 approximate condition number 8.250524 the residual sum of squares after the least squares fit is greater than the sum of squares about the mean y observation. the model is less representative of the data than a simple average. data and model should be checked to be sure that they are compatible. 1test of error checking facilities starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aime ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to 1 . the input value of nfac is 0. the value of the argument nfac must be greater than or equal to 1 . the correct form of the call statement is call aime (y, n, mspec, nfac, par, npar, res, ldstak) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimec ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to 1 . the input value of nfac is 0. the value of the argument nfac must be greater than or equal to 1 . the correct form of the call statement is call aimec (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimes ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to 1 . the input value of nfac is 0. the value of the argument nfac must be greater than or equal to 1 . the correct form of the call statement is call aimes (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt, + npare, rsd, pv, sdpv, sdres, vcv, ivcv) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimf ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to 1 . the input value of nfac is 0. the value of the argument nfac must be greater than or equal to 1 . ***** error ***** attempt has been made to de-allocate a non-existent allocation in dstak. the correct form of the call statement is call aimf (y, n, mspec, nfac, par, npar, ldstak) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimfs ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to 1 . the input value of nfac is 0. the value of the argument nfac must be greater than or equal to 1 . ***** error ***** attempt has been made to de-allocate a non-existent allocation in dstak. the correct form of the call statement is call aimfs (y, n, mspec, nfac, par, npar, ldstak, + nfcst, nfcsto, ifcsto, nprt, fcst, ifcst, fcstsd) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aime ------------------------------------- the number of values in array mspc less than 0 is 1. the values in the array mspc must all be greater than or equal to 0. the correct form of the call statement is call aime (y, n, mspec, nfac, par, npar, res, ldstak) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimec ------------------------------------- the number of values in array mspc less than 0 is 1. the values in the array mspc must all be greater than or equal to 0. the correct form of the call statement is call aimec (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimes ------------------------------------- the number of values in array mspc less than 0 is 1. the values in the array mspc must all be greater than or equal to 0. the correct form of the call statement is call aimes (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt, + npare, rsd, pv, sdpv, sdres, vcv, ivcv) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimf ------------------------------------- the number of values in array mspc less than 0 is 1. the values in the array mspc must all be greater than or equal to 0. ***** error ***** attempt has been made to de-allocate a non-existent allocation in dstak. the correct form of the call statement is call aimf (y, n, mspec, nfac, par, npar, ldstak) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimfs ------------------------------------- the number of values in array mspc less than 0 is 1. the values in the array mspc must all be greater than or equal to 0. ***** error ***** attempt has been made to de-allocate a non-existent allocation in dstak. the correct form of the call statement is call aimfs (y, n, mspec, nfac, par, npar, ldstak, + nfcst, nfcsto, ifcsto, nprt, fcst, ifcst, fcstsd) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aime ------------------------------------- the input value of npar is 1. the value of the argument npar must be greater than or equal to 3 = one plus the sum of mspec(1,j)+mspec(3,j) for j = 1, ..., nfac, = one plus the number of autoregressive parameters plus the number of moving average parameters. the correct form of the call statement is call aime (y, n, mspec, nfac, par, npar, res, ldstak) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimec ------------------------------------- the input value of npar is 1. the value of the argument npar must be greater than or equal to 3 = one plus the sum of mspec(1,j)+mspec(3,j) for j = 1, ..., nfac, = one plus the number of autoregressive parameters plus the number of moving average parameters. the correct form of the call statement is call aimec (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimes ------------------------------------- the input value of npar is 1. the value of the argument npar must be greater than or equal to 3 = one plus the sum of mspec(1,j)+mspec(3,j) for j = 1, ..., nfac, = one plus the number of autoregressive parameters plus the number of moving average parameters. the correct form of the call statement is call aimes (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt, + npare, rsd, pv, sdpv, sdres, vcv, ivcv) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimf ------------------------------------- the input value of npar is 1. the value of the argument npar must be greater than or equal to 3 = one plus the sum of mspec(1,j)+mspec(3,j) for j = 1, ..., nfac, = one plus the number of autoregressive parameters plus the number of moving average parameters. ***** error ***** attempt has been made to de-allocate a non-existent allocation in dstak. the correct form of the call statement is call aimf (y, n, mspec, nfac, par, npar, ldstak) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimfs ------------------------------------- the input value of npar is 1. the value of the argument npar must be greater than or equal to 3 = one plus the sum of mspec(1,j)+mspec(3,j) for j = 1, ..., nfac, = one plus the number of autoregressive parameters plus the number of moving average parameters. ***** error ***** attempt has been made to de-allocate a non-existent allocation in dstak. the correct form of the call statement is call aimfs (y, n, mspec, nfac, par, npar, ldstak, + nfcst, nfcsto, ifcsto, nprt, fcst, ifcst, fcstsd) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimec ------------------------------------- the input value of npare is 0. the value of the argument npare must be greater than or equal to 1 . the correct form of the call statement is call aimec (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimes ------------------------------------- the input value of npare is 0. the value of the argument npare must be greater than or equal to 1 . the correct form of the call statement is call aimes (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt, + npare, rsd, pv, sdpv, sdres, vcv, ivcv) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimfs ------------------------------------- the input value of ifcst is 0. the first dimension of fcst , as indicated by the argument ifcst , must be greater than or equal to nfcst . ***** error ***** attempt has been made to de-allocate a non-existent allocation in dstak. the correct form of the call statement is call aimfs (y, n, mspec, nfac, par, npar, ldstak, + nfcst, nfcsto, ifcsto, nprt, fcst, ifcst, fcstsd) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aimec ------------------------------------- the input value of npare is 0. the value of the argument npare must be greater than or equal to 1 . the number of values in vector scale less than or equal to zero is 1. since the first value of the vector scale is greater than zero all of the values must be greater than zero . the correct form of the call statement is call aimec (y, n, mspec, nfac, par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt) 1****test routines with correct call**** test of aov1 starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 1 source d.f. sum of squares mean squares f ratio f prob. between groups 2 5.651040E+02 2.825520E+02 0.261E+02 0.000 slope 1 6.450886E+01 6.450886E+01 0.141E+01 0.257 devs. about line 1 5.005952E+02 5.005952E+02 0.462E+02 0.000 within groups 13 1.408333E+02 1.083333E+01 total 15 7.059375E+02 kruskal-wallis rank test for difference between group means * h = 0.114E+02, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.100000E+01 6 7.81667E+01+ 3.86868E+00+ 1.57938E+00 7.20000E+01 8.30000E+01 76.0 7.41067E+01 to 8.22266E+01 1.150000E+01 5 6.40000E+01- 3.00000E+00 1.34164E+00 6.10000E+01 6.70000E+01 15.0 6.02750E+01 to 6.77250E+01 1.200000E+01 5 7.40000E+01 2.73861E+00- 1.22474E+00 7.10000E+01 7.80000E+01 45.0 7.05996E+01 to 7.74004E+01 total 16 7.24375E+01 6.10000E+01 8.30000E+01 fixed effects model 3.29140E+00 8.22851E-01 7.06594E+01 to 7.42156E+01 random effects model 7.29576E+00 4.21221E+00 5.43138E+01 to 9.05612E+01 ungrouped data 6.86021E+00 1.71505E+00 6.87815E+01 to 7.60935E+01 pairwise multiple comparison of means. the means hare put in increasing order in groups separated by *****. a mean is adjudged non-significantly different from any mean in the same group and significantly different at the .05 level from any mean in another group. ***** ***** indicates adjacent groups have no common mean. newman-keuls technique, hartley modification. (approximate if group numbers are unequal.) 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, scheffe technique. 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 0.4756, p = 0.395 (approx.) bartlett-box f = 0.269, p = 0.764 maximum variance / minimum variance = 1.9956 model ii - components of variance. estimate of between component 5.114704E+01 expected value for ierr is 0 returned value for ierr is 0 check to see if tags have been changed 11.500000 11.500000 11.500000 11.500000 11.500000 12.000000 12.000000 12.000000 12.000000 12.000000 11.000000 11.000000 11.000000 11.000000 11.000000 -11.000000 11.000000 1printout not supressed. test of aov1s starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 1 source d.f. sum of squares mean squares f ratio f prob. between groups 2 5.651040E+02 2.825520E+02 0.261E+02 0.000 slope 1 6.450886E+01 6.450886E+01 0.141E+01 0.257 devs. about line 1 5.005952E+02 5.005952E+02 0.462E+02 0.000 within groups 13 1.408333E+02 1.083333E+01 total 15 7.059375E+02 kruskal-wallis rank test for difference between group means * h = 0.114E+02, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.100000E+01 6 7.81667E+01+ 3.86868E+00+ 1.57938E+00 7.20000E+01 8.30000E+01 76.0 7.41067E+01 to 8.22266E+01 1.150000E+01 5 6.40000E+01- 3.00000E+00 1.34164E+00 6.10000E+01 6.70000E+01 15.0 6.02750E+01 to 6.77250E+01 1.200000E+01 5 7.40000E+01 2.73861E+00- 1.22474E+00 7.10000E+01 7.80000E+01 45.0 7.05996E+01 to 7.74004E+01 total 16 7.24375E+01 6.10000E+01 8.30000E+01 fixed effects model 3.29140E+00 8.22851E-01 7.06594E+01 to 7.42156E+01 random effects model 7.29576E+00 4.21221E+00 5.43138E+01 to 9.05612E+01 ungrouped data 6.86021E+00 1.71505E+00 6.87815E+01 to 7.60935E+01 pairwise multiple comparison of means. the means hare put in increasing order in groups separated by *****. a mean is adjudged non-significantly different from any mean in the same group and significantly different at the .05 level from any mean in another group. ***** ***** indicates adjacent groups have no common mean. newman-keuls technique, hartley modification. (approximate if group numbers are unequal.) 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, scheffe technique. 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 0.4756, p = 0.395 (approx.) bartlett-box f = 0.269, p = 0.764 maximum variance / minimum variance = 1.9956 model ii - components of variance. estimate of between component 5.114704E+01 expected value for ierr is 0 returned value for ierr is 0 storage from aov1 tagvalue groupsize groupmean groupsd 11.000000000000 6.0000000000000 78.166664123535 3.8686778545380 11.500000000000 5.0000000000000 64.000000000000 3.0000000000000 12.000000000000 5.0000000000000 74.000000000000 2.7386128902435 printout supressed. expected value for ierr is 0 returned value for ierr is 0 storage from aov1 tagvalue groupsize groupmean groupsd 11.000000000000 6.0000000000000 78.166664123535 3.8686780929565 11.500000000000 5.0000000000000 64.000000000000 3.0000000000000 12.000000000000 5.0000000000000 74.000000000000 2.7386128902435 1****number of observations less than 2**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1 ------------------------------------- the input value of n is 1. the value of the argument n must be greater than or equal to two . the correct form of the call statement is call aov1 (y, tag, n, ldstak) expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1s ------------------------------------- the input value of n is -14. the value of the argument n must be greater than or equal to two . the correct form of the call statement is call aov1s (y, tag, n, ldstak, nprt, gstat, igstat, ng) expected value for ierr is 1 returned value for ierr is 1 1****all observations with same value**** test of aov1 starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 0 source d.f. sum of squares mean squares f ratio f prob. between groups 1 0.000000E+00 0.000000E+00 0.100E+01 1.000 within groups 8 0.000000E+00 0.000000E+00 total 9 0.000000E+00 kruskal-wallis rank test for difference between group means * h = 0.000E+00, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.150000E+01 5 0.00000E+00+ 0.00000E+00+ 0.00000E+00 0.00000E+00 0.00000E+00 27.5 0.00000E+00 to 0.00000E+00 1.200000E+01 5 0.00000E+00+ 0.00000E+00+ 0.00000E+00 0.00000E+00 0.00000E+00 27.5 0.00000E+00 to 0.00000E+00 total 10 0.00000E+00 0.00000E+00 0.00000E+00 fixed effects model 0.00000E+00 0.00000E+00 0.00000E+00 to 0.00000E+00 random effects model 0.00000E+00 0.00000E+00 0.00000E+00 to 0.00000E+00 ungrouped data 0.00000E+00 0.00000E+00 0.00000E+00 to 0.00000E+00 tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 1.0000, p = 1.000 (approx.) bartlett-box f = 0.000, p = 1.000 maximum variance / minimum variance = 1.0000 model ii - components of variance. estimate of between component 0.000000E+00 expected value for ierr is 0 returned value for ierr is 0 1****all observations with same value**** test of aov1s starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 0 source d.f. sum of squares mean squares f ratio f prob. between groups 1 0.000000E+00 0.000000E+00 0.100E+01 1.000 within groups 8 0.000000E+00 0.000000E+00 total 9 0.000000E+00 kruskal-wallis rank test for difference between group means * h = 0.000E+00, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.150000E+01 5 2.00000E+00+ 0.00000E+00+ 0.00000E+00 2.00000E+00 2.00000E+00 27.5 2.00000E+00 to 2.00000E+00 1.200000E+01 5 2.00000E+00+ 0.00000E+00+ 0.00000E+00 2.00000E+00 2.00000E+00 27.5 2.00000E+00 to 2.00000E+00 total 10 2.00000E+00 2.00000E+00 2.00000E+00 fixed effects model 0.00000E+00 0.00000E+00 2.00000E+00 to 2.00000E+00 random effects model 0.00000E+00 0.00000E+00 2.00000E+00 to 2.00000E+00 ungrouped data 0.00000E+00 0.00000E+00 2.00000E+00 to 2.00000E+00 tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 1.0000, p = 1.000 (approx.) bartlett-box f = 0.000, p = 1.000 maximum variance / minimum variance = 1.0000 model ii - components of variance. estimate of between component 0.000000E+00 expected value for ierr is 0 returned value for ierr is 0 1****test with insufficient work area**** test of aov1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1 ------------------------------------- the input value of ldstak is 1. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 15. note. the value of ldstak mentioned above is the minimum necessary to continue checking for errors and to calculate the correct value of ldstak. the correct value will be larger. consult the documentation for the formulas used to calculate ldstak. the correct form of the call statement is call aov1 (y, tag, n, ldstak) expected value for ierr is 1 returned value for ierr is 1 1****test with insufficient work area**** test of aov1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1 ------------------------------------- the input value of ldstak is 57. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 58. the correct form of the call statement is call aov1 (y, tag, n, ldstak) expected value for ierr is 1 returned value for ierr is 1 check to see if tags have been changed 11.500000 11.500000 11.500000 11.500000 11.500000 12.000000 12.000000 12.000000 12.000000 12.000000 11.000000 11.000000 11.000000 11.000000 11.000000 -11.000000 11.000000 1****test with exactly the right amount of work area**** test of aov1 starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 1 source d.f. sum of squares mean squares f ratio f prob. between groups 2 5.651040E+02 2.825520E+02 0.261E+02 0.000 slope 1 6.450886E+01 6.450886E+01 0.141E+01 0.257 devs. about line 1 5.005952E+02 5.005952E+02 0.462E+02 0.000 within groups 13 1.408333E+02 1.083333E+01 total 15 7.059375E+02 kruskal-wallis rank test for difference between group means * h = 0.114E+02, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.100000E+01 6 7.81667E+01+ 3.86868E+00+ 1.57938E+00 7.20000E+01 8.30000E+01 76.0 7.41067E+01 to 8.22266E+01 1.150000E+01 5 6.40000E+01- 3.00000E+00 1.34164E+00 6.10000E+01 6.70000E+01 15.0 6.02750E+01 to 6.77250E+01 1.200000E+01 5 7.40000E+01 2.73861E+00- 1.22474E+00 7.10000E+01 7.80000E+01 45.0 7.05996E+01 to 7.74004E+01 total 16 7.24375E+01 6.10000E+01 8.30000E+01 fixed effects model 3.29140E+00 8.22851E-01 7.06594E+01 to 7.42156E+01 random effects model 7.29576E+00 4.21221E+00 5.43138E+01 to 9.05612E+01 ungrouped data 6.86021E+00 1.71505E+00 6.87815E+01 to 7.60935E+01 pairwise multiple comparison of means. the means hare put in increasing order in groups separated by *****. a mean is adjudged non-significantly different from any mean in the same group and significantly different at the .05 level from any mean in another group. ***** ***** indicates adjacent groups have no common mean. newman-keuls technique, hartley modification. (approximate if group numbers are unequal.) 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, scheffe technique. 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 0.4756, p = 0.395 (approx.) bartlett-box f = 0.269, p = 0.764 maximum variance / minimum variance = 1.9956 model ii - components of variance. estimate of between component 5.114704E+01 expected value for ierr is 0 returned value for ierr is 0 1****test with insufficient work area**** test of aov1s starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1s ------------------------------------- the input value of ldstak is 1. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 15. note. the value of ldstak mentioned above is the minimum necessary to continue checking for errors and to calculate the correct value of ldstak. the correct value will be larger. consult the documentation for the formulas used to calculate ldstak. the correct form of the call statement is call aov1s (y, tag, n, ldstak, nprt, gstat, igstat, ng) expected value for ierr is 1 returned value for ierr is 1 1****test with insufficient work area**** test of aov1s starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1s ------------------------------------- the input value of ldstak is 51. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 52. the correct form of the call statement is call aov1s (y, tag, n, ldstak, nprt, gstat, igstat, ng) expected value for ierr is 1 returned value for ierr is 1 check to see if tags have been changed 11.500000 11.500000 11.500000 11.500000 11.500000 12.000000 12.000000 12.000000 12.000000 12.000000 11.000000 11.000000 11.000000 11.000000 11.000000 -11.000000 11.000000 1****test with exactly the right amount of work area**** test of aov1s starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 1 source d.f. sum of squares mean squares f ratio f prob. between groups 2 5.651040E+02 2.825520E+02 0.261E+02 0.000 slope 1 6.450886E+01 6.450886E+01 0.141E+01 0.257 devs. about line 1 5.005952E+02 5.005952E+02 0.462E+02 0.000 within groups 13 1.408333E+02 1.083333E+01 total 15 7.059375E+02 kruskal-wallis rank test for difference between group means * h = 0.114E+02, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.100000E+01 6 7.81667E+01+ 3.86868E+00+ 1.57938E+00 7.20000E+01 8.30000E+01 76.0 7.41067E+01 to 8.22266E+01 1.150000E+01 5 6.40000E+01- 3.00000E+00 1.34164E+00 6.10000E+01 6.70000E+01 15.0 6.02750E+01 to 6.77250E+01 1.200000E+01 5 7.40000E+01 2.73861E+00- 1.22474E+00 7.10000E+01 7.80000E+01 45.0 7.05996E+01 to 7.74004E+01 total 16 7.24375E+01 6.10000E+01 8.30000E+01 fixed effects model 3.29140E+00 8.22851E-01 7.06594E+01 to 7.42156E+01 random effects model 7.29576E+00 4.21221E+00 5.43138E+01 to 9.05612E+01 ungrouped data 6.86021E+00 1.71505E+00 6.87815E+01 to 7.60935E+01 pairwise multiple comparison of means. the means hare put in increasing order in groups separated by *****. a mean is adjudged non-significantly different from any mean in the same group and significantly different at the .05 level from any mean in another group. ***** ***** indicates adjacent groups have no common mean. newman-keuls technique, hartley modification. (approximate if group numbers are unequal.) 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, scheffe technique. 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 0.4756, p = 0.395 (approx.) bartlett-box f = 0.269, p = 0.764 maximum variance / minimum variance = 1.9956 model ii - components of variance. estimate of between component 5.114704E+01 expected value for ierr is 0 returned value for ierr is 0 1****same number of groups as non-zero tags**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1 ------------------------------------- the number of distinct groups (ng) must be between two and one less than the number of positive tag values. the correct form of the call statement is call aov1 (y, tag, n, ldstak) expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1s ------------------------------------- the number of distinct groups (ng) must be between two and one less than the number of positive tag values. the correct form of the call statement is call aov1s (y, tag, n, ldstak, nprt, gstat, igstat, ng) expected value for ierr is 1 returned value for ierr is 1 ****less than 2 different tag groups**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1 ------------------------------------- the number of distinct groups (ng) must be between two and one less than the number of positive tag values. the correct form of the call statement is call aov1 (y, tag, n, ldstak) expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1s ------------------------------------- the number of distinct groups (ng) must be between two and one less than the number of positive tag values. the correct form of the call statement is call aov1s (y, tag, n, ldstak, nprt, gstat, igstat, ng) expected value for ierr is 1 returned value for ierr is 1 ****less than 2 tags**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1 ------------------------------------- the number of values in vector tag greater than zero is 9. there must be at least 2 values in vector tag greater than or equal to zero . the correct form of the call statement is call aov1 (y, tag, n, ldstak) expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1s ------------------------------------- the number of values in vector tag greater than zero is 9. there must be at least 2 values in vector tag greater than or equal to zero . the correct form of the call statement is call aov1s (y, tag, n, ldstak, nprt, gstat, igstat, ng) expected value for ierr is 1 returned value for ierr is 1 1****incorrect dimension of gstat**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine aov1s ------------------------------------- the input value of igstat is 2. the first dimension of gstat , as indicated by the argument igstat, must be greater than or equal to ng . the correct form of the call statement is call aov1s (y, tag, n, ldstak, nprt, gstat, igstat, ng) expected value for ierr is 1 returned value for ierr is 1 1****all observations within a group same value**** starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 0 source d.f. sum of squares mean squares f ratio f prob. between groups 3 1.466100E+03 4.887001E+02 0.340E+39 0.000 slope 1 6.433140E+01 6.433140E+01 0.367E+00 0.567 devs. about line 2 1.401769E+03 7.008844E+02 0.340E+39 0.000 within groups 6 0.000000E+00 0.000000E+00 total 9 1.466100E+03 kruskal-wallis rank test for difference between group means * h = 0.900E+01, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.000000E+00 2 5.30000E+01- 0.00000E+00+ 0.00000E+00 5.30000E+01 5.30000E+01 3.0 5.30000E+01 to 5.30000E+01 2.000000E+00 2 8.90000E+01+ 0.00000E+00+ 0.00000E+00 8.90000E+01 8.90000E+01 19.0 8.90000E+01 to 8.90000E+01 3.000000E+00 3 6.20000E+01 0.00000E+00+ 0.00000E+00 6.20000E+01 6.20000E+01 12.0 6.20000E+01 to 6.20000E+01 4.000000E+00 3 7.10000E+01 0.00000E+00+ 0.00000E+00 7.10000E+01 7.10000E+01 21.0 7.10000E+01 to 7.10000E+01 total 10 6.83000E+01 5.30000E+01 8.90000E+01 fixed effects model 0.00000E+00 0.00000E+00 6.83000E+01 to 6.83000E+01 random effects model 1.53792E+01 7.68960E+00 4.38282E+01 to 9.27718E+01 ungrouped data 1.27632E+01 4.03609E+00 5.91610E+01 to 7.74390E+01 pairwise multiple comparison of means. the means hare put in increasing order in groups separated by *****. a mean is adjudged non-significantly different from any mean in the same group and significantly different at the .05 level from any mean in another group. ***** ***** indicates adjacent groups have no common mean. newman-keuls technique, hartley modification. (approximate if group numbers are unequal.) 5.30000E+01, ***** ***** 6.20000E+01, ***** ***** 7.10000E+01, ***** ***** 8.90000E+01, scheffe technique. 5.30000E+01, ***** ***** 6.20000E+01, ***** ***** 7.10000E+01, ***** ***** 8.90000E+01, tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 1.0000, p = 1.000 (approx.) bartlett-box f = 0.000, p = 1.000 maximum variance / minimum variance = 1.0000 model ii - components of variance. estimate of between component 1.981216E+02 expected value for ierr is 0 returned value for ierr is 0 1****test with 2 tags**** starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 0 source d.f. sum of squares mean squares f ratio f prob. between groups 1 5.400000E+01 5.400000E+01 0.340E+39 0.000 within groups 1 0.000000E+00 0.000000E+00 total 2 5.400000E+01 kruskal-wallis rank test for difference between group means * h = 0.200E+01, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.000000E+00 2 5.30000E+01- 0.00000E+00+ 0.00000E+00 5.30000E+01 5.30000E+01 3.0 5.30000E+01 to 5.30000E+01 3.000000E+00 1 6.20000E+01+ estimate not available 6.20000E+01 6.20000E+01 3.0 ********** to ********** total 3 5.60000E+01 5.30000E+01 6.20000E+01 fixed effects model 0.00000E+00 0.00000E+00 5.60000E+01 to 5.60000E+01 random effects model 6.70820E+00 4.74342E+00 -4.27086E+00 to 1.16271E+02 ungrouped data 5.19615E+00 3.00000E+00 4.30920E+01 to 6.89080E+01 scheffe technique. 5.30000E+01, ***** ***** 6.20000E+01, expected value for ierr is 0 returned value for ierr is 0 1****all groups except for 1 with 1 observation **** starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 0 source d.f. sum of squares mean squares f ratio f prob. between groups 3 9.072000E+02 3.024000E+02 0.340E+39 0.000 slope 1 1.543765E+02 1.543765E+02 0.615E+00 0.577 devs. about line 2 7.528235E+02 3.764117E+02 0.340E+39 0.000 within groups 1 0.000000E+00 0.000000E+00 total 4 9.072000E+02 kruskal-wallis rank test for difference between group means * h = 0.400E+01, f prob = 0.001 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.000000E+00 2 5.30000E+01- 0.00000E+00+ 0.00000E+00 5.30000E+01 5.30000E+01 3.0 5.30000E+01 to 5.30000E+01 2.000000E+00 1 8.90000E+01+ estimate not available 8.90000E+01 8.90000E+01 5.0 ********** to ********** 3.000000E+00 1 6.20000E+01 estimate not available 6.20000E+01 6.20000E+01 3.0 ********** to ********** 4.000000E+00 1 7.10000E+01 estimate not available 7.10000E+01 7.10000E+01 4.0 ********** to ********** total 5 6.56000E+01 5.30000E+01 8.90000E+01 fixed effects model 0.00000E+00 0.00000E+00 6.56000E+01 to 6.56000E+01 random effects model 1.57949E+01 7.89747E+00 4.04667E+01 to 9.07333E+01 ungrouped data 1.50599E+01 6.73498E+00 4.69007E+01 to 8.42993E+01 scheffe technique. 5.30000E+01, ***** ***** 6.20000E+01, ***** ***** 7.10000E+01, ***** ***** 8.90000E+01, expected value for ierr is 0 returned value for ierr is 0 1check error handling - test 1 test of bfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call bfs (y1, y2, n) ierr is 1 test of bfss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfss ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the input value of icspc2 is -10. the first dimension of cspc2 , as indicated by the argument icspc2, must be greater than or equal to nf . the input value of iphas is -10. the first dimension of phas , as indicated by the argument iphas , must be greater than or equal to nf . the correct form of the call statement is call bfss (y1, y2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsf ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call bfsf (yfft1, yfft2, n, lyfft, ldstak) ierr is 1 test of bfsfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsfs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the input value of icspc2 is -10. the first dimension of cspc2 , as indicated by the argument icspc2, must be greater than or equal to nf . the input value of iphas is -10. the first dimension of phas , as indicated by the argument iphas , must be greater than or equal to nf . the correct form of the call statement is call bfsfs (yfft1, yfft2, n, lyfft, ldstak, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq) ierr is 1 Test of BFSM starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsm ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call bfsm (y1, ymiss1, y2, ymiss2, n) ierr is 1 test of bfsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsms ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the input value of icspc2 is -10. the first dimension of cspc2 , as indicated by the argument icspc2, must be greater than or equal to nf . the input value of iphas is -10. the first dimension of phas , as indicated by the argument iphas , must be greater than or equal to nf . the correct form of the call statement is call bfsms (y1, ymiss1, y2, ymiss2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsv ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of jccov is 0. the second dimension of ccov , as indicated by the argument jccov , must be greater than or equal to m . the correct form of the call statement is call bfsv(ccov, index1, index2, n, lagmax, iccov, jccov) ierr is 1 test of bfsvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsvs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of jccov is 0. the second dimension of ccov , as indicated by the argument jccov , must be greater than or equal to m . the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the input value of icspc2 is -10. the first dimension of cspc2 , as indicated by the argument icspc2, must be greater than or equal to nf . the input value of iphas is -10. the first dimension of phas , as indicated by the argument iphas , must be greater than or equal to nf . the correct form of the call statement is call bfsvs(ccov, index1, index2, n, iccov, jccov, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsmv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsmv ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of jccov is 0. the second dimension of ccov , as indicated by the argument jccov , must be greater than or equal to m . the input value of jnlppc is 0. the second dimension of nlppc , as indicated by the argument jnlppc, must be greater than or equal to m . the correct form of the call statement is call bfsmv (ccov, nlppc, index1, index2, n, lagmax, + iccov, jccov, inlppc, jnlppc) ierr is 1 test of bfsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsmvs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of jccov is 0. the second dimension of ccov , as indicated by the argument jccov , must be greater than or equal to m . the input value of jnlppc is 0. the second dimension of nlppc , as indicated by the argument jnlppc, must be greater than or equal to m . the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the input value of icspc2 is -10. the first dimension of cspc2 , as indicated by the argument icspc2, must be greater than or equal to nf . the input value of iphas is -10. the first dimension of phas , as indicated by the argument iphas , must be greater than or equal to nf . the correct form of the call statement is call bfsmvs(ccov, nlppc, index1, index2, n, + iccov, jccov, inlppc, jnlppc, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 1check error handling - test 2 test of bfss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfss ------------------------------------- the number of values in vector lags outside the range 1 to n-1 , inclusive, is 2. the values in the vector lags must all be within this range. the correct form of the call statement is call bfss (y1, y2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsf ------------------------------------- the input value of lyfft is -11. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 138. the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 76. the correct form of the call statement is call bfsf (yfft1, yfft2, n, lyfft, ldstak) ierr is 1 test of bfsfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsfs ------------------------------------- the input value of lyfft is -11. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 202. the number of values in vector lags outside the range 1 to n-1 , inclusive, is 2. the values in the vector lags must all be within this range. the correct form of the call statement is call bfsfs (yfft1, yfft2, n, lyfft, ldstak, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq) ierr is 1 test of bfsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsms ------------------------------------- the number of values in vector lags outside the range 1 to n-1 , inclusive, is 2. the values in the vector lags must all be within this range. the correct form of the call statement is call bfsms (y1, ymiss1, y2, ymiss2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsvs ------------------------------------- the input value of lagmax is 100. the value of the argument lagmax must be between 1 and n-1 , inclusive. the input value of iccov is 0. the first dimension of ccov , as indicated by the argument iccov , must be greater than or equal to lagmax+1. the input value of jccov is 0. the second dimension of ccov , as indicated by the argument jccov , must be greater than or equal to m . the number of values in vector lags outside the range 1 to lagmax , inclusive, is 1. the values in the vector lags must all be within this range. the correct form of the call statement is call bfsvs(ccov, index1, index2, n, iccov, jccov, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsmvs ------------------------------------- the input value of lagmax is 100. the value of the argument lagmax must be between 1 and n-1 , inclusive. the input value of iccov is 0. the first dimension of ccov , as indicated by the argument iccov , must be greater than or equal to lagmax+1. the input value of jccov is 0. the second dimension of ccov , as indicated by the argument jccov , must be greater than or equal to m . the input value of inlppc is 0. the first dimension of nlppc , as indicated by the argument inlppc, must be greater than or equal to lagmax+1. the input value of jnlppc is 0. the second dimension of nlppc , as indicated by the argument jnlppc, must be greater than or equal to m . the number of values in vector lags outside the range 1 to lagmax , inclusive, is 1. the values in the vector lags must all be within this range. the correct form of the call statement is call bfsmvs(ccov, nlppc, index1, index2, n, + iccov, jccov, inlppc, jnlppc, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 1lds too small test of bfss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfss ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 235. the correct form of the call statement is call bfss (y1, y2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsf ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 76. the correct form of the call statement is call bfsf (yfft1, yfft2, n, lyfft, ldstak) ierr is 1 test of bfsfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsfs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 287. the correct form of the call statement is call bfsfs (yfft1, yfft2, n, lyfft, ldstak, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq) ierr is 1 test of bfsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsms ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 296. the correct form of the call statement is call bfsms (y1, ymiss1, y2, ymiss2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsvs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 225. the correct form of the call statement is call bfsvs(ccov, index1, index2, n, iccov, jccov, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsmvs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 259. the correct form of the call statement is call bfsmvs(ccov, nlppc, index1, index2, n, + iccov, jccov, inlppc, jnlppc, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 1all data and covariances missing Test of BFSM starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsm ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call bfsm (y1, ymiss1, y2, ymiss2, n) ierr is 1 test of bfsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsms ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call bfsms (y1, ymiss1, y2, ymiss2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 test of bfsmv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsmv ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call bfsmv (ccov, nlppc, index1, index2, n, lagmax, + iccov, jccov, inlppc, jnlppc) ierr is 1 test of bfsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsmvs ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call bfsmvs(ccov, nlppc, index1, index2, n, + iccov, jccov, inlppc, jnlppc, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 Every other data value missing. Test of BFSM LACOV = 101 LAGMAX = 33 N = 100 LACOV = 101 LAGMAX = 33 N = 100 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsm ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call bfsm (y1, ymiss1, y2, ymiss2, n) ierr is 1 test of bfsms LACOV = 17 LAGMAX = 16 N = 100 LACOV = 17 LAGMAX = 16 N = 100 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine bfsms ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call bfsms (y1, ymiss1, y2, ymiss2, n, + nw, lags, nf, fmin, fmax, nprt, + cspc2, icspc2, phas, iphas, freq, ldstak) ierr is 1 1valid problem test of bfs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i ............ i i ... .... i i ... .. i i .. .. i 0.5000 - .. .. - i .. .. i i .. ++++++++++++ . i i .. +++ +++ . i i . +++ +++ .. i 0.4000 - .. ++ ++ . - i . ++ + . i i . + ++ .. i i .. ++ ........ + . i i . ++ .... .... ++ . i 0.3000 - .. + .. ... + . - i . ++ ... .. + . i i . + .. . ++ . i i .. ++ .. .. + .. i i . + . .. + . i 0.2000 - .. ++ .. . ++ . - i . + .. .. + . i i . ++ .. . + . i i + . .. ++ i i ++ .. . + i 0.1000 - ++ .. .. ++ - i ++ .. .. + i i -----------22-------22------------------------------------------------------------22-----22---------- i i +++ ... .. +++ i i +++++ .... ... +++ i 0.0000 - +++ . . ++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i ++++++ ++++ i i +++ ++ i 3.7699 - + + - i + + i i ++ + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i ++++++ ++++ i i +++ ++ i -2.5133 - + + - i + + i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i .............. i i ... ... i i .. ... i i . ... i 0.7000 - . .. - i . +++++++++ .. i i . +++ +++ . i i . ++ +++ .. i i . + ++ . i 0.6000 - . + ++ . - i + ++ .. i i . + ++ . i i . + ..... + . i i + .... ... + . i 0.5000 - . . ... ++ . - i + .. .. + . i i . + . .. + . i i . .. + . i i . + . . + . i 0.4000 - + . .. + . - i . . + . i i . + . . + .. i i . . + . i i . + .. + . i 0.3000 - . . + . - i + . . + i i . + i i + . . + i i . . + i 0.2000 - + . + - i + . . ++ i i . + i i + . .. + i i -----------2------------------------------------------------------2--------22------------------------ i 0.1000 - + . ++ - i + . .. ++ i i + . . +++ i i + . ... +++ i i + . .. ++++++ i 0.0000 - ++ ++++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i +++++++++++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ ++++++++ i i ++++++ i 3.7699 - + ++++ - i ++ i i + ++ i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i +++++++++++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ ++++++++ i i ++++++ i -2.5133 - + ++++ - i ++ i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i .. i i .... ..... i i .. .. i i . ... i 0.8000 - . ............... - i . .. i i . ++++++ . i i ++ ++ . i i . + ++ . i 0.7000 - + ++ . - i . + + . i i +++ ++++++ . i i . + +++++ ++ . i i + + .. i 0.6000 - + . - i . ...... + .. i i + . .. . i i . . + . i i . + . . + .. i 0.5000 - . . + . - i . i i + . .. + i i . ..... + i i . ....... . + i 0.4000 - + . + - i . . + i i . + i i + . + i i . + i 0.3000 - . ++ - i . + i i + . + i i -----------------------------------------------------2------------22--------------------------------- i i . . ++ i 0.2000 - + +++++ - i . ++++ i i . . ++ i i + .. + i i . + i 0.1000 - . .. + - i + . ++ i i . .. + i i + .. ++ ++ i i + +++ ++++ i 0.0000 - + +++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - + ++++++++ - i ++++ +++++++ +++++++ +++++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i +++ i i +++ i 3.7699 - +++ - i ++ i i ++ i i + i i ++ i 2.5133 - + - i i i + i i i i + i 1.2566 - + - i i i + i i ++ i i + i 0.0000 - 2 +2 - i i i i i i i i -1.2566 - + ++++++++ - i ++++ +++++++ +++++++ +++++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i +++ i i +++ i -2.5133 - +++ - i ++ i i ++ i i + i i ++ i -3.7699 - + - i i i + i i i i + i -5.0265 - + - i i i + i i ++ i i + i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i ........ .... i 0.9000 - .. .. . . - i ....... . . i i . . . i i . . i i . +++++ . ++ i 0.8000 - + ++ .. . + . - i . + + ... + ... i i + + .. i i + + + . i i +++++ + i 0.7000 - + + - i + i i + + i i + i i + + i 0.6000 - + ... - i . . + + .. + i i . . . i i . + + . + i i . . ++ + i 0.5000 - + ++ - i . . + i i ----------------------------2----------------2--------2---------------------------------------------- i i . i i ... . + + i 0.4000 - + . . . + - i . + + i i . + + + + i i . . + + + i i + + i 0.3000 - . + - i + . . +++ + + i i i i . + + i i . . . + i 0.2000 - . + + - i + . . . + i i . . .. + + i i .. ++ ++ ++++ i i ++ i 0.1000 - + . + + - i + i i + + i i + + i i ++ ++ i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i + i i + ++++ i 5.0265 - + ++ +++++++++++ + - i ++++ +++ +++ +++ ++ i i ++++++++++++++++++++++ +++++++++++++ + i i +++++ i i +++ i 3.7699 - ++ - i + i i + i i + i i +++ i 2.5133 - ++ - i + i i i i + i i i 1.2566 - - i + i i i i ++ i i +++ i 0.0000 - 2 +2 - i i i i i + i i + ++++ i -1.2566 - + ++ +++++++++++ + - i ++++ +++ +++ +++ ++ i i ++++++++++++++++++++++ +++++++++++++ + i i +++++ i i +++ i -2.5133 - ++ - i + i i + i i + i i +++ i -3.7699 - ++ - i + i i i i + i i i -5.0265 - - i + i i i i ++ i i +++ i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of bfss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i . . . . i i . i i . i i . i 0.7000 - . - i + + . i i + + i i + . i i . + i 0.6000 - - i + . i i + i i . + i i . . i 0.5000 - . . . - i . + i i + . i i i i . . i 0.4000 - . + - i i i . . . i i i i + + i 0.3000 - . - i . i i i i + i i . i 0.2000 - - i + + i i i i . . i i - - - - - - - - - - - - - - - - - - - 2 - - - - - - i 0.1000 - - i . + i i + i i . . + i i + + i 0.0000 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i + + + + i i + + + + + + + + + + + + + + + i i + + i i + + i 3.7699 - + - i i i + + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i + + + + i i + + + + + + + + + + + + + + + i i + + i i + + i -2.5133 - + - i i i i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i . . i i . . i i . i 0.8000 - . . . - i . . i i + + i i i i + + . i 0.7000 - - i i i + + + i i . + . i i + + i 0.6000 - - i . . . i i i i i i . . + . i 0.5000 - - i i i . i i . i i . . + i 0.4000 - + - i . . i i i i + i i i 0.3000 - - i . + i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i i + i 0.2000 - + - i + i i . . + i i + i i i 0.1000 - . - i + i i . i i + + i i + i 0.0000 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - + + - i + + + + + + i i + + + + + + + + + + i i + i i + i 3.7699 - - i + i i i i + i i i 2.5133 - - i i i + i i i i i 1.2566 - - i i i i i + i i i 0.0000 - 2 2 - i i i i i i i i -1.2566 - + + - i + + + + + + i i + + + + + + + + + + i i + i i + i -2.5133 - - i + i i i i + i i i -3.7699 - - i i i + i i i i i -5.0265 - - i i i i i + i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.0000000E+00 0.4457461E-02 -0.3141593E+01 0.6729817E-03 -0.0000000E+00 0.2000000E-01 0.5116763E-01 -0.1880425E+01 0.1370533E+00 -0.1583274E+01 0.4000000E-01 0.1714792E+00 -0.1751850E+01 0.4041626E+00 -0.1677583E+01 0.6000000E-01 0.3210856E+00 -0.1715397E+01 0.6116751E+00 -0.1721943E+01 0.8000000E-01 0.4593221E+00 -0.1700990E+01 0.7198188E+00 -0.1733138E+01 0.9999999E-01 0.5644947E+00 -0.1693569E+01 0.7597581E+00 -0.1721767E+01 0.1200000E+00 0.6323792E+00 -0.1688077E+01 0.7552606E+00 -0.1694870E+01 0.1400000E+00 0.6681482E+00 -0.1682582E+01 0.7182088E+00 -0.1656570E+01 0.1600000E+00 0.6796163E+00 -0.1676438E+01 0.6688998E+00 -0.1622068E+01 0.1800000E+00 0.6736777E+00 -0.1669517E+01 0.6413577E+00 -0.1624215E+01 0.2000000E+00 0.6550170E+00 -0.1661722E+01 0.6500628E+00 -0.1668285E+01 0.2200000E+00 0.6259545E+00 -0.1652669E+01 0.6595039E+00 -0.1709816E+01 0.2400000E+00 0.5868149E+00 -0.1641694E+01 0.6188422E+00 -0.1704008E+01 0.2600000E+00 0.5367091E+00 -0.1628447E+01 0.5227825E+00 -0.1623383E+01 0.2800000E+00 0.4748101E+00 -0.1614229E+01 0.4202060E+00 -0.1471867E+01 0.3000000E+00 0.4019890E+00 -0.1603764E+01 0.3430119E+00 -0.1326809E+01 0.3200000E+00 0.3221993E+00 -0.1606273E+01 0.2753133E+00 -0.1297512E+01 0.3400000E+00 0.2426298E+00 -0.1634061E+01 0.2203143E+00 -0.1436010E+01 0.3600000E+00 0.1716811E+00 -0.1697574E+01 0.1955238E+00 -0.1705994E+01 0.3800000E+00 0.1151532E+00 -0.1798537E+01 0.1863182E+00 -0.2019650E+01 0.4000000E+00 0.7375210E-01 -0.1926218E+01 0.1528519E+00 -0.2350410E+01 0.4200000E+00 0.4458512E-01 -0.2062737E+01 0.8994668E-01 -0.2737313E+01 0.4400001E+00 0.2454270E-01 -0.2197546E+01 0.3516284E-01 0.3038836E+01 0.4600001E+00 0.1180289E-01 -0.2349163E+01 0.8721530E-02 0.2035405E+01 0.4800001E+00 0.5096353E-02 -0.2609713E+01 0.1645163E-01 0.5731075E+00 0.5000000E+00 0.3084440E-02 -0.3141593E+01 0.3135666E-01 0.0000000E+00 1valid problem test of bfsf starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i ............ i i ... .... i i ... .. i i .. .. i 0.5000 - .. .. - i .. .. i i .. ++++++++++++ . i i .. +++ +++ . i i . +++ +++ .. i 0.4000 - .. ++ ++ . - i . ++ + . i i . + ++ .. i i .. ++ ........ + . i i . ++ .... .... ++ . i 0.3000 - .. + .. ... + . - i . ++ ... .. + . i i . + .. . ++ . i i .. ++ .. .. + .. i i . + . .. + . i 0.2000 - .. ++ .. . ++ . - i . + .. .. + . i i . ++ .. . + . i i + . .. ++ i i ++ .. . + i 0.1000 - ++ .. .. ++ - i ++ .. .. + i i -----------22-------22------------------------------------------------------------22-----22---------- i i +++ ... .. +++ i i +++++ .... ... +++ i 0.0000 - +++ . . ++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i ++++++ ++++ i i +++ ++ i 3.7699 - + + - i + + i i ++ + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i ++++++ ++++ i i +++ ++ i -2.5133 - + + - i + + i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i .............. i i ... ... i i .. ... i i . ... i 0.7000 - . .. - i . +++++++++ .. i i . +++ +++ . i i . ++ +++ .. i i . + ++ . i 0.6000 - . + ++ . - i + ++ .. i i . + ++ . i i . + ..... + . i i + .... ... + . i 0.5000 - . . ... ++ . - i + .. .. + . i i . + . .. + . i i . .. + . i i . + . . + . i 0.4000 - + . .. + . - i . . + . i i . + . . + .. i i . . + . i i . + .. + . i 0.3000 - . . + . - i + . . + i i . + i i + . . + i i . . + i 0.2000 - + . + - i + . . ++ i i . + i i + . .. + i i -----------2------------------------------------------------------2--------22------------------------ i 0.1000 - + . ++ - i + . .. ++ i i + . . +++ i i + . ... +++ i i + . .. ++++++ i 0.0000 - ++ ++++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i +++++++++++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ ++++++++ i i ++++++ i 3.7699 - + ++++ - i ++ i i + ++ i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i +++++++++++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ ++++++++ i i ++++++ i -2.5133 - + ++++ - i ++ i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i .. i i .... ..... i i .. .. i i . ... i 0.8000 - . ............... - i . .. i i . ++++++ . i i ++ ++ . i i . + ++ . i 0.7000 - + ++ . - i . + + . i i +++ ++++++ . i i . + +++++ ++ . i i + + .. i 0.6000 - + . - i . ...... + .. i i + . .. . i i . . + . i i . + . . + .. i 0.5000 - . . + . - i . i i + . .. + i i . ..... + i i . ....... . + i 0.4000 - + . + - i . . + i i . + i i + . + i i . + i 0.3000 - . ++ - i . + i i + . + i i -----------------------------------------------------2------------22--------------------------------- i i . . ++ i 0.2000 - + +++++ - i . ++++ i i . . ++ i i + .. + i i . + i 0.1000 - . .. + - i + . ++ i i . .. + i i + .. ++ ++ i i + +++ ++++ i 0.0000 - + +++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - + ++++++++ - i ++++ +++++++ +++++++ +++++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i +++ i i +++ i 3.7699 - +++ - i ++ i i ++ i i + i i ++ i 2.5133 - + - i i i + i i i i + i 1.2566 - + - i i i + i i ++ i i + i 0.0000 - 2 +2 - i i i i i i i i -1.2566 - + ++++++++ - i ++++ +++++++ +++++++ +++++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i +++ i i +++ i -2.5133 - +++ - i ++ i i ++ i i + i i ++ i -3.7699 - + - i i i + i i i i + i -5.0265 - + - i i i + i i ++ i i + i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i ........ .... i 0.9000 - .. .. . . - i ....... . . i i . . . i i . . i i . +++++ . ++ i 0.8000 - + ++ .. . + . - i . + + ... + ... i i + + .. i i + + + . i i +++++ + i 0.7000 - + + - i + i i + + i i + i i + + i 0.6000 - + ... - i . . + + .. + i i . . . i i . + + . + i i . . ++ + i 0.5000 - + ++ - i . . + i i ----------------------------2----------------2--------2---------------------------------------------- i i . i i ... . + + i 0.4000 - + . . . + - i . + + i i . + + + + i i . . + + + i i + + i 0.3000 - . + - i + . . +++ + + i i i i . + + i i . . . + i 0.2000 - . + + - i + . . . + i i . . .. + + i i .. ++ ++ ++++ i i ++ i 0.1000 - + . + + - i + i i + + i i + + i i ++ ++ i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i + i i + ++++ i 5.0265 - + ++ +++++++++++ + - i ++++ +++ +++ +++ ++ i i ++++++++++++++++++++++ +++++++++++++ + i i +++++ i i +++ i 3.7699 - ++ - i + i i + i i + i i +++ i 2.5133 - ++ - i + i i i i + i i i 1.2566 - - i + i i i i ++ i i +++ i 0.0000 - 2 +2 - i i i i i + i i + ++++ i -1.2566 - + ++ +++++++++++ + - i ++++ +++ +++ +++ ++ i i ++++++++++++++++++++++ +++++++++++++ + i i +++++ i i +++ i -2.5133 - ++ - i + i i + i i + i i +++ i -3.7699 - ++ - i + i i i i + i i i -5.0265 - - i + i i i i ++ i i +++ i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of bfsfs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i . . . . i i . i i . i i . i 0.7000 - . - i + + . i i + + i i + . i i . + i 0.6000 - - i + . i i + i i . + i i . . i 0.5000 - . . . - i . + i i + . i i i i . . i 0.4000 - . + - i i i . . . i i i i + + i 0.3000 - . - i . i i i i + i i . i 0.2000 - - i + + i i i i . . i i - - - - - - - - - - - - - - - - - - - 2 - - - - - - i 0.1000 - - i . + i i + i i . . + i i + + i 0.0000 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i + + + + i i + + + + + + + + + + + + + + + i i + + i i + + i 3.7699 - + - i i i + + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i + + + + i i + + + + + + + + + + + + + + + i i + + i i + + i -2.5133 - + - i i i i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i . . i i . . i i . i 0.8000 - . . . - i . . i i + + i i i i + + . i 0.7000 - - i i i + + + i i . + . i i + + i 0.6000 - - i . . . i i i i i i . . + . i 0.5000 - - i i i . i i . i i . . + i 0.4000 - + - i . . i i i i + i i i 0.3000 - - i . + i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i i + i 0.2000 - + - i + i i . . + i i + i i i 0.1000 - . - i + i i . i i + + i i + i 0.0000 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - + + - i + + + + + + i i + + + + + + + + + + i i + i i + i 3.7699 - - i + i i i i + i i i 2.5133 - - i i i + i i i i i 1.2566 - - i i i i i + i i i 0.0000 - 2 2 - i i i i i i i i -1.2566 - + + - i + + + + + + i i + + + + + + + + + + i i + i i + i -2.5133 - - i + i i i i + i i i -3.7699 - - i i i + i i i i i -5.0265 - - i i i i i + i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.0000000E+00 0.4457461E-02 -0.3141593E+01 0.6729817E-03 -0.0000000E+00 0.2000000E-01 0.5116763E-01 -0.1880425E+01 0.1370533E+00 -0.1583274E+01 0.4000000E-01 0.1714792E+00 -0.1751850E+01 0.4041626E+00 -0.1677583E+01 0.6000000E-01 0.3210856E+00 -0.1715397E+01 0.6116751E+00 -0.1721943E+01 0.8000000E-01 0.4593221E+00 -0.1700990E+01 0.7198188E+00 -0.1733138E+01 0.9999999E-01 0.5644947E+00 -0.1693569E+01 0.7597581E+00 -0.1721767E+01 0.1200000E+00 0.6323792E+00 -0.1688077E+01 0.7552606E+00 -0.1694870E+01 0.1400000E+00 0.6681482E+00 -0.1682582E+01 0.7182088E+00 -0.1656570E+01 0.1600000E+00 0.6796163E+00 -0.1676438E+01 0.6688998E+00 -0.1622068E+01 0.1800000E+00 0.6736777E+00 -0.1669517E+01 0.6413577E+00 -0.1624215E+01 0.2000000E+00 0.6550170E+00 -0.1661722E+01 0.6500628E+00 -0.1668285E+01 0.2200000E+00 0.6259545E+00 -0.1652669E+01 0.6595039E+00 -0.1709816E+01 0.2400000E+00 0.5868149E+00 -0.1641694E+01 0.6188422E+00 -0.1704008E+01 0.2600000E+00 0.5367091E+00 -0.1628447E+01 0.5227825E+00 -0.1623383E+01 0.2800000E+00 0.4748101E+00 -0.1614229E+01 0.4202060E+00 -0.1471867E+01 0.3000000E+00 0.4019890E+00 -0.1603764E+01 0.3430119E+00 -0.1326809E+01 0.3200000E+00 0.3221993E+00 -0.1606273E+01 0.2753133E+00 -0.1297512E+01 0.3400000E+00 0.2426298E+00 -0.1634061E+01 0.2203143E+00 -0.1436010E+01 0.3600000E+00 0.1716811E+00 -0.1697574E+01 0.1955238E+00 -0.1705994E+01 0.3800000E+00 0.1151532E+00 -0.1798537E+01 0.1863182E+00 -0.2019650E+01 0.4000000E+00 0.7375210E-01 -0.1926218E+01 0.1528519E+00 -0.2350410E+01 0.4200000E+00 0.4458512E-01 -0.2062737E+01 0.8994668E-01 -0.2737313E+01 0.4400001E+00 0.2454270E-01 -0.2197546E+01 0.3516284E-01 0.3038836E+01 0.4600001E+00 0.1180289E-01 -0.2349163E+01 0.8721530E-02 0.2035405E+01 0.4800001E+00 0.5096353E-02 -0.2609713E+01 0.1645163E-01 0.5731075E+00 0.5000000E+00 0.3084440E-02 -0.3141593E+01 0.3135666E-01 0.0000000E+00 1valid problem Test of BFSM LACOV = 101 LAGMAX = 33 N = 100 LACOV = 101 LAGMAX = 33 N = 100 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4470 / edf= 89) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i ..... i 0.6000 - ..... ..... - i ... ... i i ... .. i i .. .. i i .. .. i 0.5000 - .. +++++++++ . - i . +++ ++++ .. i i .. +++ ++ . i i . ++ ++ . i i .. ++ ++ .. i 0.4000 - . ++ + . - i .. ++ ++ . i i . + ............. + . i i . ++ ... ... + . i i .. + .. .. ++ . i 0.3000 - . ++ .. .. + . - i . + .. . + . i i .. ++ .. .. + .. i i . + . . + . i i . + .. .. ++ . i 0.2000 - .. ++ .. . + . - i . + . . + . i i ++ .. .. + i i + .. . + i i ++ . .. ++ i 0.1000 - ++ .. . + - i ++ .. .. ++ i i -----------22-------22--------------------------------------------------------------2------22-------- i i +++ ... ... ++ i i ++++ .... ... ++++ i 0.0000 - ++++ ++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4470 / edf= 89) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i +++++ ++++ i i ++ ++ i 3.7699 - ++ ++ - i + + i i + + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i +++++ ++++ i i ++ ++ i -2.5133 - ++ ++ - i + + i i i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - .............. - i ... .... i i .. ... i i .. ... i i . .. i 0.7000 - . +++++++++ . - i . +++ +++++ .. i i . ++ ++ . i i . ++ +++ . i i . + ++ . i 0.6000 - . + + .. - i + ++ . i i . + ..... + . i i . + .... ..... + . i i + .. ... + . i 0.5000 - . . .. + - i + .. .. + . i i . + . . + . i i . . + . i i . + .. + . i 0.4000 - . . . + . - i + . . + . i i . + . . + .. i i . . i i . + . . + . i 0.3000 - . . + - i + . + i i . . + i i + . . + i i . + i 0.2000 - + . + - i . . + i i + . ++ i i + . . + i i -------------------------------------------------------------------22--------22---------------------- i 0.1000 - + . . ++ - i + . . +++ i i . .. ++ i i ++ . .. ++++ i i + . .. ++++++ i 0.0000 - ++ +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i +++++++++++++++++++++++ i i ++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ +++++++ i i +++++++++ i 3.7699 - + +++++ - i ++ i i + ++ i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i +++++++++++++++++++++++ i i ++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ +++++++ i i +++++++++ i -2.5133 - + +++++ - i ++ i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i ........... i i .. ..... ........ i i . ...... .. i 0.8000 - . .. - i . .. i i . + . i i . ++++ ++++ .. i i ++ +++ . i 0.7000 - . + +++ ++++++++ . - i . + ++++++ + . i i + ++ . i i + . i i . + + . i 0.6000 - + - i . + + . i i ...... + . i i + .. .. + . i i . . . + i 0.5000 - + . .. ..... + . - i . .. ... . i i . ..... . + i i + . + i i . . i 0.4000 - . + - i + . . + i i . i i . + i i + . i 0.3000 - . . + - i . + i i + . + i i ----------2-----------------------------------------------2------------------------------------------ i i + +++++ i 0.2000 - + . ++ +++ + - i . . +++ + i i . + i i + . + i i . + i 0.1000 - . + - i + . . i i . + i i + . . ++ ++++ i i + + ++ i 0.0000 - + ++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - ++ ++++++ - i ++++ +++++++++++ +++++++++ ++++ i i ++++++++++++++++++++++ ++++++++++ +++ i i +++ i i ++++ i 3.7699 - +++++ - i +++ i i +++ i i + i i + i 2.5133 - - i + i i i i i i + i 1.2566 - - i i i + i i + i i +++ i 0.0000 - 2 +2 - i i i i i i i i -1.2566 - ++ ++++++ - i ++++ +++++++++++ +++++++++ ++++ i i ++++++++++++++++++++++ ++++++++++ +++ i i +++ i i ++++ i -2.5133 - +++++ - i +++ i i +++ i i + i i + i -3.7699 - - i + i i i i i i + i -5.0265 - - i i i + i i + i i +++ i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0570 / edf= 11) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i ... i i ........ .. . i 0.9000 - .. .. . - i .. .. . . i i . ... . . + . i i . . . + + . i i . + .... + ..... i 0.8000 - ++ ++++ + . .. - i + + + . . i i . + . . . i i + + + + i i + i 0.7000 - ++ + + - i + +++ + i i + + . i i + + . i i + . + i 0.6000 - + . + - i + . +++ ++++ i i . ... + + i i . . . + + i i . . + i 0.5000 - + . . + + - i ------------------------------------------------------------2---------------------------------------- i i . . + + ++ i i . . + + i i . + + + i 0.4000 - . - i + .. . i i . ... . + + i i . . i i + + i 0.3000 - . - i + . . + i i . . + i i . + i i . ... .... + i 0.2000 - . . + + - i + . . ++++ i i . + + + i i . . . + + i i . + + + i 0.1000 - + ++ - i + + i i + + i i + + i i + ++ i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0570 / edf= 11) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i + i i i i + ++++ i 5.0265 - ++ ++ +++++++ ++ + - i +++ ++++ +++ +++ +++ + i i +++++++++++++++++++++ ++++++++++++ i i ++ i i ++++++++ i 3.7699 - ++ - i + i i ++ i i ++++ i i + i 2.5133 - + - i i i i i i i + i 1.2566 - - i i i + i i ++ i i +++ i 0.0000 - 2 +2 - i i i + i i i i + ++++ i -1.2566 - ++ ++ +++++++ ++ + - i +++ ++++ +++ +++ +++ + i i +++++++++++++++++++++ ++++++++++++ i i ++ i i ++++++++ i -2.5133 - ++ - i + i i ++ i i ++++ i i + i -3.7699 - + - i i i i i i i + i -5.0265 - - i i i + i i ++ i i +++ i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of bfsms LACOV = 17 LAGMAX = 16 N = 100 LACOV = 17 LAGMAX = 16 N = 100 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - . . . - i . . i i . . i i . i i i 0.7000 - . + + - i + + + . i i i i + + i i . . i 0.6000 - + - i i i + . . i i . . + i i . . i 0.5000 - . - i . . i i + + . i i i i . i 0.4000 - - i . + . i i . . i i i i + . i 0.3000 - - i . + i i . i i i i i 0.2000 - + - i . i i + i i . + i i - - - - - - - - - - - - - - - - - 2 - - - - - - - - i 0.1000 - + - i i i . + i i + . + i i . + i 0.0000 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i + + + + + + i i + + + + + + + + + + + + i i + + i i + + i 3.7699 - + + - i i i + + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i + + + + + + i i + + + + + + + + + + + + i i + + i i + + i -2.5133 - + + - i i i i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i . . . i i . . . . i i . . i 0.8000 - - i . i i . i i + + . i i + i 0.7000 - + + + + - i + + i i . i i i i . + i 0.6000 - - i + i i . . i i . . + i i i 0.5000 - . - i . . . i i . . i i + i i i 0.4000 - - i + . i i i i . i i i 0.3000 - + - i . i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i i + + i 0.2000 - + - i . + i i i i + . + i i i 0.1000 - - i . i i i i + + i i + i 0.0000 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - + + - i + + + + + + i i + + + + + + + + + i i + i i + i 3.7699 - + - i + i i + i i i i i 2.5133 - - i + i i i i i i i 1.2566 - - i i i i i i i + i 0.0000 - 2 2 - i i i i i i i i -1.2566 - + + - i + + + + + + i i + + + + + + + + + i i + i i + i -2.5133 - + - i + i i + i i i i i -3.7699 - - i + i i i i i i i -5.0265 - - i i i i i i i + i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.0000000E+00 0.3630565E-02 -0.3141593E+01 0.2161257E-02 -0.0000000E+00 0.2000000E-01 0.4986423E-01 -0.1854185E+01 0.1326431E+00 -0.1542412E+01 0.4000000E-01 0.1688162E+00 -0.1737231E+01 0.3895083E+00 -0.1672955E+01 0.6000000E-01 0.3167333E+00 -0.1704335E+01 0.5897397E+00 -0.1727807E+01 0.8000000E-01 0.4541179E+00 -0.1690780E+01 0.6961629E+00 -0.1737419E+01 0.9999999E-01 0.5604091E+00 -0.1682705E+01 0.7414379E+00 -0.1717893E+01 0.1200000E+00 0.6319133E+00 -0.1675483E+01 0.7490694E+00 -0.1680808E+01 0.1400000E+00 0.6737280E+00 -0.1667441E+01 0.7293420E+00 -0.1634400E+01 0.1600000E+00 0.6932608E+00 -0.1658199E+01 0.6976930E+00 -0.1597344E+01 0.1800000E+00 0.6968815E+00 -0.1647987E+01 0.6805180E+00 -0.1600409E+01 0.2000000E+00 0.6887004E+00 -0.1637225E+01 0.6925312E+00 -0.1640704E+01 0.2200000E+00 0.6703428E+00 -0.1626272E+01 0.7073455E+00 -0.1671646E+01 0.2400000E+00 0.6411178E+00 -0.1615475E+01 0.6832114E+00 -0.1654239E+01 0.2600000E+00 0.5985287E+00 -0.1605731E+01 0.6158467E+00 -0.1574332E+01 0.2800000E+00 0.5395407E+00 -0.1599677E+01 0.5350267E+00 -0.1454361E+01 0.3000000E+00 0.4631522E+00 -0.1603192E+01 0.4374332E+00 -0.1361435E+01 0.3200000E+00 0.3738008E+00 -0.1626212E+01 0.3087747E+00 -0.1374501E+01 0.3400000E+00 0.2825961E+00 -0.1680928E+01 0.2091250E+00 -0.1568745E+01 0.3600000E+00 0.2027303E+00 -0.1775410E+01 0.1873786E+00 -0.1908048E+01 0.3800000E+00 0.1414258E+00 -0.1904123E+01 0.2153432E+00 -0.2217854E+01 0.4000000E+00 0.9690997E-01 -0.2045211E+01 0.2152805E+00 -0.2459651E+01 0.4200000E+00 0.6341619E-01 -0.2173923E+01 0.1414951E+00 -0.2692963E+01 0.4400001E+00 0.3740714E-01 -0.2282151E+01 0.4875129E-01 -0.2988750E+01 0.4600001E+00 0.1877982E-01 -0.2395887E+01 0.3485700E-02 0.2326911E+01 0.4800001E+00 0.8104702E-02 -0.2618454E+01 0.2094777E-01 0.3462602E+00 0.5000000E+00 0.4756864E-02 -0.3141593E+01 0.4701338E-01 0.0000000E+00 1valid problem test of bfsv starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i ............ i i ... .... i i ... .. i i .. .. i 0.5000 - .. .. - i .. .. i i .. ++++++++++++ . i i .. +++ +++ . i i . +++ +++ .. i 0.4000 - .. ++ ++ . - i . ++ + . i i . + ++ .. i i .. ++ ........ + . i i . ++ .... .... ++ . i 0.3000 - .. + .. ... + . - i . ++ ... .. + . i i . + .. . ++ . i i .. ++ .. .. + .. i i . + . .. + . i 0.2000 - .. ++ .. . ++ . - i . + .. .. + . i i . ++ .. . + . i i + . .. ++ i i ++ .. . + i 0.1000 - ++ .. .. ++ - i ++ .. .. + i i -----------22-------22------------------------------------------------------------22-----22---------- i i +++ ... .. +++ i i +++++ .... ... +++ i 0.0000 - +++ . . ++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i + + i i + i i + + i 2.5133 - + + - i +++ ++ i i ++++++ ++++ i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i ++ + i i + + i -3.7699 - + + - i +++ ++ i i ++++++ ++++ i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i .............. i i ... ... i i .. ... i i . ... i 0.7000 - . .. - i . +++++++++ .. i i . +++ +++ . i i . ++ +++ .. i i . + ++ . i 0.6000 - . + ++ . - i + ++ .. i i . + ++ . i i . + ..... + . i i + .... ... + . i 0.5000 - . . ... ++ . - i + .. .. + . i i . + . .. + . i i . .. + . i i . + . . + . i 0.4000 - + . .. + . - i . . + . i i . + . . + .. i i . . + . i i . + .. + . i 0.3000 - . . + . - i + . . + i i . + i i + . . + i i . . + i 0.2000 - + . + - i + . . ++ i i . + i i + . .. + i i -----------2------------------------------------------------------2--------22------------------------ i 0.1000 - + . ++ - i + . .. ++ i i + . . +++ i i + . ... +++ i i + . .. ++++++ i 0.0000 - ++ ++++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i + + i i + i i ++ i 2.5133 - + ++++ - i ++++++ i i ++ ++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i +++++++++++++++++ i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i + ++ i i ++ i -3.7699 - + ++++ - i ++++++ i i ++ ++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i +++++++++++++++++ i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i .. i i .... ..... i i .. .. i i . ... i 0.8000 - . ............... - i . .. i i . ++++++ . i i ++ ++ . i i . + ++ . i 0.7000 - + ++ . - i . + + . i i +++ ++++++ . i i . + +++++ ++ . i i + + .. i 0.6000 - + . - i . ...... + .. i i + . .. . i i . . + . i i . + . . + .. i 0.5000 - . . + . - i . i i + . .. + i i . ..... + i i . ....... . + i 0.4000 - + . + - i . . + i i . + i i + . + i i . + i 0.3000 - . ++ - i . + i i + . + i i -----------------------------------------------------2------------22--------------------------------- i i . . ++ i 0.2000 - + +++++ - i . ++++ i i . . ++ i i + .. + i i . + i 0.1000 - . .. + - i + . ++ i i . .. + i i + .. ++ ++ i i + +++ ++++ i 0.0000 - + +++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - + - i + i i ++ i i + i i i 5.0265 - + - i + i i i i + i i i 3.7699 - + - i ++ i i + i i ++ i i ++ i 2.5133 - +++ - i +++ i i +++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i ++++ +++++++ +++++++ +++++ i 1.2566 - + ++++++++ - i i i i i i i i 0.0000 - 2 +2 - i + i i ++ i i + i i i -1.2566 - + - i + i i i i + i i i -2.5133 - + - i ++ i i + i i ++ i i ++ i -3.7699 - +++ - i +++ i i +++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i ++++ +++++++ +++++++ +++++ i -5.0265 - + ++++++++ - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i ........ .... i 0.9000 - .. .. . . - i ....... . . i i . . . i i . . i i . +++++ . ++ i 0.8000 - + ++ .. . + . - i . + + ... + ... i i + + .. i i + + + . i i +++++ + i 0.7000 - + + - i + i i + + i i + i i + + i 0.6000 - + ... - i . . + + .. + i i . . . i i . + + . + i i . . ++ + i 0.5000 - + ++ - i . . + i i ----------------------------2----------------2--------2---------------------------------------------- i i . i i ... . + + i 0.4000 - + . . . + - i . + + i i . + + + + i i . . + + + i i + + i 0.3000 - . + - i + . . +++ + + i i i i . + + i i . . . + i 0.2000 - . + + - i + . . . + i i . . .. + + i i .. ++ ++ ++++ i i ++ i 0.1000 - + . + + - i + i i + + i i + + i i ++ ++ i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - + - i +++ i i ++ i i i i + i 5.0265 - - i i i + i i i i + i 3.7699 - ++ - i +++ i i + i i + i i + i 2.5133 - ++ - i +++ i i +++++ i i ++++++++++++++++++++++ +++++++++++++ + i i ++++ +++ +++ +++ ++ i 1.2566 - + ++ +++++++++++ + - i + ++++ i i + i i i i i 0.0000 - 2 +2 - i +++ i i ++ i i i i + i -1.2566 - - i i i + i i i i + i -2.5133 - ++ - i +++ i i + i i + i i + i -3.7699 - ++ - i +++ i i +++++ i i ++++++++++++++++++++++ +++++++++++++ + i i ++++ +++ +++ +++ ++ i -5.0265 - + ++ +++++++++++ + - i + ++++ i i + i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of bfsvs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i . . . . i i . i i . i i . i 0.7000 - . - i + + . i i + + i i + . i i . + i 0.6000 - - i + . i i + i i . + i i . . i 0.5000 - . . . - i . + i i + . i i i i . . i 0.4000 - . + - i i i . . . i i i i + + i 0.3000 - . - i . i i i i + i i . i 0.2000 - - i + + i i i i . . i i - - - - - - - - - - - - - - - - - - - 2 - - - - - - i 0.1000 - - i . + i i + i i . . + i i + + i 0.0000 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i + + i i i i i 2.5133 - + - i + + i i + + i i + + + + + + + + + + + + + + + i i + + + + i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i + + i i i -3.7699 - + - i + + i i + + i i + + + + + + + + + + + + + + + i i + + + + i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i . . i i . . i i . i 0.8000 - . . . - i . . i i + + i i i i + + . i 0.7000 - - i i i + + + i i . + . i i + + i 0.6000 - - i . . . i i i i i i . . + . i 0.5000 - - i i i . i i . i i . . + i 0.4000 - + - i . . i i i i + i i i 0.3000 - - i . + i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i i + i 0.2000 - + - i + i i . . + i i + i i i 0.1000 - . - i + i i . i i + + i i + i 0.0000 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i + i i i i i 5.0265 - - i i i i i + i i i 3.7699 - - i i i + i i i i + i 2.5133 - - i + i i + i i + + + + + + + + + + i i + + + + + + i 1.2566 - + + - i i i i i i i i 0.0000 - 2 2 - i i i + i i i i i -1.2566 - - i i i i i + i i i -2.5133 - - i i i + i i i i + i -3.7699 - - i + i i + i i + + + + + + + + + + i i + + + + + + i -5.0265 - + + - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.0000000E+00 0.4457461E-02 0.3141593E+01 0.6729817E-03 0.0000000E+00 0.2000000E-01 0.5116763E-01 0.1880425E+01 0.1370533E+00 0.1583274E+01 0.4000000E-01 0.1714792E+00 0.1751850E+01 0.4041626E+00 0.1677583E+01 0.6000000E-01 0.3210856E+00 0.1715397E+01 0.6116751E+00 0.1721943E+01 0.8000000E-01 0.4593221E+00 0.1700990E+01 0.7198188E+00 0.1733138E+01 0.9999999E-01 0.5644947E+00 0.1693569E+01 0.7597581E+00 0.1721767E+01 0.1200000E+00 0.6323792E+00 0.1688077E+01 0.7552606E+00 0.1694870E+01 0.1400000E+00 0.6681482E+00 0.1682582E+01 0.7182088E+00 0.1656570E+01 0.1600000E+00 0.6796163E+00 0.1676438E+01 0.6688998E+00 0.1622068E+01 0.1800000E+00 0.6736777E+00 0.1669517E+01 0.6413577E+00 0.1624215E+01 0.2000000E+00 0.6550170E+00 0.1661722E+01 0.6500628E+00 0.1668285E+01 0.2200000E+00 0.6259545E+00 0.1652669E+01 0.6595039E+00 0.1709816E+01 0.2400000E+00 0.5868149E+00 0.1641694E+01 0.6188422E+00 0.1704008E+01 0.2600000E+00 0.5367091E+00 0.1628447E+01 0.5227825E+00 0.1623383E+01 0.2800000E+00 0.4748101E+00 0.1614229E+01 0.4202060E+00 0.1471867E+01 0.3000000E+00 0.4019890E+00 0.1603764E+01 0.3430119E+00 0.1326809E+01 0.3200000E+00 0.3221993E+00 0.1606273E+01 0.2753133E+00 0.1297512E+01 0.3400000E+00 0.2426298E+00 0.1634061E+01 0.2203143E+00 0.1436010E+01 0.3600000E+00 0.1716811E+00 0.1697574E+01 0.1955238E+00 0.1705994E+01 0.3800000E+00 0.1151532E+00 0.1798537E+01 0.1863182E+00 0.2019650E+01 0.4000000E+00 0.7375210E-01 0.1926218E+01 0.1528519E+00 0.2350410E+01 0.4200000E+00 0.4458512E-01 0.2062737E+01 0.8994668E-01 0.2737313E+01 0.4400001E+00 0.2454270E-01 0.2197546E+01 0.3516284E-01 -0.3038836E+01 0.4600001E+00 0.1180289E-01 0.2349163E+01 0.8721530E-02 -0.2035405E+01 0.4800001E+00 0.5096353E-02 0.2609713E+01 0.1645163E-01 -0.5731075E+00 0.5000000E+00 0.3084440E-02 0.3141593E+01 0.3135666E-01 -0.0000000E+00 1valid problem LACOV = 100 LAGMAX = 99 N = 100 LACOV = 100 LAGMAX = 99 N = 100 test of bfsmv starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4470 / edf= 89) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i ..... i 0.6000 - ..... ..... - i ... ... i i ... .. i i .. .. i i .. .. i 0.5000 - .. +++++++++ . - i . +++ ++++ .. i i .. +++ ++ . i i . ++ ++ . i i .. ++ ++ .. i 0.4000 - . ++ + . - i .. ++ ++ . i i . + ............. + . i i . ++ ... ... + . i i .. + .. .. ++ . i 0.3000 - . ++ .. .. + . - i . + .. . + . i i .. ++ .. .. + .. i i . + . . + . i i . + .. .. ++ . i 0.2000 - .. ++ .. . + . - i . + . . + . i i ++ .. .. + i i + .. . + i i ++ . .. ++ i 0.1000 - ++ .. . + - i ++ .. .. ++ i i -----------22-------22--------------------------------------------------------------2------22-------- i i +++ ... ... ++ i i ++++ .... ... ++++ i 0.0000 - ++++ ++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4470 / edf= 89) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i + + i i i i + + i 2.5133 - ++ ++ - i ++ ++ i i +++++ ++++ i i ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i + + i i + + i -3.7699 - ++ ++ - i ++ ++ i i +++++ ++++ i i ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - .............. - i ... .... i i .. ... i i .. ... i i . .. i 0.7000 - . +++++++++ . - i . +++ +++++ .. i i . ++ ++ . i i . ++ +++ . i i . + ++ . i 0.6000 - . + + .. - i + ++ . i i . + ..... + . i i . + .... ..... + . i i + .. ... + . i 0.5000 - . . .. + - i + .. .. + . i i . + . . + . i i . . + . i i . + .. + . i 0.4000 - . . . + . - i + . . + . i i . + . . + .. i i . . i i . + . . + . i 0.3000 - . . + - i + . + i i . . + i i + . . + i i . + i 0.2000 - + . + - i . . + i i + . ++ i i + . . + i i -------------------------------------------------------------------22--------22---------------------- i 0.1000 - + . . ++ - i + . . +++ i i . .. ++ i i ++ . .. ++++ i i + . .. ++++++ i 0.0000 - ++ +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i + + i i + i i ++ i 2.5133 - + +++++ - i +++++++++ i i ++ +++++++ i i ++++++++++++++++++++++++++++++++++++++ +++++++++++ i i +++++++++++++++++++++++ i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i + ++ i i ++ i -3.7699 - + +++++ - i +++++++++ i i ++ +++++++ i i ++++++++++++++++++++++++++++++++++++++ +++++++++++ i i +++++++++++++++++++++++ i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i ........... i i .. ..... ........ i i . ...... .. i 0.8000 - . .. - i . .. i i . + . i i . ++++ ++++ .. i i ++ +++ . i 0.7000 - . + +++ ++++++++ . - i . + ++++++ + . i i + ++ . i i + . i i . + + . i 0.6000 - + - i . + + . i i ...... + . i i + .. .. + . i i . . . + i 0.5000 - + . .. ..... + . - i . .. ... . i i . ..... . + i i + . + i i . . i 0.4000 - . + - i + . . + i i . i i . + i i + . i 0.3000 - . . + - i . + i i + . + i i ----------2-----------------------------------------------2------------------------------------------ i i + +++++ i 0.2000 - + . ++ +++ + - i . . +++ + i i . + i i + . + i i . + i 0.1000 - . + - i + . . i i . + i i + . . ++ ++++ i i + + ++ i 0.0000 - + ++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - + - i +++ i i + i i + i i i 5.0265 - - i + i i i i i i + i 3.7699 - - i + i i + i i +++ i i +++ i 2.5133 - +++++ - i ++++ i i +++ i i ++++++++++++++++++++++ ++++++++++ +++ i i ++++ +++++++++++ +++++++++ ++++ i 1.2566 - ++ ++++++ - i i i i i i i i 0.0000 - 2 +2 - i +++ i i + i i + i i i -1.2566 - - i + i i i i i i + i -2.5133 - - i + i i + i i +++ i i +++ i -3.7699 - +++++ - i ++++ i i +++ i i ++++++++++++++++++++++ ++++++++++ +++ i i ++++ +++++++++++ +++++++++ ++++ i -5.0265 - ++ ++++++ - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0570 / edf= 11) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i ... i i ........ .. . i 0.9000 - .. .. . - i .. .. . . i i . ... . . + . i i . . . + + . i i . + .... + ..... i 0.8000 - ++ ++++ + . .. - i + + + . . i i . + . . . i i + + + + i i + i 0.7000 - ++ + + - i + +++ + i i + + . i i + + . i i + . + i 0.6000 - + . + - i + . +++ ++++ i i . ... + + i i . . . + + i i . . + i 0.5000 - + . . + + - i ------------------------------------------------------------2---------------------------------------- i i . . + + ++ i i . . + + i i . + + + i 0.4000 - . - i + .. . i i . ... . + + i i . . i i + + i 0.3000 - . - i + . . + i i . . + i i . + i i . ... .... + i 0.2000 - . . + + - i + . . ++++ i i . + + + i i . . . + + i i . + + + i 0.1000 - + ++ - i + + i i + + i i + + i i + ++ i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0570 / edf= 11) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - + - i +++ i i ++ i i + i i i 5.0265 - - i + i i i i i i i 3.7699 - + - i + i i ++++ i i ++ i i + i 2.5133 - ++ - i ++++++++ i i ++ i i +++++++++++++++++++++ ++++++++++++ i i +++ ++++ +++ +++ +++ + i 1.2566 - ++ ++ +++++++ ++ + - i + ++++ i i i i + i i i 0.0000 - 2 +2 - i +++ i i ++ i i + i i i -1.2566 - - i + i i i i i i i -2.5133 - + - i + i i ++++ i i ++ i i + i -3.7699 - ++ - i ++++++++ i i ++ i i +++++++++++++++++++++ ++++++++++++ i i +++ ++++ +++ +++ +++ + i -5.0265 - ++ ++ +++++++ ++ + - i + ++++ i i i i + i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of bfsmvs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - . . . - i . . i i . . i i . i i i 0.7000 - . + + - i + + + . i i i i + + i i . . i 0.6000 - + - i i i + . . i i . . + i i . . i 0.5000 - . - i . . i i + + . i i i i . i 0.4000 - - i . + . i i . . i i i i + . i 0.3000 - - i . + i i . i i i i i 0.2000 - + - i . i i + i i . + i i - - - - - - - - - - - - - - - - - 2 - - - - - - - - i 0.1000 - + - i i i . + i i + . + i i . + i 0.0000 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2254 / edf= 45) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i + + i i i i i 2.5133 - + + - i + + i i + + i i + + + + + + + + + + + + i i + + + + + + i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i + + i i i -3.7699 - + + - i + + i i + + i i + + + + + + + + + + + + i i + + + + + + i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i . . . i i . . . . i i . . i 0.8000 - - i . i i . i i + + . i i + i 0.7000 - + + + + - i + + i i . i i i i . + i 0.6000 - - i + i i . . i i . . + i i i 0.5000 - . - i . . . i i . . i i + i i i 0.4000 - - i + . i i i i . i i i 0.3000 - + - i . i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i i + + i 0.2000 - + - i . + i i i i + . + i i i 0.1000 - - i . i i i i + + i i + i 0.0000 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1142 / edf= 23) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i + i i i i i i i 5.0265 - - i i i i i i i + i 3.7699 - - i i i i i + i i + i 2.5133 - + - i + i i + i i + + + + + + + + + i i + + + + + + i 1.2566 - + + - i i i i i i i i 0.0000 - 2 2 - i + i i i i i i i -1.2566 - - i i i i i i i + i -2.5133 - - i i i i i + i i + i -3.7699 - + - i + i i + i i + + + + + + + + + i i + + + + + + i -5.0265 - + + - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.0000000E+00 0.3630565E-02 0.3141593E+01 0.2161257E-02 0.0000000E+00 0.2000000E-01 0.4986423E-01 0.1854185E+01 0.1326431E+00 0.1542412E+01 0.4000000E-01 0.1688162E+00 0.1737231E+01 0.3895083E+00 0.1672955E+01 0.6000000E-01 0.3167333E+00 0.1704335E+01 0.5897397E+00 0.1727807E+01 0.8000000E-01 0.4541179E+00 0.1690780E+01 0.6961629E+00 0.1737419E+01 0.9999999E-01 0.5604091E+00 0.1682705E+01 0.7414379E+00 0.1717893E+01 0.1200000E+00 0.6319133E+00 0.1675483E+01 0.7490694E+00 0.1680808E+01 0.1400000E+00 0.6737280E+00 0.1667441E+01 0.7293420E+00 0.1634400E+01 0.1600000E+00 0.6932608E+00 0.1658199E+01 0.6976930E+00 0.1597344E+01 0.1800000E+00 0.6968815E+00 0.1647987E+01 0.6805180E+00 0.1600409E+01 0.2000000E+00 0.6887004E+00 0.1637225E+01 0.6925312E+00 0.1640704E+01 0.2200000E+00 0.6703428E+00 0.1626272E+01 0.7073455E+00 0.1671646E+01 0.2400000E+00 0.6411178E+00 0.1615475E+01 0.6832114E+00 0.1654239E+01 0.2600000E+00 0.5985287E+00 0.1605731E+01 0.6158467E+00 0.1574332E+01 0.2800000E+00 0.5395407E+00 0.1599677E+01 0.5350267E+00 0.1454361E+01 0.3000000E+00 0.4631522E+00 0.1603192E+01 0.4374332E+00 0.1361435E+01 0.3200000E+00 0.3738008E+00 0.1626212E+01 0.3087747E+00 0.1374501E+01 0.3400000E+00 0.2825961E+00 0.1680928E+01 0.2091250E+00 0.1568745E+01 0.3600000E+00 0.2027303E+00 0.1775410E+01 0.1873786E+00 0.1908048E+01 0.3800000E+00 0.1414258E+00 0.1904123E+01 0.2153432E+00 0.2217854E+01 0.4000000E+00 0.9690997E-01 0.2045211E+01 0.2152805E+00 0.2459651E+01 0.4200000E+00 0.6341619E-01 0.2173923E+01 0.1414951E+00 0.2692963E+01 0.4400001E+00 0.3740714E-01 0.2282151E+01 0.4875129E-01 0.2988750E+01 0.4600001E+00 0.1877982E-01 0.2395887E+01 0.3485700E-02 -0.2326911E+01 0.4800001E+00 0.8104702E-02 0.2618454E+01 0.2094777E-01 -0.3462602E+00 0.5000000E+00 0.4756864E-02 0.3141593E+01 0.4701338E-01 -0.0000000E+00 1minimum problem size test of bfs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4798 / edf= 16) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i ............. i 0.8000 - ..... ...... - i ... .... i i .. ... i i ... ... i i . ... i 0.7000 - .. .. - i .. .. i i . . i i .. +++++++ .. i i ++++ +++++ i 0.6000 - +++ ++++ - i ++ ++ i i ++ ++ i i ++ ++ i i + ++ i 0.5000 - + ++ - i ++ ++ i i + + i i + ++ i i + + i 0.4000 - + + - i + ++ i i + .... + i i ---------------------22--------------------22222----22222-------------------------2------------------ i i + ... ... + i 0.3000 - + .. .. + - i + . .. + i i + .. .. + i i + . .. + i i + .. .. + i 0.2000 - + . .. + - i + . . + i i ++ .. .. ++ i i + . .. + i i ++ . . + i 0.1000 - ++ .. .. ++ - i ++++ .. .. ++++ i i ++ i i i i i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4798 / edf= 16) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i ++ i i ++ i i ++++ i i +++++ i 5.0265 - ++++++++++ - i ++++++++++++++++++ i i ++++++++++++++++++++++++++++++ i i ++++++++++++++ i i ++++++ i 3.7699 - +++ - i +++ i i +++ i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - 2 - i ++ i i ++ i i ++++ i i +++++ i -1.2566 - ++++++++++ - i ++++++++++++++++++ i i ++++++++++++++++++++++++++++++ i i ++++++++++++++ i i ++++++ i -2.5133 - +++ - i +++ i i ++ i i + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2514 / edf= 9) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i ........... i i ... ..... i i .. ++++ ... i i . +++ ++++ ... i 0.9000 - . + ++ .. - i + ++ .. i i . + + . i i + + i i . ++ i 0.8000 - + .. ... + - i . . + i i + . . + i i . . i i . + i 0.7000 - + . + - i . . + i i . + i i + . i i . + i 0.6000 - ---------------------------------2----------2-------------------------------------------------------- - i + . +++++++ i i . + ++ ++ i i + + ++ i i . + + ++ i 0.5000 - + . + ++ + - i . + + + i i ++++++ + i i . + i i + . + i 0.4000 - . + - i + i i . i i + . . + i i + i 0.3000 - . + - i + i i . i i + . + i i . + i 0.2000 - + - i + + i i + i i + i i + + i 0.1000 - ++ - i + +++ i i + ++ i i i i i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2514 / edf= 9) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i +++++++++++++ i i +++++ +++++++++ i 5.0265 - ++++ ++++++ - i ++++++ +++ i i +++++++++++++++++++++++++++++++ +++ i i +++++++++ ++ i i + + i 3.7699 - + ++ - i + + i i + ++ i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i +++++++++++++ i i +++++ +++++++++ i -1.2566 - ++++ ++++++ - i ++++++ +++ i i +++++++++++++++++++++++++++++++ +++ i i +++++++++ ++ i i + + i -2.5133 - + ++ - i + + i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1379 / edf= 5) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - .................. - i .... +++++++++++++ .... i i +++ ++ i i ++ ... ++ i i + ... .. + i 0.9000 - -------2--------2--------2------2-------------------------------------------------------------------- - i + .. . + i i . . i i . + i i + . . i 0.8000 - + - i . . i i ++++ i i . . + + + i i + + ++ +++ i 0.7000 - . + ++ ++ - i . + + ++++++ + i i + i i . + i i + + i 0.6000 - . + - i + + i i i i + + i i i 0.5000 - + + - i i i i i + + + i i i 0.4000 - - i + + + i i i i + ++ i i + i 0.3000 - - i +++ i i + + + i i + + i i + + + i 0.2000 - ++ + - i + i i i i ++ i i + ++ i 0.1000 - - i i i i i i i i 0.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1379 / edf= 5) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i ++++++++++++ i i ++ +++++ i i ++ ++++++++++ i 5.0265 - + ++++++ - i ++ ++ i i ++++++++++++++++++++++++ +++++++ ++ i i ++++++++ + i i + i 3.7699 - + - i ++ i i + ++++ + i i +++++++ i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i ++++++++++++ i i ++ +++++ i i ++ ++++++++++ i -1.2566 - + ++++++ - i ++ ++ i i ++++++++++++++++++++++++ +++++++ ++ i i ++++++++ + i i + i -2.5133 - + - i ++ i i ++++ i i + ++++++++ i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1minimum problem size test of bfss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 1 / bw=1.0000 / edf= 34) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i i i i i i i i 0.5000 - - i i i i i i i i 0.4000 - - i i i i i i i i 0.3000 - - i i i i i i i i 0.2000 - - i - i i i i i i i 0.1000 - - i i i i i i i + i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 1 / bw=1.0000 / edf= 34) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.0000000E+00 0.2530848E-01 -0.3141593E+01 1check handling of fmin and fmax test of bfss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i i i i i i i i 0.5000 - - i i i i i i i i 0.4000 - - i i i i i i i i 0.3000 - - i i i i i i i i 0.2000 - - i i i i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i 0.1000 - - i i i i i i i + + + + + + + + i 0.0000 - + + + + + + + + + + + + + + + + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.4500 0.4550 0.4600 0.4650 0.4700 0.4750 0.4800 0.4850 0.4900 0.4950 0.5000 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i + + + + + + + + i 3.7699 - + + + + + + + + - i + + + + + i i + + + + + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i i i i i + + + + + + + + i -2.5133 - + + + + + + + + - i + + + + + i i + + + + i i + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.4500 0.4550 0.4600 0.4650 0.4700 0.4750 0.4800 0.4850 0.4900 0.4950 0.5000 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i i i i i i i i 0.5000 - - i i i i i i i i 0.4000 - - i i i i i i i i 0.3000 - - i i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i i i 0.2000 - - i i i i i i i i 0.1000 - - i i i i i + + + i i + + + + + + + + + + + + + + + i 0.0000 - + + + + + + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.4500 0.4550 0.4600 0.4650 0.4700 0.4750 0.4800 0.4850 0.4900 0.4950 0.5000 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i i i i i + i 2.5133 - + + - i + + i i + i i + + i i + i 1.2566 - + + - i + + i i + + i i + + + i i + + + + + i 0.0000 - + + 2 - i i i i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i i i + i -3.7699 - + + - i + + i i + i i + + i i + i -5.0265 - + + - i + + i i + + i i + + + i i + + + + + i -6.2832 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.4500 0.4550 0.4600 0.4650 0.4700 0.4750 0.4800 0.4850 0.4900 0.4950 0.5000 ierr is 0 0.4500000E+00 0.1733828E-01 -0.2268027E+01 0.1783388E-01 0.2662712E+01 0.4520000E+00 0.1610241E-01 -0.2283062E+01 0.1531705E-01 0.2564167E+01 0.4540000E+00 0.1493211E-01 -0.2298589E+01 0.1313815E-01 0.2453848E+01 0.4560000E+00 0.1382627E-01 -0.2314705E+01 0.1130698E-01 0.2329802E+01 0.4580000E+00 0.1278364E-01 -0.2331521E+01 0.9832307E-02 0.2190462E+01 0.4600000E+00 0.1180290E-01 -0.2349162E+01 0.8721565E-02 0.2035409E+01 0.4620000E+00 0.1088279E-01 -0.2367768E+01 0.7980028E-02 0.1866304E+01 0.4640000E+00 0.1002193E-01 -0.2387495E+01 0.7609764E-02 0.1687552E+01 0.4660000E+00 0.9218918E-02 -0.2408515E+01 0.7608525E-02 0.1506040E+01 0.4680000E+00 0.8472345E-02 -0.2431018E+01 0.7969003E-02 0.1329561E+01 0.4700000E+00 0.7780758E-02 -0.2455213E+01 0.8677707E-02 0.1164665E+01 0.4720000E+00 0.7142686E-02 -0.2481321E+01 0.9714105E-02 0.1015269E+01 0.4740000E+00 0.6556652E-02 -0.2509578E+01 0.1105004E-01 0.8825777E+00 0.4760000E+00 0.6021212E-02 -0.2540232E+01 0.1264925E-01 0.7658660E+00 0.4780000E+00 0.5534920E-02 -0.2573529E+01 0.1446715E-01 0.6634031E+00 0.4800000E+00 0.5096362E-02 -0.2609712E+01 0.1645152E-01 0.5731101E+00 0.4820001E+00 0.4704183E-02 -0.2649002E+01 0.1854307E-01 0.4929565E+00 0.4840001E+00 0.4357107E-02 -0.2691579E+01 0.2067694E-01 0.4211220E+00 0.4860001E+00 0.4053941E-02 -0.2737565E+01 0.2278384E-01 0.3560459E+00 0.4880001E+00 0.3793566E-02 -0.2786997E+01 0.2479378E-01 0.2963995E+00 0.4900001E+00 0.3575016E-02 -0.2839796E+01 0.2663722E-01 0.2410722E+00 0.4920001E+00 0.3397418E-02 -0.2895751E+01 0.2824828E-01 0.1891176E+00 0.4940001E+00 0.3260050E-02 -0.2954498E+01 0.2956873E-01 0.1397187E+00 0.4960001E+00 0.3162345E-02 -0.3015516E+01 0.3054927E-01 0.9215710E-01 0.4980001E+00 0.3103898E-02 -0.3078139E+01 0.3115278E-01 0.4578568E-01 0.5000000E+00 0.3084440E-02 -0.3141593E+01 0.3135666E-01 0.0000000E+00 1white noise spectrum test of bfss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i i i i i i i i 0.5000 - - i i i i i i i i 0.4000 - - i i i i i . i i . i 0.3000 - - i i i i i i i i 0.2000 - - i i i + i i + i i - - - - - - - - - - - - - - - - - - - - - - - 2 - - i 0.1000 - + - i i i + + + + + i i + + + + + + + . i i + + + + + + + + . i 0.0000 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i + + + i i + + + + i i i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i i i i i i 2.5133 - - i i i i i i i i 1.2566 - + - i + + i i + + + i i + + + + i i + + + + + i 0.0000 - 2 + + 2 - i + + + i i + + + + i i i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i i i i -3.7699 - - i i i i i i i i -5.0265 - + - i + + i i + + + i i + + + + i i + + + + + i -6.2832 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i . i i . i i i i i 0.5000 - - i i i i i i i i 0.4000 - - i i i i i i i + i 0.3000 - + - i i i i i - - - - - - - - - - - - - - - - - - - - - - - - - - i i + i 0.2000 - + - i i i + i i + i i + i 0.1000 - . - i + + + + + . i i + + i i + + + + + + + i i + + + i 0.0000 - + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - + + - i i i + i i + + + + i i i 5.0265 - - i i i i i i i i 3.7699 - - i i i i i i i i 2.5133 - - i + i i + + + i i i i + + i 1.2566 - - i + + + i i + + + + + i i + + i i i 0.0000 - 2 + + + 2 - i i i + i i + + + + i i i -1.2566 - - i i i i i i i i -2.5133 - - i i i i i i i i -3.7699 - - i + i i + + + i i i i + + i -5.0265 - - i + + + i i + + + + + i i + + i i i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.0000000E+00 0.1062460E-01 0.0000000E+00 0.3991899E-01 0.0000000E+00 0.2000000E-01 0.1259556E-01 0.3564040E+00 0.3970817E-01 0.6836433E+00 0.4000000E-01 0.1781762E-01 0.5803694E+00 0.2359622E-01 0.1120388E+01 0.6000000E-01 0.2456003E-01 0.6714760E+00 0.9958330E-02 0.1385339E+01 0.8000000E-01 0.3088756E-01 0.6642999E+00 0.1207479E-01 0.1062880E+01 0.9999999E-01 0.3562115E-01 0.5792899E+00 0.4189212E-01 0.7754813E+00 0.1200000E+00 0.3900401E-01 0.4282464E+00 0.8532235E-01 0.7099605E+00 0.1400000E+00 0.4252293E-01 0.2291413E+00 0.8693677E-01 0.6950397E+00 0.1600000E+00 0.4762783E-01 0.1422634E-01 0.5746650E-01 0.5792100E+00 0.1800000E+00 0.5410570E-01 -0.1811922E+00 0.4663985E-01 0.1210311E+00 0.2000000E+00 0.5951067E-01 -0.3368103E+00 0.8436157E-01 -0.3992320E+00 0.2200000E+00 0.6027452E-01 -0.4488989E+00 0.1580998E+00 -0.6645611E+00 0.2400000E+00 0.5380107E-01 -0.5197893E+00 0.1963007E+00 -0.7887214E+00 0.2600000E+00 0.4034970E-01 -0.5469029E+00 0.1366451E+00 -0.8281283E+00 0.2800000E+00 0.2372555E-01 -0.5068364E+00 0.3696610E-01 -0.7323524E+00 0.3000000E+00 0.1004692E-01 -0.3000945E+00 0.2284167E-02 0.7800367E+00 0.3200000E+00 0.4515277E-02 0.3676544E+00 0.1913695E-01 0.1906670E+01 0.3400000E+00 0.7977478E-02 0.1042004E+01 0.3266985E-01 0.2111780E+01 0.3600000E+00 0.1685054E-01 0.1165517E+01 0.4220521E-01 0.2138790E+01 0.3800000E+00 0.2770041E-01 0.1038104E+01 0.6242230E-01 0.1910685E+01 0.4000000E+00 0.4162326E-01 0.8052125E+00 0.8560546E-01 0.1562284E+01 0.4200000E+00 0.6258494E-01 0.5611888E+00 0.8984140E-01 0.1077431E+01 0.4400001E+00 0.9153556E-01 0.3610292E+00 0.1148999E+00 0.4118865E+00 0.4600001E+00 0.1231352E+00 0.2114959E+00 0.2111955E+00 -0.1865956E-01 0.4800001E+00 0.1479192E+00 0.9725173E-01 0.3015384E+00 -0.1021575E+00 0.5000000E+00 0.1573115E+00 0.0000000E+00 0.3266877E+00 -0.0000000E+00 test of ccf starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 largest lag value to be used = 33 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -25 -26 -27 -28 -29 -30 -31 -32 -33 ccf -0.06 0.00 0.08 0.06 0.03 -0.06 -0.05 0.05 0.06 standard error 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 lag -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 -0.10 -0.04 0.04 -0.01 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 lag 0 ccf -0.07 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 lag 13 14 15 16 17 18 19 20 21 22 23 24 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 0.04 0.07 0.16 0.23 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag 25 26 27 28 29 30 31 32 33 ccf 0.25 0.15 0.03 -0.04 -0.03 -0.02 0.00 -0.11 -0.12 standard error 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -33.000 i ++++ i 0.62185E-01 -32.000 i ++++ i 0.51762E-01 -31.000 i +++ i -0.49269E-01 -30.000 i ++++ i -0.64776E-01 -29.000 i ++ i 0.26418E-01 -28.000 i ++++ i 0.62819E-01 -27.000 i +++++ i 0.81593E-01 -26.000 i + i 0.11350E-02 -25.000 i ++++ i -0.64341E-01 -24.000 i ++ i -0.13985E-01 -23.000 i +++ i 0.43281E-01 -22.000 i +++ i -0.41656E-01 -21.000 i ++++++ i -.10226 -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 21.000 i +++ i 0.44491E-01 22.000 i +++++ i 0.72656E-01 23.000 i +++++++++ i .15576 24.000 i +++++++++++++ i .23426 25.000 i +++++++++++++ i .24505 26.000 i +++++++++ i .15307 27.000 i ++ i 0.26330E-01 28.000 i +++ i -0.36697E-01 29.000 i ++ i -0.27453E-01 30.000 i ++ i -0.19839E-01 31.000 i + i 0.33872E-02 32.000 i +++++++ i -.11343 33.000 i +++++++ i -.12325 ierr = 0 test of ccfs starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 0 ccf -0.07 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 1 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 3 average of the series = 0.5379997E-01 0.5379997E-01 standard deviation of the series = 1.441247 1.441247 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf -0.10 -0.02 0.01 -0.03 -0.02 0.12 0.19 0.14 standard error 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf 0.51 0.07 -0.14 -0.14 -0.09 -0.12 -0.09 0.01 0.10 0.11 0.03 -0.10 standard error 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 lag 0 ccf 1.00 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.51 0.07 -0.14 -0.14 -0.09 -0.12 -0.09 0.01 0.10 0.11 0.03 -0.10 standard error 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 lag 13 14 15 16 17 18 19 20 ccf -0.10 -0.02 0.01 -0.03 -0.02 0.12 0.19 0.14 standard error 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i ++++++++ i .13750 -19.000 i +++++++++++ i .19496 -18.000 i +++++++ i .11596 -17.000 i ++ i -0.18818E-01 -16.000 i +++ i -0.30566E-01 -15.000 i ++ i 0.11325E-01 -14.000 i ++ i -0.24968E-01 -13.000 i ++++++ i -0.96500E-01 -12.000 i ++++++ i -0.97421E-01 -11.000 i ++ i 0.27683E-01 -10.000 i +++++++ i .11036 -9.0000 i ++++++ i .10287 -8.0000 i + i 0.90269E-02 -7.0000 i ++++++ i -0.91534E-01 -6.0000 i +++++++ i -.12387 -5.0000 i ++++++ i -0.91776E-01 -4.0000 i ++++++++ i -.13719 -3.0000 i ++++++++ i -.13975 -2.0000 i +++++ i 0.71037E-01 -1.0000 i ++++++++++++++++++++++++++ i .50539 .00000 i +++++++++++++++++++++++++++++++++++++++++++++++++++i 1.0000 1.0000 i ++++++++++++++++++++++++++ i .50539 2.0000 i +++++ i 0.71037E-01 3.0000 i ++++++++ i -.13975 4.0000 i ++++++++ i -.13719 5.0000 i ++++++ i -0.91776E-01 6.0000 i +++++++ i -.12387 7.0000 i ++++++ i -0.91534E-01 8.0000 i + i 0.90269E-02 9.0000 i ++++++ i .10287 10.000 i +++++++ i .11036 11.000 i ++ i 0.27683E-01 12.000 i ++++++ i -0.97421E-01 13.000 i ++++++ i -0.96500E-01 14.000 i ++ i -0.24968E-01 15.000 i ++ i 0.11325E-01 16.000 i +++ i -0.30566E-01 17.000 i ++ i -0.18818E-01 18.000 i +++++++ i .11596 19.000 i +++++++++++ i .19496 20.000 i ++++++++ i .13750 1 starpac 2.08s (03/15/90) cross correlation analysis series 2 series 3 average of the series = .1578000 0.5379997E-01 standard deviation of the series = 1.361219 1.441247 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 0 ccf -0.07 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 13 14 15 16 17 18 19 20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i ++++ i -0.58556E-01 -19.000 i ++++++ i -.10988 -18.000 i +++++++ i -.12144 -17.000 i +++++ i -0.71337E-01 -16.000 i +++ i -0.38786E-01 -15.000 i +++ i -0.48143E-01 -14.000 i ++++++ i -0.98233E-01 -13.000 i +++ i -0.33208E-01 -12.000 i +++++ i 0.82117E-01 -11.000 i ++++++++ i .13368 -10.000 i ++++ i 0.55835E-01 -9.0000 i +++ i -0.34681E-01 -8.0000 i +++ i -0.49948E-01 -7.0000 i + i -0.39092E-02 -6.0000 i +++++++ i .12316 -5.0000 i +++++ i 0.79524E-01 -4.0000 i +++++++++++ i .19548 -3.0000 i +++++++++++++++++ i .32016 -2.0000 i ++++++++++++++++++++++++ i .45175 -1.0000 i +++++++++++++++++++++ i .40901 .00000 i +++++ i -0.74967E-01 1.0000 i ++++++++++++++++++++++++++ i -.49744 2.0000 i +++++++++++++++++++++++++ i -.48414 3.0000 i +++++++++++++++ i -.27815 4.0000 i +++++++ i -.11377 5.0000 i +++ i -0.49506E-01 6.0000 i +++ i 0.41613E-01 7.0000 i ++++++ i .10877 8.0000 i +++++ i 0.86854E-01 9.0000 i + i -0.55187E-02 10.000 i ++++ i -0.66454E-01 11.000 i ++++ i -0.66898E-01 12.000 i ++ i 0.16530E-01 13.000 i +++ i 0.39593E-01 14.000 i +++ i 0.42736E-01 15.000 i + i 0.12624E-02 16.000 i +++ i -0.48845E-01 17.000 i +++++ i 0.85199E-01 18.000 i ++++++++++ i .17455 19.000 i ++++++ i .10146 20.000 i +++++ i -0.77251E-01 1 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 4 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 0 ccf -0.07 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 1 starpac 2.08s (03/15/90) cross correlation analysis series 2 series 4 average of the series = .1578000 .1578000 standard deviation of the series = 1.361219 1.361219 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf -0.04 0.02 0.03 -0.05 -0.08 -0.06 0.01 0.17 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf 0.54 0.19 -0.06 -0.12 -0.08 -0.07 -0.05 0.01 0.05 -0.02 0.00 -0.06 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 lag 0 ccf 1.00 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.54 0.19 -0.06 -0.12 -0.08 -0.07 -0.05 0.01 0.05 -0.02 0.00 -0.06 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 lag 13 14 15 16 17 18 19 20 ccf -0.04 0.02 0.03 -0.05 -0.08 -0.06 0.01 0.17 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++++++ i .16672 -19.000 i ++ i 0.13947E-01 -18.000 i ++++ i -0.62115E-01 -17.000 i +++++ i -0.84303E-01 -16.000 i ++++ i -0.50382E-01 -15.000 i ++ i 0.25153E-01 -14.000 i ++ i 0.17623E-01 -13.000 i +++ i -0.42127E-01 -12.000 i ++++ i -0.59988E-01 -11.000 i + i 0.48686E-02 -10.000 i ++ i -0.20506E-01 -9.0000 i ++++ i 0.53381E-01 -8.0000 i + i 0.62074E-02 -7.0000 i +++ i -0.48306E-01 -6.0000 i ++++ i -0.66048E-01 -5.0000 i +++++ i -0.80109E-01 -4.0000 i +++++++ i -.12052 -3.0000 i ++++ i -0.55296E-01 -2.0000 i ++++++++++ i .18635 -1.0000 i ++++++++++++++++++++++++++++ i .53600 .00000 i +++++++++++++++++++++++++++++++++++++++++++++++++++i 1.0000 1.0000 i ++++++++++++++++++++++++++++ i .53600 2.0000 i ++++++++++ i .18635 3.0000 i ++++ i -0.55296E-01 4.0000 i +++++++ i -.12052 5.0000 i +++++ i -0.80109E-01 6.0000 i ++++ i -0.66048E-01 7.0000 i +++ i -0.48306E-01 8.0000 i + i 0.62074E-02 9.0000 i ++++ i 0.53381E-01 10.000 i ++ i -0.20506E-01 11.000 i + i 0.48686E-02 12.000 i ++++ i -0.59988E-01 13.000 i +++ i -0.42127E-01 14.000 i ++ i 0.17623E-01 15.000 i ++ i 0.25153E-01 16.000 i ++++ i -0.50382E-01 17.000 i +++++ i -0.84303E-01 18.000 i ++++ i -0.62115E-01 19.000 i ++ i 0.13947E-01 20.000 i +++++++++ i .16672 1 starpac 2.08s (03/15/90) cross correlation analysis series 3 series 4 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 3 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 0 ccf -0.07 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 3 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 ierr = 0 ccvf lag 1,1 1,2 1,3 1,4 2,1 2,2 2,3 2,4 3,1 3,2 3,3 3,4 4,1 4,2 4,3 4,4 0 2.0564 -0.1456 2.0564 -0.1456 -0.1456 1.8344 -0.1456 1.8344 2.0564 -0.1456 2.0564 -0.1456 -0.1456 1.8344 -0.1456 1.8344 1 1.0393 0.7944 1.0393 0.7944 -0.9661 0.9832 -0.9661 0.9832 1.0393 0.7944 1.0393 0.7944 -0.9661 0.9832 -0.9661 0.9832 2 0.1461 0.8774 0.1461 0.8774 -0.9403 0.3418 -0.9403 0.3418 0.1461 0.8774 0.1461 0.8774 -0.9403 0.3418 -0.9403 0.3418 3 -0.2874 0.6218 -0.2874 0.6218 -0.5402 -0.1014 -0.5402 -0.1014 -0.2874 0.6218 -0.2874 0.6218 -0.5402 -0.1014 -0.5402 -0.1014 4 -0.2821 0.3797 -0.2821 0.3797 -0.2210 -0.2211 -0.2210 -0.2211 -0.2821 0.3797 -0.2821 0.3797 -0.2210 -0.2211 -0.2210 -0.2211 5 -0.1887 0.1545 -0.1887 0.1545 -0.0962 -0.1470 -0.0962 -0.1470 -0.1887 0.1545 -0.1887 0.1545 -0.0962 -0.1470 -0.0962 -0.1470 6 -0.2547 0.2392 -0.2547 0.2392 0.0808 -0.1212 0.0808 -0.1212 -0.2547 0.2392 -0.2547 0.2392 0.0808 -0.1212 0.0808 -0.1212 7 -0.1882 -0.0076 -0.1882 -0.0076 0.2112 -0.0886 0.2112 -0.0886 -0.1882 -0.0076 -0.1882 -0.0076 0.2112 -0.0886 0.2112 -0.0886 8 0.0186 -0.0970 0.0186 -0.0970 0.1687 0.0114 0.1687 0.0114 0.0186 -0.0970 0.0186 -0.0970 0.1687 0.0114 0.1687 0.0114 9 0.2115 -0.0674 0.2115 -0.0674 -0.0107 0.0979 -0.0107 0.0979 0.2115 -0.0674 0.2115 -0.0674 -0.0107 0.0979 -0.0107 0.0979 10 0.2269 0.1084 0.2269 0.1084 -0.1291 -0.0376 -0.1291 -0.0376 0.2269 0.1084 0.2269 0.1084 -0.1291 -0.0376 -0.1291 -0.0376 11 0.0569 0.2596 0.0569 0.2596 -0.1299 0.0089 -0.1299 0.0089 0.0569 0.2596 0.0569 0.2596 -0.1299 0.0089 -0.1299 0.0089 12 -0.2003 0.1595 -0.2003 0.1595 0.0321 -0.1100 0.0321 -0.1100 -0.2003 0.1595 -0.2003 0.1595 0.0321 -0.1100 0.0321 -0.1100 13 -0.1984 -0.0645 -0.1984 -0.0645 0.0769 -0.0773 0.0769 -0.0773 -0.1984 -0.0645 -0.1984 -0.0645 0.0769 -0.0773 0.0769 -0.0773 14 -0.0513 -0.1908 -0.0513 -0.1908 0.0830 0.0323 0.0830 0.0323 -0.0513 -0.1908 -0.0513 -0.1908 0.0830 0.0323 0.0830 0.0323 15 0.0233 -0.0935 0.0233 -0.0935 0.0025 0.0461 0.0025 0.0461 0.0233 -0.0935 0.0233 -0.0935 0.0025 0.0461 0.0025 0.0461 16 -0.0629 -0.0753 -0.0629 -0.0753 -0.0949 -0.0924 -0.0949 -0.0924 -0.0629 -0.0753 -0.0629 -0.0753 -0.0949 -0.0924 -0.0949 -0.0924 17 -0.0387 -0.1386 -0.0387 -0.1386 0.1655 -0.1546 0.1655 -0.1546 -0.0387 -0.1386 -0.0387 -0.1386 0.1655 -0.1546 0.1655 -0.1546 18 0.2385 -0.2359 0.2385 -0.2359 0.3390 -0.1139 0.3390 -0.1139 0.2385 -0.2359 0.2385 -0.2359 0.3390 -0.1139 0.3390 -0.1139 19 0.4009 -0.2134 0.4009 -0.2134 0.1971 0.0256 0.1971 0.0256 0.4009 -0.2134 0.4009 -0.2134 0.1971 0.0256 0.1971 0.0256 20 0.2828 -0.1137 0.2828 -0.1137 -0.1500 0.3058 -0.1500 0.3058 0.2828 -0.1137 0.2828 -0.1137 -0.1500 0.3058 -0.1500 0.3058 ccf lag 1,1 1,2 1,3 1,4 2,1 2,2 2,3 2,4 3,1 3,2 3,3 3,4 4,1 4,2 4,3 4,4 0 1.0000 -0.0750 1.0000 -0.0750 -0.0750 1.0000 -0.0750 1.0000 1.0000 -0.0750 1.0000 -0.0750 -0.0750 1.0000 -0.0750 1.0000 1 0.5054 0.4090 0.5054 0.4090 -0.4974 0.5360 -0.4974 0.5360 0.5054 0.4090 0.5054 0.4090 -0.4974 0.5360 -0.4974 0.5360 2 0.0710 0.4517 0.0710 0.4517 -0.4841 0.1863 -0.4841 0.1863 0.0710 0.4517 0.0710 0.4517 -0.4841 0.1863 -0.4841 0.1863 3 -0.1397 0.3202 -0.1397 0.3202 -0.2782 -0.0553 -0.2782 -0.0553 -0.1397 0.3202 -0.1397 0.3202 -0.2782 -0.0553 -0.2782 -0.0553 4 -0.1372 0.1955 -0.1372 0.1955 -0.1138 -0.1205 -0.1138 -0.1205 -0.1372 0.1955 -0.1372 0.1955 -0.1138 -0.1205 -0.1138 -0.1205 5 -0.0918 0.0795 -0.0918 0.0795 -0.0495 -0.0801 -0.0495 -0.0801 -0.0918 0.0795 -0.0918 0.0795 -0.0495 -0.0801 -0.0495 -0.0801 6 -0.1239 0.1232 -0.1239 0.1232 0.0416 -0.0660 0.0416 -0.0660 -0.1239 0.1232 -0.1239 0.1232 0.0416 -0.0660 0.0416 -0.0660 7 -0.0915 -0.0039 -0.0915 -0.0039 0.1088 -0.0483 0.1088 -0.0483 -0.0915 -0.0039 -0.0915 -0.0039 0.1088 -0.0483 0.1088 -0.0483 8 0.0090 -0.0499 0.0090 -0.0499 0.0869 0.0062 0.0869 0.0062 0.0090 -0.0499 0.0090 -0.0499 0.0869 0.0062 0.0869 0.0062 9 0.1029 -0.0347 0.1029 -0.0347 -0.0055 0.0534 -0.0055 0.0534 0.1029 -0.0347 0.1029 -0.0347 -0.0055 0.0534 -0.0055 0.0534 10 0.1104 0.0558 0.1104 0.0558 -0.0665 -0.0205 -0.0665 -0.0205 0.1104 0.0558 0.1104 0.0558 -0.0665 -0.0205 -0.0665 -0.0205 11 0.0277 0.1337 0.0277 0.1337 -0.0669 0.0049 -0.0669 0.0049 0.0277 0.1337 0.0277 0.1337 -0.0669 0.0049 -0.0669 0.0049 12 -0.0974 0.0821 -0.0974 0.0821 0.0165 -0.0600 0.0165 -0.0600 -0.0974 0.0821 -0.0974 0.0821 0.0165 -0.0600 0.0165 -0.0600 13 -0.0965 -0.0332 -0.0965 -0.0332 0.0396 -0.0421 0.0396 -0.0421 -0.0965 -0.0332 -0.0965 -0.0332 0.0396 -0.0421 0.0396 -0.0421 14 -0.0250 -0.0982 -0.0250 -0.0982 0.0427 0.0176 0.0427 0.0176 -0.0250 -0.0982 -0.0250 -0.0982 0.0427 0.0176 0.0427 0.0176 15 0.0113 -0.0481 0.0113 -0.0481 0.0013 0.0252 0.0013 0.0252 0.0113 -0.0481 0.0113 -0.0481 0.0013 0.0252 0.0013 0.0252 16 -0.0306 -0.0388 -0.0306 -0.0388 -0.0488 -0.0504 -0.0488 -0.0504 -0.0306 -0.0388 -0.0306 -0.0388 -0.0488 -0.0504 -0.0488 -0.0504 17 -0.0188 -0.0713 -0.0188 -0.0713 0.0852 -0.0843 0.0852 -0.0843 -0.0188 -0.0713 -0.0188 -0.0713 0.0852 -0.0843 0.0852 -0.0843 18 0.1160 -0.1214 0.1160 -0.1214 0.1745 -0.0621 0.1745 -0.0621 0.1160 -0.1214 0.1160 -0.1214 0.1745 -0.0621 0.1745 -0.0621 19 0.1950 -0.1099 0.1950 -0.1099 0.1015 0.0139 0.1015 0.0139 0.1950 -0.1099 0.1950 -0.1099 0.1015 0.0139 0.1015 0.0139 20 0.1375 -0.0586 0.1375 -0.0586 -0.0773 0.1667 -0.0773 0.1667 0.1375 -0.0586 0.1375 -0.0586 -0.0773 0.1667 -0.0773 0.1667 test of ccfm without missing values LACOV = 101 LAGMAX = 33 N = 100 LACOV = 101 LAGMAX = 33 N = 100 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 number of missing observations = 0 0 percentage of observations missing = 0.0000 0.0000 largest lag value to be used = 33 missing value code = 1.160000 1.160000 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -25 -26 -27 -28 -29 -30 -31 -32 -33 ccf -0.06 0.00 0.08 0.06 0.03 -0.06 -0.05 0.05 0.06 standard error 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 no. of obs. used 75 74 73 72 71 70 69 68 67 lag -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 -0.10 -0.04 0.04 -0.01 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 79 78 77 76 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 0 ccf -0.07 standard error 0.10 no. of obs. used 100 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 13 14 15 16 17 18 19 20 21 22 23 24 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 0.04 0.07 0.16 0.23 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 79 78 77 76 lag 25 26 27 28 29 30 31 32 33 ccf 0.25 0.15 0.03 -0.04 -0.03 -0.02 0.00 -0.11 -0.12 standard error 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 no. of obs. used 75 74 73 72 71 70 69 68 67 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -33.000 i ++++ i 0.62185E-01 -32.000 i ++++ i 0.51762E-01 -31.000 i +++ i -0.49269E-01 -30.000 i ++++ i -0.64776E-01 -29.000 i ++ i 0.26418E-01 -28.000 i ++++ i 0.62819E-01 -27.000 i +++++ i 0.81593E-01 -26.000 i + i 0.11350E-02 -25.000 i ++++ i -0.64341E-01 -24.000 i ++ i -0.13985E-01 -23.000 i +++ i 0.43281E-01 -22.000 i +++ i -0.41656E-01 -21.000 i ++++++ i -.10226 -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 21.000 i +++ i 0.44491E-01 22.000 i +++++ i 0.72656E-01 23.000 i +++++++++ i .15576 24.000 i +++++++++++++ i .23426 25.000 i +++++++++++++ i .24505 26.000 i +++++++++ i .15307 27.000 i ++ i 0.26330E-01 28.000 i +++ i -0.36697E-01 29.000 i ++ i -0.27453E-01 30.000 i ++ i -0.19839E-01 31.000 i + i 0.33872E-02 32.000 i +++++++ i -.11343 33.000 i +++++++ i -.12325 ierr = 0 test of ccfms without missing values LACOV = 21 LAGMAX = 20 N = 100 LACOV = 21 LAGMAX = 20 N = 100 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 number of missing observations = 0 0 percentage of observations missing = 0.0000 0.0000 largest lag value to be used = 20 missing value code = 1.160000 1.160000 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 0 ccf -0.07 standard error 0.10 no. of obs. used 100 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 LACOV = 21 LAGMAX = 20 N = 100 1 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 3 average of the series = 0.5379997E-01 0.5379997E-01 standard deviation of the series = 1.441247 1.441247 number of time points = 100 100 number of missing observations = 0 0 percentage of observations missing = 0.0000 0.0000 largest lag value to be used = 20 missing value code = 1.160000 1.160000 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf -0.10 -0.02 0.01 -0.03 -0.02 0.12 0.19 0.14 standard error 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf 0.51 0.07 -0.14 -0.14 -0.09 -0.12 -0.09 0.01 0.10 0.11 0.03 -0.10 standard error 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 0 ccf 1.00 standard error 0.10 no. of obs. used 100 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.51 0.07 -0.14 -0.14 -0.09 -0.12 -0.09 0.01 0.10 0.11 0.03 -0.10 standard error 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 13 14 15 16 17 18 19 20 ccf -0.10 -0.02 0.01 -0.03 -0.02 0.12 0.19 0.14 standard error 0.13 0.13 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i ++++++++ i .13750 -19.000 i +++++++++++ i .19496 -18.000 i +++++++ i .11596 -17.000 i ++ i -0.18818E-01 -16.000 i +++ i -0.30566E-01 -15.000 i ++ i 0.11325E-01 -14.000 i ++ i -0.24968E-01 -13.000 i ++++++ i -0.96500E-01 -12.000 i ++++++ i -0.97421E-01 -11.000 i ++ i 0.27683E-01 -10.000 i +++++++ i .11036 -9.0000 i ++++++ i .10287 -8.0000 i + i 0.90269E-02 -7.0000 i ++++++ i -0.91534E-01 -6.0000 i +++++++ i -.12387 -5.0000 i ++++++ i -0.91776E-01 -4.0000 i ++++++++ i -.13719 -3.0000 i ++++++++ i -.13975 -2.0000 i +++++ i 0.71037E-01 -1.0000 i ++++++++++++++++++++++++++ i .50539 .00000 i +++++++++++++++++++++++++++++++++++++++++++++++++++i 1.0000 1.0000 i ++++++++++++++++++++++++++ i .50539 2.0000 i +++++ i 0.71037E-01 3.0000 i ++++++++ i -.13975 4.0000 i ++++++++ i -.13719 5.0000 i ++++++ i -0.91776E-01 6.0000 i +++++++ i -.12387 7.0000 i ++++++ i -0.91534E-01 8.0000 i + i 0.90269E-02 9.0000 i ++++++ i .10287 10.000 i +++++++ i .11036 11.000 i ++ i 0.27683E-01 12.000 i ++++++ i -0.97421E-01 13.000 i ++++++ i -0.96500E-01 14.000 i ++ i -0.24968E-01 15.000 i ++ i 0.11325E-01 16.000 i +++ i -0.30566E-01 17.000 i ++ i -0.18818E-01 18.000 i +++++++ i .11596 19.000 i +++++++++++ i .19496 20.000 i ++++++++ i .13750 1 starpac 2.08s (03/15/90) cross correlation analysis series 2 series 3 average of the series = .1578000 0.5379997E-01 standard deviation of the series = 1.361219 1.441247 number of time points = 100 100 number of missing observations = 0 0 percentage of observations missing = 0.0000 0.0000 largest lag value to be used = 20 missing value code = 1.160000 1.160000 cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 0 ccf -0.07 standard error 0.10 no. of obs. used 100 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 13 14 15 16 17 18 19 20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 3 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i ++++ i -0.58556E-01 -19.000 i ++++++ i -.10988 -18.000 i +++++++ i -.12144 -17.000 i +++++ i -0.71337E-01 -16.000 i +++ i -0.38786E-01 -15.000 i +++ i -0.48143E-01 -14.000 i ++++++ i -0.98233E-01 -13.000 i +++ i -0.33208E-01 -12.000 i +++++ i 0.82117E-01 -11.000 i ++++++++ i .13368 -10.000 i ++++ i 0.55835E-01 -9.0000 i +++ i -0.34681E-01 -8.0000 i +++ i -0.49948E-01 -7.0000 i + i -0.39092E-02 -6.0000 i +++++++ i .12316 -5.0000 i +++++ i 0.79524E-01 -4.0000 i +++++++++++ i .19548 -3.0000 i +++++++++++++++++ i .32016 -2.0000 i ++++++++++++++++++++++++ i .45175 -1.0000 i +++++++++++++++++++++ i .40901 .00000 i +++++ i -0.74967E-01 1.0000 i ++++++++++++++++++++++++++ i -.49744 2.0000 i +++++++++++++++++++++++++ i -.48414 3.0000 i +++++++++++++++ i -.27815 4.0000 i +++++++ i -.11377 5.0000 i +++ i -0.49506E-01 6.0000 i +++ i 0.41613E-01 7.0000 i ++++++ i .10877 8.0000 i +++++ i 0.86854E-01 9.0000 i + i -0.55187E-02 10.000 i ++++ i -0.66454E-01 11.000 i ++++ i -0.66898E-01 12.000 i ++ i 0.16530E-01 13.000 i +++ i 0.39593E-01 14.000 i +++ i 0.42736E-01 15.000 i + i 0.12624E-02 16.000 i +++ i -0.48845E-01 17.000 i +++++ i 0.85199E-01 18.000 i ++++++++++ i .17455 19.000 i ++++++ i .10146 20.000 i +++++ i -0.77251E-01 LACOV = 21 LAGMAX = 20 N = 100 1 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 4 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 number of missing observations = 0 0 percentage of observations missing = 0.0000 0.0000 largest lag value to be used = 20 missing value code = 1.160000 1.160000 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 0 ccf -0.07 standard error 0.10 no. of obs. used 100 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 1 starpac 2.08s (03/15/90) cross correlation analysis series 2 series 4 average of the series = .1578000 .1578000 standard deviation of the series = 1.361219 1.361219 number of time points = 100 100 number of missing observations = 0 0 percentage of observations missing = 0.0000 0.0000 largest lag value to be used = 20 missing value code = 1.160000 1.160000 cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf -0.04 0.02 0.03 -0.05 -0.08 -0.06 0.01 0.17 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf 0.54 0.19 -0.06 -0.12 -0.08 -0.07 -0.05 0.01 0.05 -0.02 0.00 -0.06 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 0 ccf 1.00 standard error 0.10 no. of obs. used 100 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.54 0.19 -0.06 -0.12 -0.08 -0.07 -0.05 0.01 0.05 -0.02 0.00 -0.06 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 13 14 15 16 17 18 19 20 ccf -0.04 0.02 0.03 -0.05 -0.08 -0.06 0.01 0.17 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 2 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++++++ i .16672 -19.000 i ++ i 0.13947E-01 -18.000 i ++++ i -0.62115E-01 -17.000 i +++++ i -0.84303E-01 -16.000 i ++++ i -0.50382E-01 -15.000 i ++ i 0.25153E-01 -14.000 i ++ i 0.17623E-01 -13.000 i +++ i -0.42127E-01 -12.000 i ++++ i -0.59988E-01 -11.000 i + i 0.48686E-02 -10.000 i ++ i -0.20506E-01 -9.0000 i ++++ i 0.53381E-01 -8.0000 i + i 0.62074E-02 -7.0000 i +++ i -0.48306E-01 -6.0000 i ++++ i -0.66048E-01 -5.0000 i +++++ i -0.80109E-01 -4.0000 i +++++++ i -.12052 -3.0000 i ++++ i -0.55296E-01 -2.0000 i ++++++++++ i .18635 -1.0000 i ++++++++++++++++++++++++++++ i .53600 .00000 i +++++++++++++++++++++++++++++++++++++++++++++++++++i 1.0000 1.0000 i ++++++++++++++++++++++++++++ i .53600 2.0000 i ++++++++++ i .18635 3.0000 i ++++ i -0.55296E-01 4.0000 i +++++++ i -.12052 5.0000 i +++++ i -0.80109E-01 6.0000 i ++++ i -0.66048E-01 7.0000 i +++ i -0.48306E-01 8.0000 i + i 0.62074E-02 9.0000 i ++++ i 0.53381E-01 10.000 i ++ i -0.20506E-01 11.000 i + i 0.48686E-02 12.000 i ++++ i -0.59988E-01 13.000 i +++ i -0.42127E-01 14.000 i ++ i 0.17623E-01 15.000 i ++ i 0.25153E-01 16.000 i ++++ i -0.50382E-01 17.000 i +++++ i -0.84303E-01 18.000 i ++++ i -0.62115E-01 19.000 i ++ i 0.13947E-01 20.000 i +++++++++ i .16672 1 starpac 2.08s (03/15/90) cross correlation analysis series 3 series 4 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 number of missing observations = 0 0 percentage of observations missing = 0.0000 0.0000 largest lag value to be used = 20 missing value code = 1.160000 1.160000 cross correlation function estimate (ccf) ccf correlates series 3 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 0 ccf -0.07 standard error 0.10 no. of obs. used 100 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 no. of obs. used 99 98 97 96 95 94 93 92 91 90 89 88 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 87 86 85 84 83 82 81 80 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 3 at time t with series 4 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55187E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 ierr = 0 ccvf lag 1,1 1,2 1,3 1,4 2,1 2,2 2,3 2,4 3,1 3,2 3,3 3,4 4,1 4,2 4,3 4,4 0 2.0564 -0.1456 2.0564 -0.1456 -0.1456 1.8344 -0.1456 1.8344 2.0564 -0.1456 2.0564 -0.1456 -0.1456 1.8344 -0.1456 1.8344 1 1.0393 0.7944 1.0393 0.7944 -0.9661 0.9832 -0.9661 0.9832 1.0393 0.7944 1.0393 0.7944 -0.9661 0.9832 -0.9661 0.9832 2 0.1461 0.8774 0.1461 0.8774 -0.9403 0.3418 -0.9403 0.3418 0.1461 0.8774 0.1461 0.8774 -0.9403 0.3418 -0.9403 0.3418 3 -0.2874 0.6218 -0.2874 0.6218 -0.5402 -0.1014 -0.5402 -0.1014 -0.2874 0.6218 -0.2874 0.6218 -0.5402 -0.1014 -0.5402 -0.1014 4 -0.2821 0.3797 -0.2821 0.3797 -0.2210 -0.2211 -0.2210 -0.2211 -0.2821 0.3797 -0.2821 0.3797 -0.2210 -0.2211 -0.2210 -0.2211 5 -0.1887 0.1545 -0.1887 0.1545 -0.0962 -0.1470 -0.0962 -0.1470 -0.1887 0.1545 -0.1887 0.1545 -0.0962 -0.1470 -0.0962 -0.1470 6 -0.2547 0.2392 -0.2547 0.2392 0.0808 -0.1212 0.0808 -0.1212 -0.2547 0.2392 -0.2547 0.2392 0.0808 -0.1212 0.0808 -0.1212 7 -0.1882 -0.0076 -0.1882 -0.0076 0.2112 -0.0886 0.2112 -0.0886 -0.1882 -0.0076 -0.1882 -0.0076 0.2112 -0.0886 0.2112 -0.0886 8 0.0186 -0.0970 0.0186 -0.0970 0.1687 0.0114 0.1687 0.0114 0.0186 -0.0970 0.0186 -0.0970 0.1687 0.0114 0.1687 0.0114 9 0.2115 -0.0674 0.2115 -0.0674 -0.0107 0.0979 -0.0107 0.0979 0.2115 -0.0674 0.2115 -0.0674 -0.0107 0.0979 -0.0107 0.0979 10 0.2269 0.1084 0.2269 0.1084 -0.1291 -0.0376 -0.1291 -0.0376 0.2269 0.1084 0.2269 0.1084 -0.1291 -0.0376 -0.1291 -0.0376 11 0.0569 0.2596 0.0569 0.2596 -0.1299 0.0089 -0.1299 0.0089 0.0569 0.2596 0.0569 0.2596 -0.1299 0.0089 -0.1299 0.0089 12 -0.2003 0.1595 -0.2003 0.1595 0.0321 -0.1100 0.0321 -0.1100 -0.2003 0.1595 -0.2003 0.1595 0.0321 -0.1100 0.0321 -0.1100 13 -0.1984 -0.0645 -0.1984 -0.0645 0.0769 -0.0773 0.0769 -0.0773 -0.1984 -0.0645 -0.1984 -0.0645 0.0769 -0.0773 0.0769 -0.0773 14 -0.0513 -0.1908 -0.0513 -0.1908 0.0830 0.0323 0.0830 0.0323 -0.0513 -0.1908 -0.0513 -0.1908 0.0830 0.0323 0.0830 0.0323 15 0.0233 -0.0935 0.0233 -0.0935 0.0025 0.0461 0.0025 0.0461 0.0233 -0.0935 0.0233 -0.0935 0.0025 0.0461 0.0025 0.0461 16 -0.0629 -0.0753 -0.0629 -0.0753 -0.0949 -0.0924 -0.0949 -0.0924 -0.0629 -0.0753 -0.0629 -0.0753 -0.0949 -0.0924 -0.0949 -0.0924 17 -0.0387 -0.1386 -0.0387 -0.1386 0.1655 -0.1546 0.1655 -0.1546 -0.0387 -0.1386 -0.0387 -0.1386 0.1655 -0.1546 0.1655 -0.1546 18 0.2385 -0.2359 0.2385 -0.2359 0.3390 -0.1139 0.3390 -0.1139 0.2385 -0.2359 0.2385 -0.2359 0.3390 -0.1139 0.3390 -0.1139 19 0.4009 -0.2134 0.4009 -0.2134 0.1971 0.0256 0.1971 0.0256 0.4009 -0.2134 0.4009 -0.2134 0.1971 0.0256 0.1971 0.0256 20 0.2828 -0.1137 0.2828 -0.1137 -0.1500 0.3058 -0.1500 0.3058 0.2828 -0.1137 0.2828 -0.1137 -0.1500 0.3058 -0.1500 0.3058 ccf lag 1,1 1,2 1,3 1,4 2,1 2,2 2,3 2,4 3,1 3,2 3,3 3,4 4,1 4,2 4,3 4,4 0 1.0000 -0.0750 1.0000 -0.0750 -0.0750 1.0000 -0.0750 1.0000 1.0000 -0.0750 1.0000 -0.0750 -0.0750 1.0000 -0.0750 1.0000 1 0.5054 0.4090 0.5054 0.4090 -0.4974 0.5360 -0.4974 0.5360 0.5054 0.4090 0.5054 0.4090 -0.4974 0.5360 -0.4974 0.5360 2 0.0710 0.4517 0.0710 0.4517 -0.4841 0.1863 -0.4841 0.1863 0.0710 0.4517 0.0710 0.4517 -0.4841 0.1863 -0.4841 0.1863 3 -0.1397 0.3202 -0.1397 0.3202 -0.2782 -0.0553 -0.2782 -0.0553 -0.1397 0.3202 -0.1397 0.3202 -0.2782 -0.0553 -0.2782 -0.0553 4 -0.1372 0.1955 -0.1372 0.1955 -0.1138 -0.1205 -0.1138 -0.1205 -0.1372 0.1955 -0.1372 0.1955 -0.1138 -0.1205 -0.1138 -0.1205 5 -0.0918 0.0795 -0.0918 0.0795 -0.0495 -0.0801 -0.0495 -0.0801 -0.0918 0.0795 -0.0918 0.0795 -0.0495 -0.0801 -0.0495 -0.0801 6 -0.1239 0.1232 -0.1239 0.1232 0.0416 -0.0660 0.0416 -0.0660 -0.1239 0.1232 -0.1239 0.1232 0.0416 -0.0660 0.0416 -0.0660 7 -0.0915 -0.0039 -0.0915 -0.0039 0.1088 -0.0483 0.1088 -0.0483 -0.0915 -0.0039 -0.0915 -0.0039 0.1088 -0.0483 0.1088 -0.0483 8 0.0090 -0.0499 0.0090 -0.0499 0.0869 0.0062 0.0869 0.0062 0.0090 -0.0499 0.0090 -0.0499 0.0869 0.0062 0.0869 0.0062 9 0.1029 -0.0347 0.1029 -0.0347 -0.0055 0.0534 -0.0055 0.0534 0.1029 -0.0347 0.1029 -0.0347 -0.0055 0.0534 -0.0055 0.0534 10 0.1104 0.0558 0.1104 0.0558 -0.0665 -0.0205 -0.0665 -0.0205 0.1104 0.0558 0.1104 0.0558 -0.0665 -0.0205 -0.0665 -0.0205 11 0.0277 0.1337 0.0277 0.1337 -0.0669 0.0049 -0.0669 0.0049 0.0277 0.1337 0.0277 0.1337 -0.0669 0.0049 -0.0669 0.0049 12 -0.0974 0.0821 -0.0974 0.0821 0.0165 -0.0600 0.0165 -0.0600 -0.0974 0.0821 -0.0974 0.0821 0.0165 -0.0600 0.0165 -0.0600 13 -0.0965 -0.0332 -0.0965 -0.0332 0.0396 -0.0421 0.0396 -0.0421 -0.0965 -0.0332 -0.0965 -0.0332 0.0396 -0.0421 0.0396 -0.0421 14 -0.0250 -0.0982 -0.0250 -0.0982 0.0427 0.0176 0.0427 0.0176 -0.0250 -0.0982 -0.0250 -0.0982 0.0427 0.0176 0.0427 0.0176 15 0.0113 -0.0481 0.0113 -0.0481 0.0013 0.0252 0.0013 0.0252 0.0113 -0.0481 0.0113 -0.0481 0.0013 0.0252 0.0013 0.0252 16 -0.0306 -0.0388 -0.0306 -0.0388 -0.0488 -0.0504 -0.0488 -0.0504 -0.0306 -0.0388 -0.0306 -0.0388 -0.0488 -0.0504 -0.0488 -0.0504 17 -0.0188 -0.0713 -0.0188 -0.0713 0.0852 -0.0843 0.0852 -0.0843 -0.0188 -0.0713 -0.0188 -0.0713 0.0852 -0.0843 0.0852 -0.0843 18 0.1160 -0.1214 0.1160 -0.1214 0.1745 -0.0621 0.1745 -0.0621 0.1160 -0.1214 0.1160 -0.1214 0.1745 -0.0621 0.1745 -0.0621 19 0.1950 -0.1099 0.1950 -0.1099 0.1015 0.0139 0.1015 0.0139 0.1950 -0.1099 0.1950 -0.1099 0.1015 0.0139 0.1015 0.0139 20 0.1375 -0.0586 0.1375 -0.0586 -0.0773 0.1667 -0.0773 0.1667 0.1375 -0.0586 0.1375 -0.0586 -0.0773 0.1667 -0.0773 0.1667 nlppc lag 1,1 1,2 1,3 1,4 2,1 2,2 2,3 2,4 3,1 3,2 3,3 3,4 4,1 4,2 4,3 4,4 0 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 1 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 2 98 98 98 98 98 98 98 98 98 98 98 98 98 98 98 98 3 97 97 97 97 97 97 97 97 97 97 97 97 97 97 97 97 4 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 96 5 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 95 6 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 7 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 8 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 92 9 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 91 10 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 11 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 89 12 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 88 13 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 87 14 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 86 15 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 85 16 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 17 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 18 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 82 19 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 20 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 80 test of ccff starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 largest lag value to be used = 33 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -25 -26 -27 -28 -29 -30 -31 -32 -33 ccf -0.06 0.00 0.08 0.06 0.03 -0.06 -0.05 0.05 0.06 standard error 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 lag -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 -0.10 -0.04 0.04 -0.01 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 lag 0 ccf -0.07 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 lag 13 14 15 16 17 18 19 20 21 22 23 24 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 0.04 0.07 0.16 0.23 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag 25 26 27 28 29 30 31 32 33 ccf 0.25 0.15 0.03 -0.04 -0.03 -0.02 0.00 -0.11 -0.12 standard error 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -33.000 i ++++ i 0.62185E-01 -32.000 i ++++ i 0.51762E-01 -31.000 i +++ i -0.49269E-01 -30.000 i ++++ i -0.64776E-01 -29.000 i ++ i 0.26419E-01 -28.000 i ++++ i 0.62819E-01 -27.000 i +++++ i 0.81593E-01 -26.000 i + i 0.11350E-02 -25.000 i ++++ i -0.64341E-01 -24.000 i ++ i -0.13985E-01 -23.000 i +++ i 0.43281E-01 -22.000 i +++ i -0.41656E-01 -21.000 i ++++++ i -.10226 -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39593E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66453E-01 -9.0000 i + i -0.55186E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98234E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 21.000 i +++ i 0.44491E-01 22.000 i +++++ i 0.72656E-01 23.000 i +++++++++ i .15576 24.000 i +++++++++++++ i .23426 25.000 i +++++++++++++ i .24505 26.000 i +++++++++ i .15307 27.000 i ++ i 0.26330E-01 28.000 i +++ i -0.36697E-01 29.000 i ++ i -0.27453E-01 30.000 i ++ i -0.19839E-01 31.000 i + i 0.33871E-02 32.000 i +++++++ i -.11343 33.000 i +++++++ i -.12325 ierr = 0 test of ccffs starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.5379997E-01 .1578000 standard deviation of the series = 1.441247 1.361219 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.04 0.00 -0.05 0.09 0.17 0.10 -0.08 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.48 -0.28 -0.11 -0.05 0.04 0.11 0.09 -0.01 -0.07 -0.07 0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 0 ccf -0.07 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.45 0.32 0.20 0.08 0.12 -0.00 -0.05 -0.03 0.06 0.13 0.08 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 0.12 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.05 -0.04 -0.07 -0.12 -0.11 -0.06 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.77251E-01 -19.000 i ++++++ i .10146 -18.000 i ++++++++++ i .17455 -17.000 i +++++ i 0.85199E-01 -16.000 i +++ i -0.48845E-01 -15.000 i + i 0.12624E-02 -14.000 i +++ i 0.42736E-01 -13.000 i +++ i 0.39594E-01 -12.000 i ++ i 0.16530E-01 -11.000 i ++++ i -0.66898E-01 -10.000 i ++++ i -0.66454E-01 -9.0000 i + i -0.55186E-02 -8.0000 i +++++ i 0.86854E-01 -7.0000 i ++++++ i .10877 -6.0000 i +++ i 0.41613E-01 -5.0000 i +++ i -0.49506E-01 -4.0000 i +++++++ i -.11377 -3.0000 i +++++++++++++++ i -.27815 -2.0000 i +++++++++++++++++++++++++ i -.48414 -1.0000 i ++++++++++++++++++++++++++ i -.49744 .00000 i +++++ i -0.74967E-01 1.0000 i +++++++++++++++++++++ i .40901 2.0000 i ++++++++++++++++++++++++ i .45175 3.0000 i +++++++++++++++++ i .32016 4.0000 i +++++++++++ i .19548 5.0000 i +++++ i 0.79524E-01 6.0000 i +++++++ i .12316 7.0000 i + i -0.39092E-02 8.0000 i +++ i -0.49948E-01 9.0000 i +++ i -0.34681E-01 10.000 i ++++ i 0.55835E-01 11.000 i ++++++++ i .13368 12.000 i +++++ i 0.82117E-01 13.000 i +++ i -0.33208E-01 14.000 i ++++++ i -0.98233E-01 15.000 i +++ i -0.48143E-01 16.000 i +++ i -0.38786E-01 17.000 i +++++ i -0.71337E-01 18.000 i +++++++ i -.12144 19.000 i ++++++ i -.10988 20.000 i ++++ i -0.58556E-01 ierr = 0 ccvf lag 1,1 1,2 2,1 2,2 0 2.0564 -0.1456 -0.1456 1.8344 1 1.0393 0.7944 -0.9661 0.9832 2 0.1461 0.8774 -0.9403 0.3418 3 -0.2874 0.6218 -0.5402 -0.1014 4 -0.2821 0.3797 -0.2210 -0.2211 5 -0.1887 0.1545 -0.0962 -0.1470 6 -0.2547 0.2392 0.0808 -0.1212 7 -0.1882 -0.0076 0.2112 -0.0886 8 0.0186 -0.0970 0.1687 0.0114 9 0.2115 -0.0674 -0.0107 0.0979 10 0.2269 0.1084 -0.1291 -0.0376 11 0.0569 0.2596 -0.1299 0.0089 12 -0.2003 0.1595 0.0321 -0.1100 13 -0.1984 -0.0645 0.0769 -0.0773 14 -0.0513 -0.1908 0.0830 0.0323 15 0.0233 -0.0935 0.0025 0.0461 16 -0.0629 -0.0753 -0.0949 -0.0924 17 -0.0387 -0.1386 0.1655 -0.1546 18 0.2385 -0.2359 0.3390 -0.1139 19 0.4009 -0.2134 0.1971 0.0256 20 0.2828 -0.1137 -0.1500 0.3058 ccf lag 1,1 1,2 2,1 2,2 0 1.0000 -0.0750 -0.0750 1.0000 1 0.5054 0.4090 -0.4974 0.5360 2 0.0710 0.4517 -0.4841 0.1863 3 -0.1397 0.3202 -0.2782 -0.0553 4 -0.1372 0.1955 -0.1138 -0.1205 5 -0.0918 0.0795 -0.0495 -0.0801 6 -0.1239 0.1232 0.0416 -0.0660 7 -0.0915 -0.0039 0.1088 -0.0483 8 0.0090 -0.0499 0.0869 0.0062 9 0.1029 -0.0347 -0.0055 0.0534 10 0.1104 0.0558 -0.0665 -0.0205 11 0.0277 0.1337 -0.0669 0.0049 12 -0.0974 0.0821 0.0165 -0.0600 13 -0.0965 -0.0332 0.0396 -0.0421 14 -0.0250 -0.0982 0.0427 0.0176 15 0.0113 -0.0481 0.0013 0.0252 16 -0.0306 -0.0388 -0.0488 -0.0504 17 -0.0188 -0.0713 0.0852 -0.0843 18 0.1160 -0.1214 0.1745 -0.0621 19 0.1950 -0.1099 0.1015 0.0139 20 0.1375 -0.0586 -0.0773 0.1667 test of ccfm with missing values LACOV = 101 LAGMAX = 33 N = 100 LACOV = 101 LAGMAX = 33 N = 100 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.2793813E-01 .1504041 standard deviation of the series = 1.478219 1.373007 number of time points = 100 100 number of missing observations = 3 1 percentage of observations missing = 3.0000 1.0000 largest lag value to be used = 33 missing value code = .8900000 .8900000 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -25 -26 -27 -28 -29 -30 -31 -32 -33 ccf -0.08 0.00 0.08 0.07 0.03 -0.06 -0.05 0.04 0.07 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 no. of obs. used 72 71 70 69 68 67 66 65 64 lag -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 ccf 0.04 0.03 -0.00 -0.06 0.08 0.17 0.09 -0.09 -0.11 -0.04 0.05 -0.02 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.12 0.12 no. of obs. used 83 83 81 80 79 78 77 77 76 75 74 73 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.50 -0.28 -0.09 -0.04 0.05 0.12 0.10 0.01 -0.07 -0.08 0.01 standard error 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 no. of obs. used 95 94 93 92 91 90 89 88 87 86 86 84 lag 0 ccf -0.07 standard error 0.10 no. of obs. used 96 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.43 0.46 0.31 0.19 0.08 0.12 0.01 -0.04 -0.03 0.05 0.13 0.09 standard error 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 0.13 no. of obs. used 95 94 93 92 91 90 89 88 87 86 85 84 lag 13 14 15 16 17 18 19 20 21 22 23 24 ccf -0.03 -0.10 -0.06 -0.05 -0.08 -0.14 -0.13 -0.06 0.03 0.05 0.16 0.25 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 no. of obs. used 83 82 81 80 79 78 77 76 75 74 73 72 lag 25 26 27 28 29 30 31 32 33 ccf 0.25 0.15 0.03 -0.03 -0.04 -0.04 -0.01 -0.12 -0.11 standard error 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11 no. of obs. used 71 70 69 69 68 67 67 66 65 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -33.000 i +++++ i 0.74711E-01 -32.000 i +++ i 0.42562E-01 -31.000 i ++++ i -0.51046E-01 -30.000 i ++++ i -0.63433E-01 -29.000 i ++ i 0.25922E-01 -28.000 i ++++ i 0.65406E-01 -27.000 i +++++ i 0.78863E-01 -26.000 i + i 0.23979E-02 -25.000 i +++++ i -0.78787E-01 -24.000 i ++ i -0.22222E-01 -23.000 i +++ i 0.45344E-01 -22.000 i +++ i -0.44783E-01 -21.000 i +++++++ i -.11336 -20.000 i +++++ i -0.87624E-01 -19.000 i ++++++ i 0.90985E-01 -18.000 i ++++++++++ i .17371 -17.000 i +++++ i 0.80527E-01 -16.000 i ++++ i -0.58723E-01 -15.000 i + i -0.36456E-03 -14.000 i ++ i 0.29777E-01 -13.000 i +++ i 0.44568E-01 -12.000 i ++ i 0.13475E-01 -11.000 i +++++ i -0.78048E-01 -10.000 i +++++ i -0.72121E-01 -9.0000 i ++ i 0.11351E-01 -8.0000 i ++++++ i 0.98858E-01 -7.0000 i +++++++ i .12114 -6.0000 i ++++ i 0.54851E-01 -5.0000 i +++ i -0.44146E-01 -4.0000 i ++++++ i -0.94422E-01 -3.0000 i +++++++++++++++ i -.28165 -2.0000 i ++++++++++++++++++++++++++ i -.49902 -1.0000 i ++++++++++++++++++++++++++ i -.50420 .00000 i ++++ i -0.69124E-01 1.0000 i ++++++++++++++++++++++ i .42939 2.0000 i ++++++++++++++++++++++++ i .46091 3.0000 i ++++++++++++++++ i .30865 4.0000 i ++++++++++ i .18911 5.0000 i +++++ i 0.82395E-01 6.0000 i +++++++ i .11684 7.0000 i + i 0.69027E-02 8.0000 i +++ i -0.38806E-01 9.0000 i +++ i -0.33805E-01 10.000 i ++++ i 0.52098E-01 11.000 i ++++++++ i .13037 12.000 i ++++++ i 0.93005E-01 13.000 i +++ i -0.34856E-01 14.000 i ++++++ i -.10246 15.000 i ++++ i -0.57646E-01 16.000 i +++ i -0.45728E-01 17.000 i +++++ i -0.75106E-01 18.000 i ++++++++ i -.14355 19.000 i +++++++ i -.12720 20.000 i ++++ i -0.64572E-01 21.000 i +++ i 0.34833E-01 22.000 i ++++ i 0.51723E-01 23.000 i +++++++++ i .16006 24.000 i ++++++++++++++ i .25344 25.000 i ++++++++++++++ i .25479 26.000 i +++++++++ i .15414 27.000 i ++ i 0.25639E-01 28.000 i +++ i -0.34965E-01 29.000 i +++ i -0.39851E-01 30.000 i +++ i -0.41130E-01 31.000 i + i -0.93695E-02 32.000 i +++++++ i -.11948 33.000 i +++++++ i -.11405 ierr = 0 test of ccfms with missing values LACOV = 21 LAGMAX = 20 N = 100 LACOV = 21 LAGMAX = 20 N = 100 starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.2793813E-01 .1504041 standard deviation of the series = 1.455649 1.366055 number of time points = 100 100 number of missing observations = 3 1 percentage of observations missing = 3.0000 1.0000 largest lag value to be used = 20 missing value code = .8900000 .8900000 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf 0.04 0.03 -0.00 -0.06 0.08 0.17 0.09 -0.09 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 no. of obs. used 83 83 81 80 79 78 77 77 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.50 -0.50 -0.28 -0.09 -0.04 0.05 0.12 0.10 0.01 -0.07 -0.08 0.01 standard error 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 no. of obs. used 95 94 93 92 91 90 89 88 87 86 86 84 lag 0 ccf -0.07 standard error 0.10 no. of obs. used 96 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.43 0.46 0.31 0.19 0.08 0.12 0.01 -0.04 -0.03 0.05 0.13 0.09 standard error 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 no. of obs. used 95 94 93 92 91 90 89 88 87 86 85 84 lag 13 14 15 16 17 18 19 20 ccf -0.03 -0.10 -0.06 -0.05 -0.08 -0.14 -0.13 -0.06 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.12 no. of obs. used 83 82 81 80 79 78 77 76 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i +++++ i -0.87624E-01 -19.000 i ++++++ i 0.90985E-01 -18.000 i ++++++++++ i .17371 -17.000 i +++++ i 0.80527E-01 -16.000 i ++++ i -0.58723E-01 -15.000 i + i -0.36456E-03 -14.000 i ++ i 0.29777E-01 -13.000 i +++ i 0.44568E-01 -12.000 i ++ i 0.13475E-01 -11.000 i +++++ i -0.78048E-01 -10.000 i +++++ i -0.72121E-01 -9.0000 i ++ i 0.11351E-01 -8.0000 i ++++++ i 0.98858E-01 -7.0000 i +++++++ i .12114 -6.0000 i ++++ i 0.54851E-01 -5.0000 i +++ i -0.44146E-01 -4.0000 i ++++++ i -0.94422E-01 -3.0000 i +++++++++++++++ i -.28165 -2.0000 i ++++++++++++++++++++++++++ i -.49902 -1.0000 i ++++++++++++++++++++++++++ i -.50420 .00000 i ++++ i -0.69124E-01 1.0000 i ++++++++++++++++++++++ i .42939 2.0000 i ++++++++++++++++++++++++ i .46091 3.0000 i ++++++++++++++++ i .30865 4.0000 i ++++++++++ i .18911 5.0000 i +++++ i 0.82395E-01 6.0000 i +++++++ i .11684 7.0000 i + i 0.69027E-02 8.0000 i +++ i -0.38806E-01 9.0000 i +++ i -0.33805E-01 10.000 i ++++ i 0.52098E-01 11.000 i ++++++++ i .13037 12.000 i ++++++ i 0.93005E-01 13.000 i +++ i -0.34856E-01 14.000 i ++++++ i -.10246 15.000 i ++++ i -0.57646E-01 16.000 i +++ i -0.45728E-01 17.000 i +++++ i -0.75106E-01 18.000 i ++++++++ i -.14355 19.000 i +++++++ i -.12720 20.000 i ++++ i -0.64572E-01 ierr = 0 ccvf lag 1,1 1,2 2,1 2,2 0 2.0977 -0.1361 -0.1361 1.8474 1 1.1202 0.8453 -0.9926 1.0107 2 0.1496 0.9074 -0.9824 0.3376 3 -0.3133 0.6076 -0.5545 -0.1216 4 -0.3391 0.3723 -0.1859 -0.2122 5 -0.2522 0.1622 -0.0869 -0.1117 6 -0.2888 0.2300 0.1080 -0.1132 7 -0.2121 0.0136 0.2385 -0.0926 8 0.0506 -0.0764 0.1946 -0.0113 9 0.2507 -0.0665 0.0223 0.0850 10 0.2690 0.1026 -0.1420 -0.0242 11 0.1004 0.2567 -0.1536 0.0130 12 -0.2012 0.1831 0.0265 -0.1029 13 -0.2168 -0.0686 0.0877 -0.0895 14 -0.0707 -0.2017 0.0586 0.0332 15 0.0095 -0.1135 -0.0007 0.0508 16 -0.0417 -0.0900 -0.1156 -0.0946 17 -0.0369 -0.1479 0.1585 -0.1810 18 0.2259 -0.2826 0.3420 -0.1230 19 0.4679 -0.2504 0.1791 0.0286 20 0.2933 -0.1271 -0.1725 0.3123 ccf lag 1,1 1,2 2,1 2,2 0 1.0000 -0.0691 -0.0691 1.0000 1 0.5340 0.4294 -0.5042 0.5471 2 0.0713 0.4609 -0.4990 0.1828 3 -0.1494 0.3087 -0.2816 -0.0658 4 -0.1616 0.1891 -0.0944 -0.1148 5 -0.1202 0.0824 -0.0441 -0.0604 6 -0.1377 0.1168 0.0549 -0.0613 7 -0.1011 0.0069 0.1211 -0.0501 8 0.0241 -0.0388 0.0989 -0.0061 9 0.1195 -0.0338 0.0114 0.0460 10 0.1282 0.0521 -0.0721 -0.0131 11 0.0479 0.1304 -0.0780 0.0070 12 -0.0959 0.0930 0.0135 -0.0557 13 -0.1033 -0.0349 0.0446 -0.0485 14 -0.0337 -0.1025 0.0298 0.0180 15 0.0045 -0.0576 -0.0004 0.0275 16 -0.0199 -0.0457 -0.0587 -0.0512 17 -0.0176 -0.0751 0.0805 -0.0980 18 0.1077 -0.1436 0.1737 -0.0666 19 0.2230 -0.1272 0.0910 0.0155 20 0.1398 -0.0646 -0.0876 0.1690 nlppc lag 1,1 1,2 2,1 2,2 0 97 96 96 99 1 93 95 95 97 2 92 94 94 96 3 92 93 93 95 4 90 92 92 94 5 89 91 91 93 6 88 90 90 92 7 87 89 89 91 8 86 88 88 90 9 85 87 87 89 10 84 86 86 88 11 83 85 86 87 12 82 84 84 86 13 81 83 83 85 14 80 82 83 84 15 79 81 81 83 16 78 80 80 82 17 77 79 79 81 18 76 78 78 80 19 75 77 77 79 20 75 76 77 78 test of ccfs output suppressed starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ccfs ------------------------------------- the input value of lagmax is 150. the value of the argument lagmax must be between one and (n-1) , inclusive. the input value of iym is 20. the first dimension of ym , as indicated by the argument iym , must be greater than or equal to n . the correct form of the call statement is call ccfs (ym, n, m, iym, + lagmax, ccov, iccov, jccov, nprt, ldstak) ierr = 1 test of ccfms with missing values output suppressed LACOV = 21 LAGMAX = 20 N = 100 LACOV = 21 LAGMAX = 20 N = 100 ierr = 0 ccvf lag 1,1 1,2 2,1 2,2 0 2.0977 -0.1361 -0.1361 1.8474 1 1.1202 0.8453 -0.9926 1.0107 2 0.1496 0.9074 -0.9824 0.3376 3 -0.3133 0.6076 -0.5545 -0.1216 4 -0.3391 0.3723 -0.1859 -0.2122 5 -0.2522 0.1622 -0.0869 -0.1117 6 -0.2888 0.2300 0.1080 -0.1132 7 -0.2121 0.0136 0.2385 -0.0926 8 0.0506 -0.0764 0.1946 -0.0113 9 0.2507 -0.0665 0.0223 0.0850 10 0.2690 0.1026 -0.1420 -0.0242 11 0.1004 0.2567 -0.1536 0.0130 12 -0.2012 0.1831 0.0265 -0.1029 13 -0.2168 -0.0686 0.0877 -0.0895 14 -0.0707 -0.2017 0.0586 0.0332 15 0.0095 -0.1135 -0.0007 0.0508 16 -0.0417 -0.0900 -0.1156 -0.0946 17 -0.0369 -0.1479 0.1585 -0.1810 18 0.2259 -0.2826 0.3420 -0.1230 19 0.4679 -0.2504 0.1791 0.0286 20 0.2933 -0.1271 -0.1725 0.3123 ccf lag 1,1 1,2 2,1 2,2 0 1.0000 -0.0691 -0.0691 1.0000 1 0.5340 0.4294 -0.5042 0.5471 2 0.0713 0.4609 -0.4990 0.1828 3 -0.1494 0.3087 -0.2816 -0.0658 4 -0.1616 0.1891 -0.0944 -0.1148 5 -0.1202 0.0824 -0.0441 -0.0604 6 -0.1377 0.1168 0.0549 -0.0613 7 -0.1011 0.0069 0.1211 -0.0501 8 0.0241 -0.0388 0.0989 -0.0061 9 0.1195 -0.0338 0.0114 0.0460 10 0.1282 0.0521 -0.0721 -0.0131 11 0.0479 0.1304 -0.0780 0.0070 12 -0.0959 0.0930 0.0135 -0.0557 13 -0.1033 -0.0349 0.0446 -0.0485 14 -0.0337 -0.1025 0.0298 0.0180 15 0.0045 -0.0576 -0.0004 0.0275 16 -0.0199 -0.0457 -0.0587 -0.0512 17 -0.0176 -0.0751 0.0805 -0.0980 18 0.1077 -0.1436 0.1737 -0.0666 19 0.2230 -0.1272 0.0910 0.0155 20 0.1398 -0.0646 -0.0876 0.1690 nlppc lag 1,1 1,2 2,1 2,2 0 97 96 96 99 1 93 95 95 97 2 92 94 94 96 3 92 93 93 95 4 90 92 92 94 5 89 91 91 93 6 88 90 90 92 7 87 89 89 91 8 86 88 88 90 9 85 87 87 89 10 84 86 86 88 11 83 85 86 87 12 82 84 84 86 13 81 83 83 85 14 80 82 83 84 15 79 81 81 83 16 78 80 80 82 17 77 79 79 81 18 76 78 78 80 19 75 77 77 79 20 75 76 77 78 test of ccffs output suppressed ierr = 0 ccvf lag 1,1 1,2 2,1 2,2 0 2.0564 -0.1456 -0.1456 1.8344 1 1.0393 0.7944 -0.9661 0.9832 2 0.1461 0.8774 -0.9403 0.3418 3 -0.2874 0.6218 -0.5402 -0.1014 4 -0.2821 0.3797 -0.2210 -0.2211 5 -0.1887 0.1545 -0.0962 -0.1470 6 -0.2547 0.2392 0.0808 -0.1212 7 -0.1882 -0.0076 0.2112 -0.0886 8 0.0186 -0.0970 0.1687 0.0114 9 0.2115 -0.0674 -0.0107 0.0979 10 0.2269 0.1084 -0.1291 -0.0376 11 0.0569 0.2596 -0.1299 0.0089 12 -0.2003 0.1595 0.0321 -0.1100 13 -0.1984 -0.0645 0.0769 -0.0773 14 -0.0513 -0.1908 0.0830 0.0323 15 0.0233 -0.0935 0.0025 0.0461 16 -0.0629 -0.0753 -0.0949 -0.0924 17 -0.0387 -0.1386 0.1655 -0.1546 18 0.2385 -0.2359 0.3390 -0.1139 19 0.4009 -0.2134 0.1971 0.0256 20 0.2828 -0.1137 -0.1500 0.3058 ccf lag 1,1 1,2 2,1 2,2 0 1.0000 -0.0750 -0.0750 1.0000 1 0.5054 0.4090 -0.4974 0.5360 2 0.0710 0.4517 -0.4841 0.1863 3 -0.1397 0.3202 -0.2782 -0.0553 4 -0.1372 0.1955 -0.1138 -0.1205 5 -0.0918 0.0795 -0.0495 -0.0801 6 -0.1239 0.1232 0.0416 -0.0660 7 -0.0915 -0.0039 0.1088 -0.0483 8 0.0090 -0.0499 0.0869 0.0062 9 0.1029 -0.0347 -0.0055 0.0534 10 0.1104 0.0558 -0.0665 -0.0205 11 0.0277 0.1337 -0.0669 0.0049 12 -0.0974 0.0821 0.0165 -0.0600 13 -0.0965 -0.0332 0.0396 -0.0421 14 -0.0250 -0.0982 0.0427 0.0176 15 0.0113 -0.0481 0.0013 0.0252 16 -0.0306 -0.0388 -0.0488 -0.0504 17 -0.0188 -0.0713 0.0852 -0.0843 18 0.1160 -0.1214 0.1745 -0.0621 19 0.1950 -0.1099 0.1015 0.0139 20 0.1375 -0.0586 -0.0773 0.1667 test of ccffs output suppressed starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = 0.1000000E-01 0.1000000E-01 standard deviation of the series = .1000000 .1000000 number of time points = 100 100 largest lag value to be used = 20 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -13 -14 -15 -16 -17 -18 -19 -20 ccf -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 standard error 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.01 -0.01 -0.01 -0.01 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 standard error 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.09 0.09 0.09 lag 0 ccf -0.01 standard error 0.10 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 1.00 -0.01 -0.01 standard error 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.09 0.09 0.09 lag 13 14 15 16 17 18 19 20 ccf -0.01 -0.01 -0.00 -0.00 -0.00 -0.00 -0.00 -0.00 standard error 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -20.000 i + i -0.20202E-02 -19.000 i + i -0.19192E-02 -18.000 i + i -0.18182E-02 -17.000 i + i -0.17172E-02 -16.000 i + i -0.16162E-02 -15.000 i + i -0.15152E-02 -14.000 i + i -0.14141E-02 -13.000 i + i -0.13131E-02 -12.000 i + i -0.12121E-02 -11.000 i + i -0.11111E-02 -10.000 i + i -0.10101E-02 -9.0000 i + i -0.90909E-03 -8.0000 i + i -0.80808E-03 -7.0000 i + i -0.70706E-03 -6.0000 i + i -0.60609E-03 -5.0000 i + i -0.50504E-03 -4.0000 i ++ i -0.10505E-01 -3.0000 i ++ i -0.10404E-01 -2.0000 i ++ i -0.10303E-01 -1.0000 i ++ i -0.10202E-01 .00000 i ++ i -0.10101E-01 1.0000 i ++ i -0.10202E-01 2.0000 i ++ i -0.10303E-01 3.0000 i ++ i -0.10404E-01 4.0000 i ++ i -0.10505E-01 5.0000 i ++ i -0.10606E-01 6.0000 i ++ i -0.10707E-01 7.0000 i ++ i -0.10808E-01 8.0000 i ++ i -0.10909E-01 9.0000 i ++ i -0.11010E-01 10.000 i +++++++++++++++++++++++++++++++++++++++++++++++++++i .99899 11.000 i ++ i -0.11212E-01 12.000 i ++ i -0.11313E-01 13.000 i ++ i -0.11414E-01 14.000 i ++ i -0.11515E-01 15.000 i + i -0.15151E-02 16.000 i + i -0.16162E-02 17.000 i + i -0.17171E-02 18.000 i + i -0.18182E-02 19.000 i + i -0.19192E-02 20.000 i + i -0.20202E-02 ierr = 0 ccvf lag 1,1 1,2 2,1 2,2 0 0.0099 -0.0001 -0.0001 0.0099 1 -0.0001 -0.0001 -0.0001 -0.0001 2 -0.0001 -0.0001 -0.0001 -0.0001 3 -0.0001 -0.0001 -0.0001 -0.0001 4 -0.0001 -0.0001 -0.0001 -0.0001 5 -0.0000 -0.0001 -0.0000 -0.0001 6 -0.0000 -0.0001 -0.0000 -0.0001 7 -0.0000 -0.0001 -0.0000 -0.0001 8 -0.0000 -0.0001 -0.0000 -0.0001 9 -0.0000 -0.0001 -0.0000 -0.0001 10 -0.0000 0.0099 -0.0000 -0.0001 11 -0.0000 -0.0001 -0.0000 -0.0001 12 -0.0000 -0.0001 -0.0000 -0.0001 13 -0.0000 -0.0001 -0.0000 -0.0001 14 -0.0000 -0.0001 -0.0000 -0.0001 15 -0.0000 -0.0000 -0.0000 -0.0000 16 -0.0000 -0.0000 -0.0000 -0.0000 17 -0.0000 -0.0000 -0.0000 -0.0000 18 -0.0000 -0.0000 -0.0000 -0.0000 19 -0.0000 -0.0000 -0.0000 -0.0000 20 -0.0000 -0.0000 -0.0000 -0.0000 ccf lag 1,1 1,2 2,1 2,2 0 1.0000 -0.0101 -0.0101 1.0000 1 -0.0102 -0.0102 -0.0102 -0.0102 2 -0.0103 -0.0103 -0.0103 -0.0103 3 -0.0104 -0.0104 -0.0104 -0.0104 4 -0.0105 -0.0105 -0.0105 -0.0105 5 -0.0005 -0.0106 -0.0005 -0.0106 6 -0.0006 -0.0107 -0.0006 -0.0107 7 -0.0007 -0.0108 -0.0007 -0.0108 8 -0.0008 -0.0109 -0.0008 -0.0109 9 -0.0009 -0.0110 -0.0009 -0.0110 10 -0.0010 0.9990 -0.0010 -0.0111 11 -0.0011 -0.0112 -0.0011 -0.0112 12 -0.0012 -0.0113 -0.0012 -0.0113 13 -0.0013 -0.0114 -0.0013 -0.0114 14 -0.0014 -0.0115 -0.0014 -0.0115 15 -0.0015 -0.0015 -0.0015 -0.0015 16 -0.0016 -0.0016 -0.0016 -0.0016 17 -0.0017 -0.0017 -0.0017 -0.0017 18 -0.0018 -0.0018 -0.0018 -0.0018 19 -0.0019 -0.0019 -0.0019 -0.0019 20 -0.0020 -0.0020 -0.0020 -0.0020 1 ****test routines with correct call**** test of corr draper and smith data set (page 216). starpac 2.08s (03/15/90) correlation analysis for 4 variables with 9 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 4.1764550 2 .68374240 .74628705 3 -.61596984 -.17249313 7.9279222 4 .80175227 .76795024 -.62874597 12.645156 significance levels of simple correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 0.42269588E-01 .00000000 3 0.77358723E-01 .65719634 .00000000 4 0.93530416E-02 0.15660405E-01 0.69711268E-01 .00000000 partial correlation coefficients with 2 remaining variables fixed column 1 2 3 4 1 1.0000000 2 .43170992 1.0000000 3 -.45663619 .69716984 1.0000000 4 .10539015 .72681993 -.64778912 1.0000000 significance levels of partial correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .33344835 .00000000 3 .30301750 0.81676781E-01 .00000000 4 .82207656 0.64249456E-01 .11565989 .00000000 spearman rank correlation coefficients (adjusted for ties) column 1 2 3 4 1 1.0000000 2 .61088401 1.0000000 3 -.56666666 -.12552410 1.0000000 4 .68333334 .60251576 -.71666664 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 6 degrees of freedom (for example, 0.1704 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 3 4 1 1.0000000 .40443492 .94935948 .85222399 2 .17035985 1.0000000 .80987620 .93774122 3 .71654016 .56763339 1.0000000 .84988511 4 .15654355 .59975278 .36811483 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 3 4 1 99.000000 .95517081 .32129729 .97349304 95.000000 -.21219550 -.94361627 0.51874168E-01 2 .92694801 99.000000 .70508558 .96846092 0.35941135E-01 95.000000 -.84136051 -.36249600E-01 3 0.81486106E-01 .55523425 99.000000 .30247203 -.90845978 -.75062579 95.000000 -.94585729 4 .95654887 .94838423 0.60737621E-01 99.000000 .29437226 .21190052 -.91203487 95.000000 expected value for ierr is 0 returned value for ierr is 0 1test of corrs printout supressed. draper and smith data set (page 216). expected value for ierr is 0 returned value for ierr is 0 Storage from CORRS. Storage from variance-covariance matrix. column 1 2 3 4 1 17.442776 2.1311119 -20.395138 42.342079 2 2.1311119 .55694437 -1.0205566 7.2470822 3 -20.395138 -1.0205566 62.851948 -63.031662 4 42.342079 7.2470822 -63.031662 159.89996 1printout not supressed. draper and smith data set (page 216). starpac 2.08s (03/15/90) correlation analysis for 4 variables with 9 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 4.1764550 2 .68374240 .74628705 3 -.61596984 -.17249313 7.9279222 4 .80175227 .76795024 -.62874597 12.645156 significance levels of simple correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 0.42269588E-01 .00000000 3 0.77358723E-01 .65719634 .00000000 4 0.93530416E-02 0.15660405E-01 0.69711268E-01 .00000000 partial correlation coefficients with 2 remaining variables fixed column 1 2 3 4 1 1.0000000 2 .43170992 1.0000000 3 -.45663619 .69716984 1.0000000 4 .10539015 .72681993 -.64778912 1.0000000 significance levels of partial correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .33344835 .00000000 3 .30301750 0.81676781E-01 .00000000 4 .82207656 0.64249456E-01 .11565989 .00000000 spearman rank correlation coefficients (adjusted for ties) column 1 2 3 4 1 1.0000000 2 .61088401 1.0000000 3 -.56666666 -.12552410 1.0000000 4 .68333334 .60251576 -.71666664 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 6 degrees of freedom (for example, 0.1704 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 3 4 1 1.0000000 .40443492 .94935948 .85222399 2 .17035985 1.0000000 .80987620 .93774122 3 .71654016 .56763339 1.0000000 .84988511 4 .15654355 .59975278 .36811483 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 3 4 1 99.000000 .95517081 .32129729 .97349304 95.000000 -.21219550 -.94361627 0.51874168E-01 2 .92694801 99.000000 .70508558 .96846092 0.35941135E-01 95.000000 -.84136051 -.36249600E-01 3 0.81486106E-01 .55523425 99.000000 .30247203 -.90845978 -.75062579 95.000000 -.94585729 4 .95654887 .94838423 0.60737621E-01 99.000000 .29437226 .21190052 -.91203487 95.000000 expected value for ierr is 0 returned value for ierr is 0 Storage from CORRS. Storage from variance-covariance matrix. column 1 2 3 4 1 17.442776 2.1311119 -20.395138 42.342079 2 2.1311119 .55694437 -1.0205566 7.2470822 3 -20.395138 -1.0205566 62.851948 -63.031662 4 42.342079 7.2470822 -63.031662 159.89996 1****special case 2 column matrix**** draper and smith data set (page 216). starpac 2.08s (03/15/90) correlation analysis for 2 variables with 9 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 4.1764550 2 .68374240 .74628705 significance levels of simple correlation coefficients (assuming normality) column 1 2 1 .00000000 2 0.42269588E-01 .00000000 the partial correlation coefficients (and significance levels) are not printed or defined because either the number of vectors being compared is two or the number of measurements is less than or equal to the number of vectors being compared. spearman rank correlation coefficients (adjusted for ties) column 1 2 1 1.0000000 2 .61088401 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 6 degrees of freedom (for example, 0.1704 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 1 1.0000000 .40443492 2 .17035985 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 1 99.000000 .95517081 95.000000 -.21219550 2 .92694801 99.000000 0.35941135E-01 95.000000 expected value for ierr is 0 returned value for ierr is 0 1****test with insufficient work area**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corr ------------------------------------- the input value of ldstak is 121. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 122. the correct form of the call statement is call corr (ym, n, m, iym, ldstak) expected value for ierr is 1 returned value for ierr is 1 1****test with exactly the right amount of work area**** starpac 2.08s (03/15/90) correlation analysis for 4 variables with 9 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 4.1764550 2 .68374240 .74628705 3 -.61596984 -.17249313 7.9279222 4 .80175227 .76795024 -.62874597 12.645156 significance levels of simple correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 0.42269588E-01 .00000000 3 0.77358723E-01 .65719634 .00000000 4 0.93530416E-02 0.15660405E-01 0.69711268E-01 .00000000 partial correlation coefficients with 2 remaining variables fixed column 1 2 3 4 1 1.0000000 2 .43170992 1.0000000 3 -.45663619 .69716984 1.0000000 4 .10539015 .72681993 -.64778912 1.0000000 significance levels of partial correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .33344835 .00000000 3 .30301750 0.81676781E-01 .00000000 4 .82207656 0.64249456E-01 .11565989 .00000000 spearman rank correlation coefficients (adjusted for ties) column 1 2 3 4 1 1.0000000 2 .61088401 1.0000000 3 -.56666666 -.12552410 1.0000000 4 .68333334 .60251576 -.71666664 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 6 degrees of freedom (for example, 0.1704 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 3 4 1 1.0000000 .40443492 .94935948 .85222399 2 .17035985 1.0000000 .80987620 .93774122 3 .71654016 .56763339 1.0000000 .84988511 4 .15654355 .59975278 .36811483 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 3 4 1 99.000000 .95517081 .32129729 .97349304 95.000000 -.21219550 -.94361627 0.51874168E-01 2 .92694801 99.000000 .70508558 .96846092 0.35941135E-01 95.000000 -.84136051 -.36249600E-01 3 0.81486106E-01 .55523425 99.000000 .30247203 -.90845978 -.75062579 95.000000 -.94585729 4 .95654887 .94838423 0.60737621E-01 99.000000 .29437226 .21190052 -.91203487 95.000000 expected value for ierr is 0 returned value for ierr is 0 1****test with insufficient work area**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corrs ------------------------------------- the input value of ldstak is 113. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 114. the correct form of the call statement is call corrs (ym, n, m, iym, ldstak, nprt, vcv, ivcv) expected value for ierr is 1 returned value for ierr is 1 1****test with exactly the right amount of work area**** starpac 2.08s (03/15/90) correlation analysis for 4 variables with 9 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 4.1764550 2 .68374240 .74628705 3 -.61596984 -.17249313 7.9279222 4 .80175227 .76795024 -.62874597 12.645156 significance levels of simple correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 0.42269588E-01 .00000000 3 0.77358723E-01 .65719634 .00000000 4 0.93530416E-02 0.15660405E-01 0.69711268E-01 .00000000 partial correlation coefficients with 2 remaining variables fixed column 1 2 3 4 1 1.0000000 2 .43170992 1.0000000 3 -.45663619 .69716984 1.0000000 4 .10539015 .72681993 -.64778912 1.0000000 significance levels of partial correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .33344835 .00000000 3 .30301750 0.81676781E-01 .00000000 4 .82207656 0.64249456E-01 .11565989 .00000000 spearman rank correlation coefficients (adjusted for ties) column 1 2 3 4 1 1.0000000 2 .61088401 1.0000000 3 -.56666666 -.12552410 1.0000000 4 .68333334 .60251576 -.71666664 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 6 degrees of freedom (for example, 0.1704 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 3 4 1 1.0000000 .40443492 .94935948 .85222399 2 .17035985 1.0000000 .80987620 .93774122 3 .71654016 .56763339 1.0000000 .84988511 4 .15654355 .59975278 .36811483 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 3 4 1 99.000000 .95517081 .32129729 .97349304 95.000000 -.21219550 -.94361627 0.51874168E-01 2 .92694801 99.000000 .70508558 .96846092 0.35941135E-01 95.000000 -.84136051 -.36249600E-01 3 0.81486106E-01 .55523425 99.000000 .30247203 -.90845978 -.75062579 95.000000 -.94585729 4 .95654887 .94838423 0.60737621E-01 99.000000 .29437226 .21190052 -.91203487 95.000000 Storage from CORRS. Storage from variance-covariance matrix. column 1 2 3 4 1 17.442776 2.1311119 -20.395138 42.342079 2 2.1311119 .55694437 -1.0205566 7.2470822 3 -20.395138 -1.0205566 62.851948 -63.031662 4 42.342079 7.2470822 -63.031662 159.89996 expected value for ierr is 0 returned value for ierr is 0 1****test with insufficient work area**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corrs ------------------------------------- the input value of ldstak is 22. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 23. the correct form of the call statement is call corrs (ym, n, m, iym, ldstak, nprt, vcv, ivcv) expected value for ierr is 1 returned value for ierr is 1 1****test with exactly the right amount of work area**** Storage from CORRS. Storage from variance-covariance matrix. column 1 2 3 4 1 17.442776 2.1311119 -20.395138 42.342079 2 2.1311119 .55694437 -1.0205566 7.2470822 3 -20.395138 -1.0205566 62.851948 -63.031662 4 42.342079 7.2470822 -63.031662 159.89996 expected value for ierr is 0 returned value for ierr is 0 1****number of variables less than 2**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corr ------------------------------------- the input value of m is 1. the value of the argument m must be greater than or equal to two . the correct form of the call statement is call corr (ym, n, m, iym, ldstak) expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corrs ------------------------------------- the input value of m is 1. the value of the argument m must be greater than or equal to two . the correct form of the call statement is call corrs (ym, n, m, iym, ldstak, nprt, vcv, ivcv) expected value for ierr is 1 returned value for ierr is 1 ****number of observations less than 3**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corr ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to three . the correct form of the call statement is call corr (ym, n, m, iym, ldstak) expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corrs ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to three . the correct form of the call statement is call corrs (ym, n, m, iym, ldstak, nprt, vcv, ivcv) expected value for ierr is 1 returned value for ierr is 1 ****observation matrix dimensioned less than number of observations designated**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corr ------------------------------------- the input value of iym is 8. the first dimension of ym , as indicated by the argument iym , must be greater than or equal to n . the correct form of the call statement is call corr (ym, n, m, iym, ldstak) expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corrs ------------------------------------- the input value of iym is 8. the first dimension of ym , as indicated by the argument iym , must be greater than or equal to n . the correct form of the call statement is call corrs (ym, n, m, iym, ldstak, nprt, vcv, ivcv) expected value for ierr is 1 returned value for ierr is 1 ****inadequate space in storage arrays**** starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine corrs ------------------------------------- the input value of ivcv is 2. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to m . the correct form of the call statement is call corrs (ym, n, m, iym, ldstak, nprt, vcv, ivcv) expected value for ierr is 1 returned value for ierr is 1 1****all observations on a variable equal to zero**** starpac 2.08s (03/15/90) correlation analysis for 4 variables with 9 observations covariance matrix nonpositive variances encountered. correlation coefficients cannot be computed. column 1 2 3 4 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) correlation analysis for 4 variables with 9 observations covariance matrix nonpositive variances encountered. correlation coefficients cannot be computed. column 1 2 3 4 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 Storage from CORRS. Storage from variance-covariance matrix. column 1 2 3 4 1 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 expected value for ierr is 1 returned value for ierr is 1 starpac 2.08s (03/15/90) correlation analysis for 4 variables with 10 observations covariance matrix nonpositive variances encountered. correlation coefficients cannot be computed. column 1 2 3 4 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 expected value for ierr is 1 returned value for ierr is 1 1****array containing a single value**** starpac 2.08s (03/15/90) correlation analysis for 10 variables with 4 observations covariance matrix nonpositive variances encountered. correlation coefficients cannot be computed. column 1 2 3 4 5 6 7 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 7 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 8 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 9 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 10 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 column 8 9 10 8 .00000000 9 .00000000 .00000000 10 .00000000 .00000000 .00000000 expected value for ierr is 1 returned value for ierr is 1 1****2 columns related**** starpac 2.08s (03/15/90) correlation analysis for 4 variables with 5 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 7.9056940 2 1.0000001 15.811388 3 1.0000000 1.0000000 23.717083 4 1.0000000 1.0000000 1.0000000 23.717083 significance levels of simple correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 partial correlation coefficients with 2 remaining variables fixed column 1 2 3 4 1 1.0000000 2 -1.0000000 1.0000000 3 1.0000000 1.0000000 1.0000000 4 1.0000000 1.0000000 -.59604645E-07 1.0000000 significance levels of partial correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 .00000000 .00000000 1.0000000 .00000000 spearman rank correlation coefficients (adjusted for ties) column 1 2 3 4 1 1.0000000 2 1.0000000 1.0000000 3 1.0000000 1.0000000 1.0000000 4 1.0000000 1.0000000 1.0000000 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 2 degrees of freedom (for example, 1.0000 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 3 4 1 1.0000000 1.0000000 1.0000000 1.0000000 2 1.0000000 1.0000000 1.0000000 1.0000000 3 1.0000000 1.0000000 1.0000000 1.0000000 4 1.0000000 1.0000000 1.0000000 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 3 4 1 99.000000 .99293894 .99293894 .99293894 95.000000 -.67582375 -.67582375 -.67582375 2 .98321199 99.000000 .99293894 .99293894 -.36782360 95.000000 -.67582375 -.67582375 3 .98321199 .98321199 99.000000 1.0000000 -.36782360 -.36782360 95.000000 .99999774 4 .98321199 .98321199 1.0000000 99.000000 -.36782360 -.36782360 .99999905 95.000000 expected value for ierr is 1 returned value for ierr is 0 possible error, unexpected value for error flag 1****2 columns related**** starpac 2.08s (03/15/90) correlation analysis for 4 variables with 5 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 7.9056940 2 1.0000001 15.811388 3 1.0000000 1.0000000 23.717083 4 .00000000 .00000000 .00000000 23.717083 significance levels of simple correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 1.0000000 1.0000000 1.0000000 .00000000 partial correlation coefficients with 2 remaining variables fixed column 1 2 3 4 1 1.0000000 2 0.34028235E+39 1.0000000 3 0.34028235E+39 0.34028235E+39 1.0000000 4 0.34028235E+39 0.34028235E+39 -.00000000 1.0000000 significance levels of partial correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .00000000 .00000000 3 .00000000 .00000000 .00000000 4 .00000000 .00000000 1.0000000 .00000000 spearman rank correlation coefficients (adjusted for ties) column 1 2 3 4 1 1.0000000 2 1.0000000 1.0000000 3 1.0000000 1.0000000 1.0000000 4 .00000000 .00000000 .00000000 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 2 degrees of freedom (for example, 1.0000 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 3 4 1 1.0000000 1.0000000 1.0000000 .15484571 2 1.0000000 1.0000000 1.0000000 .15484571 3 1.0000000 1.0000000 1.0000000 .15484554 4 .15484554 .15484554 .15484554 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 3 4 1 99.000000 .99293894 .99293894 .94897646 95.000000 -.67582375 -.67582375 -.94897646 2 .98321199 99.000000 .99293894 .94897646 -.36782360 95.000000 -.67582375 -.94897646 3 .98321199 .98321199 99.000000 .94897646 -.36782360 -.36782360 95.000000 -.94897646 4 .88226640 .88226640 .88226640 99.000000 -.88226640 -.88226640 -.88226640 95.000000 expected value for ierr is 1 returned value for ierr is 0 possible error, unexpected value for error flag derivative checking subroutine test number 1 correctly coded derivative simple example test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 2 -values of the independent variables at this row index 1 value 0.6250000E-01 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 derivative checking subroutine test number 2 correctly coded derivative simple example input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 ok 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 ok 4 2.0000000 1.0000000 ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 1derivative checking subroutine test number 1 check error handling - test 1 test of dckls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine dckls ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of m is -5. the value of the argument m must be greater than or equal to 1. the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the correct form of the call statement is call dckls (xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, ldstak) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 test of dcklsc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine dcklsc ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of m is -5. the value of the argument m must be greater than or equal to 1. the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the correct form of the call statement is call dcklsc (xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, ldstak, + neta, ntau, scale, nrow, nprt) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 1derivative checking subroutine test number 2 check error handling - test 2 test of dckls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine dckls ------------------------------------- the input value of ldstak is 271. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 272. the correct form of the call statement is call dckls (xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, ldstak) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 test of dcklsc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine dcklsc ------------------------------------- the input value of ldstak is 271. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 272. the number of values in vector scale less than or equal to zero is 1. since the first value of the vector scale is greater than zero all of the values must be greater than zero . the correct form of the call statement is call dcklsc (xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, ldstak, + neta, ntau, scale, nrow, nprt) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 derivative checking subroutine test number 1 correctly coded derivative simple example input - neta = -1, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = -1, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 2 correctly coded derivative simple example input - neta = -1, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = -1, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 3 correctly coded derivative simple example input - neta = -1, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = -1, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 4 correctly coded derivative simple example input - neta = 0, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 0, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 5 correctly coded derivative simple example input - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 6 correctly coded derivative simple example input - neta = 0, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 0, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 7 correctly coded derivative simple example input - neta = 1, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 1, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 8 correctly coded derivative simple example input - neta = 1, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 1, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 9 correctly coded derivative simple example input - neta = 1, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 1, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 10 correctly coded derivative simple example input - neta = 2, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 2 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 2, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 11 correctly coded derivative simple example input - neta = 2, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 2 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 2, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 12 correctly coded derivative simple example input - neta = 2, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 2 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 2, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 13 correctly coded derivative simple example input - neta = 6, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 6, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 14 correctly coded derivative simple example input - neta = 6, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 6, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 15 correctly coded derivative simple example input - neta = 6, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 6, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 16 correctly coded derivative simple example input - neta = 6, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 6, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 17 correctly coded derivative simple example input - neta = 6, ntau = 2, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 6, ntau = 2, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 18 correctly coded derivative simple example input - neta = 6, ntau = 3, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 6, ntau = 3, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 19 correctly coded derivative simple example input - neta = 7, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 7, ntau = -1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 20 correctly coded derivative simple example input - neta = 7, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 7, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 21 correctly coded derivative simple example input - neta = 7, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 7, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 22 correctly coded derivative simple example input - neta = 7, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 1 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 7, ntau = 1, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 23 correctly coded derivative simple example input - neta = 7, ntau = 2, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 7, ntau = 2, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 24 correctly coded derivative simple example input - neta = 7, ntau = 3, scale(1) = .00000000 , nrow = 1, nprt = 1 test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 1.0000000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 7, ntau = 3, scale(1) = .00000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 25 correctly coded derivative simple example input - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 0 test of dcklsc ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 1, nprt = 0 derivative checking subroutine test number 26 correctly coded derivative large calculation error problem zero derivative input - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 51, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default questionable (1) 3 100.00000 default ok 4 2.0000000 default questionable (1) * numbers in parentheses refer to the following notes. (1) user-supplied and approximated derivatives agree, but both are zero. recheck at another row. number of reliable digits in model results (neta) 5 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 51 -values of the independent variables at this row index 1 value 3.125000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 2 output - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 51, nprt = 1 derivative checking subroutine test number 27 correctly coded derivative large calculation error problem nearly zero derivative input - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 50, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default ok 2 3.1250000 default ok 3 100.00000 default ok 4 2.0000000 default ok number of reliable digits in model results (neta) 5 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 50 -values of the independent variables at this row index 1 value 3.062500 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 output - neta = 0, ntau = 0, scale(1) = .00000000 , nrow = 50, nprt = 1 derivative checking subroutine test number 28 incorrectly coded derivative for parameters 1, 2 and 4 simple example test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 default questionable (3) 2 3.1250000 default ok 3 1.0000000 default questionable (3) 4 2.0000000 default incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 2 -values of the independent variables at this row index 1 value 0.6250000E-01 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 derivative checking subroutine test number 29 incorrectly coded derivative for parameters 1, 2 and 4 simple example input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 30 incorrectly coded derivative for parameters 1, 2 and 4 simple example input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 0 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 0 derivative checking subroutine test number 31 incorrectly coded derivative for parameters 1, 2 and 4 large calculation error problem input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 26, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 100.00000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 26 -values of the independent variables at this row index 1 value 1.562500 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 26, nprt = 1 derivative checking subroutine test number 32 incorrectly coded derivative for parameters 1, 2 and 4 large calculation error problem input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 100.00000 1.0000000 questionable (2) 4 .75000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (2) user-supplied and approximated derivatives may agree, but user-supplied derivative is identically zero and approximated derivative is only approximately zero. recheck at another row. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 33 incorrectly coded derivative for parameters 1, 2 and 4 all independent variables equal to zero input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = -1, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = -1, nprt = 1 derivative checking subroutine test number 34 incorrectly coded derivative for parameters 1, 2 and 4 all independent variables equal to zero input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 0, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 0, nprt = 1 derivative checking subroutine test number 35 incorrectly coded derivative for parameters 1, 2 and 4 all independent variables equal to zero input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 1, nprt = 1 derivative checking subroutine test number 36 incorrectly coded derivative for parameters 1, 2 and 4 all independent variables equal to zero input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 101, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 101 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 101, nprt = 1 derivative checking subroutine test number 37 incorrectly coded derivative for parameters 1, 2 and 4 all independent variables equal to zero input - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 102, nprt = 1 test of dcklsc starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 1.0000000 1.0000000 questionable (3) 2 3.1250000 0.99999998E-02 ok 3 1.0000000 1.0000000 questionable (3) 4 2.0000000 1.0000000 incorrect * numbers in parentheses refer to the following notes. (3) user-supplied and approximated derivatives disagree, but user-supplied derivative is identically zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value .0000000 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 3 output - neta = 0, ntau = 0, scale(1) = 1.0000000 , nrow = 102, nprt = 1 test of demod starpac 2.08s (03/15/90) time series demodulation demodulation frequency is0.09090909 cutoff frequency is 0.04545455 the number of terms in the filter is 41 plot of amplitude of smoothed demodulated series location of mean is given by plot character m 19.7733 24.0727 28.3722 32.6717 36.9712 41.2706 45.5701 49.8696 54.1690 58.4685 62.7680 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + m i 30.652 2.0000 i + m i 32.848 3.0000 i + m i 35.191 4.0000 i + m i 37.582 5.0000 i m + i 39.925 6.0000 i m + i 42.146 7.0000 i m + i 44.216 8.0000 i m + i 46.153 9.0000 i m + i 47.948 10.000 i m + i 49.529 11.000 i m + i 50.786 12.000 i m + i 51.633 13.000 i m + i 52.052 14.000 i m + i 52.071 15.000 i m + i 51.754 16.000 i m + i 51.178 17.000 i m + i 50.429 18.000 i m + i 49.599 19.000 i m + i 48.776 20.000 i m + i 48.011 21.000 i m + i 47.287 22.000 i m + i 46.515 23.000 i m + i 45.587 24.000 i m + i 44.441 25.000 i m + i 43.055 26.000 i m + i 41.462 27.000 i m+ i 39.721 28.000 i + m i 37.931 29.000 i + m i 36.245 30.000 i + m i 34.853 31.000 i + m i 33.877 32.000 i + m i 33.306 33.000 i + m i 33.020 34.000 i + m i 32.861 35.000 i + m i 32.690 36.000 i + m i 32.398 37.000 i + m i 31.913 38.000 i + m i 31.235 39.000 i + m i 30.500 40.000 i + m i 29.924 41.000 i + m i 29.721 42.000 i + m i 30.052 43.000 i + m i 30.936 44.000 i + m i 32.234 45.000 i + m i 33.719 46.000 i + m i 35.148 47.000 i + m i 36.375 48.000 i + m i 37.438 49.000 i +m i 38.520 50.000 i m + i 39.817 51.000 i m + i 41.445 52.000 i m + i 43.391 53.000 i m + i 45.493 54.000 i m + i 47.509 55.000 i m + i 49.231 56.000 i m + i 50.580 57.000 i m + i 51.625 58.000 i m + i 52.548 59.000 i m + i 53.573 60.000 i m + i 54.903 61.000 i m + i 56.621 62.000 i m + i 58.605 63.000 i m + i 60.549 64.000 i m + i 62.053 65.000 i m +i 62.768 66.000 i m + i 62.470 67.000 i m + i 61.053 68.000 i m + i 58.498 69.000 i m + i 54.856 70.000 i m + i 50.271 71.000 i m + i 44.992 72.000 i m+ i 39.363 73.000 i + m i 33.810 74.000 i + m i 28.877 75.000 i + m i 25.219 76.000 i + m i 23.400 77.000 i + m i 23.440 78.000 i + m i 24.701 79.000 i + m i 26.348 80.000 i + m i 27.725 81.000 i + m i 28.463 82.000 i + m i 28.457 83.000 i + m i 27.798 84.000 i + m i 26.694 85.000 i + m i 25.380 86.000 i + m i 24.053 87.000 i + m i 22.852 88.000 i + m i 21.861 89.000 i + m i 21.115 90.000 i + m i 20.585 91.000 i + m i 20.228 92.000 i + m i 19.997 93.000 i+ m i 19.856 94.000 i+ m i 19.780 95.000 i+ m i 19.773 96.000 i+ m i 19.881 97.000 i + m i 20.173 98.000 i + m i 20.663 99.000 i + m i 21.322 100.00 i + m i 22.121 101.00 i + m i 23.043 102.00 i + m i 24.074 103.00 i + m i 25.170 104.00 i + m i 26.225 105.00 i + m i 27.089 106.00 i + m i 27.651 107.00 i + m i 27.926 108.00 i + m i 28.094 109.00 i + m i 28.456 110.00 i + m i 29.302 111.00 i + m i 30.831 112.00 i + m i 33.091 113.00 i + m i 35.983 114.00 i m i 39.318 115.00 i m + i 42.865 116.00 i m + i 46.383 117.00 i m + i 49.656 118.00 i m + i 52.532 119.00 i m + i 54.905 120.00 i m + i 56.646 121.00 i m + i 57.612 122.00 i m + i 57.717 123.00 i m + i 56.979 124.00 i m + i 55.517 125.00 i m + i 53.529 126.00 i m + i 51.257 127.00 i m + i 48.940 128.00 i m + i 46.789 129.00 i m + i 44.991 130.00 i m + i 43.671 131.00 i m + i 42.836 132.00 i m + i 42.376 133.00 i m + i 42.143 134.00 i m + i 41.978 135.00 i m + i 41.754 136.00 i m + i 41.392 137.00 i m + i 40.857 138.00 i m + i 40.153 139.00 i m+ i 39.349 140.00 i +m i 38.626 141.00 i + m i 38.249 142.00 i + m i 38.471 143.00 i m+ i 39.409 144.00 i m + i 41.011 145.00 i m + i 43.087 146.00 i m + i 45.406 147.00 i m + i 47.742 148.00 i m + i 49.872 149.00 i m + i 51.604 150.00 i m + i 52.815 151.00 i m + i 53.486 152.00 i m + i 53.642 153.00 i m + i 53.276 154.00 i m + i 52.344 155.00 i m + i 50.815 156.00 i m + i 48.720 157.00 i m + i 46.180 158.00 i m + i 43.393 159.00 i m + i 40.591 160.00 i + m i 37.983 161.00 i + m i 35.722 162.00 i + m i 33.921 163.00 i + m i 32.684 164.00 i + m i 32.106 165.00 i + m i 32.232 166.00 i + m i 32.994 167.00 i + m i 34.207 168.00 i + m i 35.618 169.00 i + m i 36.974 170.00 i + m i 38.080 171.00 i +m i 38.791 172.00 i m i 39.002 173.00 i +m i 38.724 174.00 i + m i 38.055 175.00 i + m i 37.128 176.00 i + m i 36.047 177.00 i + m i 34.859 178.00 i + m i 33.594 179.00 i + m i 32.295 180.00 i + m i 31.060 181.00 i + m i 29.998 182.00 i + m i 29.205 183.00 i + m i 28.723 184.00 i + m i 28.547 185.00 i + m i 28.668 186.00 i + m i 29.109 187.00 i + m i 29.919 188.00 i + m i 31.119 189.00 i + m i 32.649 190.00 i + m i 34.381 191.00 i + m i 36.171 192.00 i + m i 37.886 193.00 i m+ i 39.408 194.00 i m + i 40.621 195.00 i m + i 41.430 196.00 i m + i 41.815 197.00 i m + i 41.858 198.00 i m + i 41.681 199.00 i m + i 41.339 200.00 i m + i 40.812 201.00 i m + i 40.056 202.00 i m i 39.052 203.00 i + m i 37.838 204.00 i + m i 36.522 205.00 i + m i 35.275 206.00 i + m i 34.325 207.00 i + m i 33.954 208.00 i + m i 34.416 209.00 i + m i 35.740 210.00 i + m i 37.716 211.00 i m + i 40.017 212.00 i m + i 42.334 213.00 i m + i 44.438 214.00 i m + i 46.206 215.00 i m + i 47.597 216.00 i m + i 48.667 217.00 i m + i 49.589 218.00 i m + i 50.609 219.00 i m + i 51.878 220.00 i m + i 53.421 221.00 i m + i 55.145 1 starpac 2.08s (03/15/90) plot of phase of smoothed demodulated series location of zero is given by plot character 0 -6.2832 -5.0265 -3.7699 -2.5133 -1.2566 0.0000 1.2566 2.5133 3.7699 5.0265 6.2832 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i b 0 a i 2.0000 i b 0 a i 3.0000 i b 0 a i 4.0000 i b 0 a i 5.0000 i b 0 a i 6.0000 i b 0 a i 7.0000 i b 0 a i 8.0000 i a 0 b i 9.0000 i a 0 b i 10.000 i a 0 b i 11.000 i a 0 b i 12.000 i a 0 b i 13.000 i a 0 b i 14.000 i a 0 b i 15.000 i a 0 b i 16.000 i a 0 b i 17.000 i a 0 b i 18.000 i a 0 b i 19.000 i a 0 b i 20.000 i a 0 b i 21.000 i a 0 b i 22.000 i a 0 b i 23.000 i a 0 b i 24.000 i a 0 b i 25.000 i a 0 b i 26.000 i a 0 b i 27.000 i b 0 a i 28.000 i b 0 a i 29.000 i b 0 a i 30.000 i b 0 a i 31.000 i b 0 a i 32.000 i b 0 a i 33.000 i b 0 a i 34.000 i b 0 a i 35.000 i b 0 a i 36.000 i b 0 a i 37.000 i b 0 a i 38.000 i b 0 a i 39.000 i b 0 a i 40.000 i b 0 a i 41.000 i a 0 b i 42.000 i a 0 b i 43.000 i a 0 b i 44.000 i a 0 b i 45.000 i a 0 b i 46.000 i a 0 b i 47.000 i a 0 b i 48.000 i a 0 b i 49.000 i a 0 b i 50.000 i a 0 b i 51.000 i a 0 b i 52.000 i a 0 b i 53.000 i a 0 b i 54.000 i a 0 b i 55.000 i a 0 b i 56.000 i a 0 b i 57.000 i a 0 b i 58.000 i a 0 b i 59.000 i a 0 b i 60.000 i a 0 b i 61.000 i a 0 b i 62.000 i a 0 b i 63.000 i a 0 b i 64.000 i a 0 b i 65.000 i a 0 b i 66.000 i a 0 b i 67.000 i a 0 b i 68.000 i a 0 b i 69.000 i a 0 b i 70.000 i a 0 b i 71.000 i a 0 b i 72.000 i a 0 b i 73.000 i a 0 b i 74.000 i a 0 b i 75.000 i a 0 b i 76.000 i a 0 b i 77.000 i a 0 b i 78.000 i a 0 b i 79.000 i a 0 b i 80.000 i a 0 b i 81.000 i a 0 b i 82.000 i a 0 b i 83.000 i a 0 b i 84.000 i a 0 b i 85.000 i a 0 b i 86.000 i a 0 b i 87.000 i a 0 b i 88.000 i a 0 b i 89.000 i a 0 b i 90.000 i a 0 b i 91.000 i b 0 a i 92.000 i b 0 a i 93.000 i b 0 a i 94.000 i b 0 a i 95.000 i b 0 a i 96.000 i b 0 a i 97.000 i b 0 a i 98.000 i b 0 a i 99.000 i b 0 a i 100.00 i b 0 a i 101.00 i b 0 a i 102.00 i b 0 a i 103.00 i b 0 a i 104.00 i b 0 a i 105.00 i b 0 a i 106.00 i b 0 a i 107.00 i b 0 a i 108.00 i b 0 a i 109.00 i b 0 a i 110.00 i b 0 a i 111.00 i b 0 a i 112.00 i b 0 a i 113.00 i b 0 a i 114.00 i b 0 a i 115.00 i b 0 a i 116.00 i b 0 a i 117.00 i b 0 a i 118.00 i b 0 a i 119.00 i a 0 b i 120.00 i a 0 b i 121.00 i a 0 b i 122.00 i a 0 b i 123.00 i a 0 b i 124.00 i a 0 b i 125.00 i a 0 b i 126.00 i a 0 b i 127.00 i a 0 b i 128.00 i a 0 b i 129.00 i b 0 a i 130.00 i b 0 a i 131.00 i b 0 a i 132.00 i b 0 a i 133.00 i b 0 a i 134.00 i b 0 a i 135.00 i b 0 a i 136.00 i b 0 a i 137.00 i b 0 a i 138.00 i b 0 a i 139.00 i b 0 a i 140.00 i b 0 a i 141.00 i b 0 a i 142.00 i b 0 a i 143.00 i b 0 a i 144.00 i b 0 a i 145.00 i b 0 a i 146.00 i b 0 a i 147.00 i b 0 a i 148.00 i b 0 a i 149.00 i b 0 a i 150.00 i b 0 a i 151.00 i b 0 a i 152.00 i b 0 a i 153.00 i b 0 a i 154.00 i b 0 a i 155.00 i b 0 a i 156.00 i b 0 a i 157.00 i b 0 a i 158.00 i b 0 a i 159.00 i b 0 a i 160.00 i b 0 a i 161.00 i b 0 a i 162.00 i b 0 a i 163.00 i b 0 a i 164.00 i b 0 a i 165.00 i b 0 a i 166.00 i b 0 a i 167.00 i b 0 a i 168.00 i b 0 a i 169.00 i b 0 a i 170.00 i b 0 a i 171.00 i b 0 a i 172.00 i b 0 a i 173.00 i b 0 a i 174.00 i b 0 a i 175.00 i b 0 a i 176.00 i b 0 a i 177.00 i b 0 a i 178.00 i b 0 a i 179.00 i b 0 a i 180.00 i b 0 a i 181.00 i b 0 a i 182.00 i b 0 a i 183.00 i b 0 a i 184.00 i b 0 a i 185.00 i b 0 a i 186.00 i b 0 a i 187.00 i b 0 a i 188.00 i b 0 a i 189.00 i b 0 a i 190.00 i b 0 a i 191.00 i b 0 a i 192.00 i b 0 a i 193.00 i b 0 a i 194.00 i b 0 a i 195.00 i b 0 a i 196.00 i b 0 a i 197.00 i b 0 a i 198.00 i b 0 a i 199.00 i b 0 a i 200.00 i b 0 a i 201.00 i b 0 a i 202.00 i b 0 a i 203.00 i b 0 a i 204.00 i b 0 a i 205.00 i b 0 a i 206.00 i b 0 a i 207.00 i b 0 a i 208.00 i b 0 a i 209.00 i b 0 a i 210.00 i b 0 a i 211.00 i b 0 a i 212.00 i b 0 a i 213.00 i b 0 a i 214.00 i b 0 a i 215.00 i b 0 a i 216.00 i b 0 a i 217.00 i b 0 a i 218.00 i b 0 a i 219.00 i b 0 a i 220.00 i b 0 a i 221.00 i b 0 a i ierr is 0 test of demods starpac 2.08s (03/15/90) time series demodulation demodulation frequency is0.09090909 cutoff frequency is 0.04545455 the number of terms in the filter is 41 plot of amplitude of smoothed demodulated series location of mean is given by plot character m 19.7733 24.0727 28.3722 32.6717 36.9712 41.2706 45.5701 49.8696 54.1690 58.4685 62.7680 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + m i 30.652 2.0000 i + m i 32.848 3.0000 i + m i 35.191 4.0000 i + m i 37.582 5.0000 i m + i 39.925 6.0000 i m + i 42.146 7.0000 i m + i 44.216 8.0000 i m + i 46.153 9.0000 i m + i 47.948 10.000 i m + i 49.529 11.000 i m + i 50.786 12.000 i m + i 51.633 13.000 i m + i 52.052 14.000 i m + i 52.071 15.000 i m + i 51.754 16.000 i m + i 51.178 17.000 i m + i 50.429 18.000 i m + i 49.599 19.000 i m + i 48.776 20.000 i m + i 48.011 21.000 i m + i 47.287 22.000 i m + i 46.515 23.000 i m + i 45.587 24.000 i m + i 44.441 25.000 i m + i 43.055 26.000 i m + i 41.462 27.000 i m+ i 39.721 28.000 i + m i 37.931 29.000 i + m i 36.245 30.000 i + m i 34.853 31.000 i + m i 33.877 32.000 i + m i 33.306 33.000 i + m i 33.020 34.000 i + m i 32.861 35.000 i + m i 32.690 36.000 i + m i 32.398 37.000 i + m i 31.913 38.000 i + m i 31.235 39.000 i + m i 30.500 40.000 i + m i 29.924 41.000 i + m i 29.721 42.000 i + m i 30.052 43.000 i + m i 30.936 44.000 i + m i 32.234 45.000 i + m i 33.719 46.000 i + m i 35.148 47.000 i + m i 36.375 48.000 i + m i 37.438 49.000 i +m i 38.520 50.000 i m + i 39.817 51.000 i m + i 41.445 52.000 i m + i 43.391 53.000 i m + i 45.493 54.000 i m + i 47.509 55.000 i m + i 49.231 56.000 i m + i 50.580 57.000 i m + i 51.625 58.000 i m + i 52.548 59.000 i m + i 53.573 60.000 i m + i 54.903 61.000 i m + i 56.621 62.000 i m + i 58.605 63.000 i m + i 60.549 64.000 i m + i 62.053 65.000 i m +i 62.768 66.000 i m + i 62.470 67.000 i m + i 61.053 68.000 i m + i 58.498 69.000 i m + i 54.856 70.000 i m + i 50.271 71.000 i m + i 44.992 72.000 i m+ i 39.363 73.000 i + m i 33.810 74.000 i + m i 28.877 75.000 i + m i 25.219 76.000 i + m i 23.400 77.000 i + m i 23.440 78.000 i + m i 24.701 79.000 i + m i 26.348 80.000 i + m i 27.725 81.000 i + m i 28.463 82.000 i + m i 28.457 83.000 i + m i 27.798 84.000 i + m i 26.694 85.000 i + m i 25.380 86.000 i + m i 24.053 87.000 i + m i 22.852 88.000 i + m i 21.861 89.000 i + m i 21.115 90.000 i + m i 20.585 91.000 i + m i 20.228 92.000 i + m i 19.997 93.000 i+ m i 19.856 94.000 i+ m i 19.780 95.000 i+ m i 19.773 96.000 i+ m i 19.881 97.000 i + m i 20.173 98.000 i + m i 20.663 99.000 i + m i 21.322 100.00 i + m i 22.121 101.00 i + m i 23.043 102.00 i + m i 24.074 103.00 i + m i 25.170 104.00 i + m i 26.225 105.00 i + m i 27.089 106.00 i + m i 27.651 107.00 i + m i 27.926 108.00 i + m i 28.094 109.00 i + m i 28.456 110.00 i + m i 29.302 111.00 i + m i 30.831 112.00 i + m i 33.091 113.00 i + m i 35.983 114.00 i m i 39.318 115.00 i m + i 42.865 116.00 i m + i 46.383 117.00 i m + i 49.656 118.00 i m + i 52.532 119.00 i m + i 54.905 120.00 i m + i 56.646 121.00 i m + i 57.612 122.00 i m + i 57.717 123.00 i m + i 56.979 124.00 i m + i 55.517 125.00 i m + i 53.529 126.00 i m + i 51.257 127.00 i m + i 48.940 128.00 i m + i 46.789 129.00 i m + i 44.991 130.00 i m + i 43.671 131.00 i m + i 42.836 132.00 i m + i 42.376 133.00 i m + i 42.143 134.00 i m + i 41.978 135.00 i m + i 41.754 136.00 i m + i 41.392 137.00 i m + i 40.857 138.00 i m + i 40.153 139.00 i m+ i 39.349 140.00 i +m i 38.626 141.00 i + m i 38.249 142.00 i + m i 38.471 143.00 i m+ i 39.409 144.00 i m + i 41.011 145.00 i m + i 43.087 146.00 i m + i 45.406 147.00 i m + i 47.742 148.00 i m + i 49.872 149.00 i m + i 51.604 150.00 i m + i 52.815 151.00 i m + i 53.486 152.00 i m + i 53.642 153.00 i m + i 53.276 154.00 i m + i 52.344 155.00 i m + i 50.815 156.00 i m + i 48.720 157.00 i m + i 46.180 158.00 i m + i 43.393 159.00 i m + i 40.591 160.00 i + m i 37.983 161.00 i + m i 35.722 162.00 i + m i 33.921 163.00 i + m i 32.684 164.00 i + m i 32.106 165.00 i + m i 32.232 166.00 i + m i 32.994 167.00 i + m i 34.207 168.00 i + m i 35.618 169.00 i + m i 36.974 170.00 i + m i 38.080 171.00 i +m i 38.791 172.00 i m i 39.002 173.00 i +m i 38.724 174.00 i + m i 38.055 175.00 i + m i 37.128 176.00 i + m i 36.047 177.00 i + m i 34.859 178.00 i + m i 33.594 179.00 i + m i 32.295 180.00 i + m i 31.060 181.00 i + m i 29.998 182.00 i + m i 29.205 183.00 i + m i 28.723 184.00 i + m i 28.547 185.00 i + m i 28.668 186.00 i + m i 29.109 187.00 i + m i 29.919 188.00 i + m i 31.119 189.00 i + m i 32.649 190.00 i + m i 34.381 191.00 i + m i 36.171 192.00 i + m i 37.886 193.00 i m+ i 39.408 194.00 i m + i 40.621 195.00 i m + i 41.430 196.00 i m + i 41.815 197.00 i m + i 41.858 198.00 i m + i 41.681 199.00 i m + i 41.339 200.00 i m + i 40.812 201.00 i m + i 40.056 202.00 i m i 39.052 203.00 i + m i 37.838 204.00 i + m i 36.522 205.00 i + m i 35.275 206.00 i + m i 34.325 207.00 i + m i 33.954 208.00 i + m i 34.416 209.00 i + m i 35.740 210.00 i + m i 37.716 211.00 i m + i 40.017 212.00 i m + i 42.334 213.00 i m + i 44.438 214.00 i m + i 46.206 215.00 i m + i 47.597 216.00 i m + i 48.667 217.00 i m + i 49.589 218.00 i m + i 50.609 219.00 i m + i 51.878 220.00 i m + i 53.421 221.00 i m + i 55.145 1 starpac 2.08s (03/15/90) plot of phase of smoothed demodulated series location of zero is given by plot character 0 -6.2832 -5.0265 -3.7699 -2.5133 -1.2566 0.0000 1.2566 2.5133 3.7699 5.0265 6.2832 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i b 0 a i 2.0000 i b 0 a i 3.0000 i b 0 a i 4.0000 i b 0 a i 5.0000 i b 0 a i 6.0000 i b 0 a i 7.0000 i b 0 a i 8.0000 i a 0 b i 9.0000 i a 0 b i 10.000 i a 0 b i 11.000 i a 0 b i 12.000 i a 0 b i 13.000 i a 0 b i 14.000 i a 0 b i 15.000 i a 0 b i 16.000 i a 0 b i 17.000 i a 0 b i 18.000 i a 0 b i 19.000 i a 0 b i 20.000 i a 0 b i 21.000 i a 0 b i 22.000 i a 0 b i 23.000 i a 0 b i 24.000 i a 0 b i 25.000 i a 0 b i 26.000 i a 0 b i 27.000 i b 0 a i 28.000 i b 0 a i 29.000 i b 0 a i 30.000 i b 0 a i 31.000 i b 0 a i 32.000 i b 0 a i 33.000 i b 0 a i 34.000 i b 0 a i 35.000 i b 0 a i 36.000 i b 0 a i 37.000 i b 0 a i 38.000 i b 0 a i 39.000 i b 0 a i 40.000 i b 0 a i 41.000 i a 0 b i 42.000 i a 0 b i 43.000 i a 0 b i 44.000 i a 0 b i 45.000 i a 0 b i 46.000 i a 0 b i 47.000 i a 0 b i 48.000 i a 0 b i 49.000 i a 0 b i 50.000 i a 0 b i 51.000 i a 0 b i 52.000 i a 0 b i 53.000 i a 0 b i 54.000 i a 0 b i 55.000 i a 0 b i 56.000 i a 0 b i 57.000 i a 0 b i 58.000 i a 0 b i 59.000 i a 0 b i 60.000 i a 0 b i 61.000 i a 0 b i 62.000 i a 0 b i 63.000 i a 0 b i 64.000 i a 0 b i 65.000 i a 0 b i 66.000 i a 0 b i 67.000 i a 0 b i 68.000 i a 0 b i 69.000 i a 0 b i 70.000 i a 0 b i 71.000 i a 0 b i 72.000 i a 0 b i 73.000 i a 0 b i 74.000 i a 0 b i 75.000 i a 0 b i 76.000 i a 0 b i 77.000 i a 0 b i 78.000 i a 0 b i 79.000 i a 0 b i 80.000 i a 0 b i 81.000 i a 0 b i 82.000 i a 0 b i 83.000 i a 0 b i 84.000 i a 0 b i 85.000 i a 0 b i 86.000 i a 0 b i 87.000 i a 0 b i 88.000 i a 0 b i 89.000 i a 0 b i 90.000 i a 0 b i 91.000 i b 0 a i 92.000 i b 0 a i 93.000 i b 0 a i 94.000 i b 0 a i 95.000 i b 0 a i 96.000 i b 0 a i 97.000 i b 0 a i 98.000 i b 0 a i 99.000 i b 0 a i 100.00 i b 0 a i 101.00 i b 0 a i 102.00 i b 0 a i 103.00 i b 0 a i 104.00 i b 0 a i 105.00 i b 0 a i 106.00 i b 0 a i 107.00 i b 0 a i 108.00 i b 0 a i 109.00 i b 0 a i 110.00 i b 0 a i 111.00 i b 0 a i 112.00 i b 0 a i 113.00 i b 0 a i 114.00 i b 0 a i 115.00 i b 0 a i 116.00 i b 0 a i 117.00 i b 0 a i 118.00 i b 0 a i 119.00 i a 0 b i 120.00 i a 0 b i 121.00 i a 0 b i 122.00 i a 0 b i 123.00 i a 0 b i 124.00 i a 0 b i 125.00 i a 0 b i 126.00 i a 0 b i 127.00 i a 0 b i 128.00 i a 0 b i 129.00 i b 0 a i 130.00 i b 0 a i 131.00 i b 0 a i 132.00 i b 0 a i 133.00 i b 0 a i 134.00 i b 0 a i 135.00 i b 0 a i 136.00 i b 0 a i 137.00 i b 0 a i 138.00 i b 0 a i 139.00 i b 0 a i 140.00 i b 0 a i 141.00 i b 0 a i 142.00 i b 0 a i 143.00 i b 0 a i 144.00 i b 0 a i 145.00 i b 0 a i 146.00 i b 0 a i 147.00 i b 0 a i 148.00 i b 0 a i 149.00 i b 0 a i 150.00 i b 0 a i 151.00 i b 0 a i 152.00 i b 0 a i 153.00 i b 0 a i 154.00 i b 0 a i 155.00 i b 0 a i 156.00 i b 0 a i 157.00 i b 0 a i 158.00 i b 0 a i 159.00 i b 0 a i 160.00 i b 0 a i 161.00 i b 0 a i 162.00 i b 0 a i 163.00 i b 0 a i 164.00 i b 0 a i 165.00 i b 0 a i 166.00 i b 0 a i 167.00 i b 0 a i 168.00 i b 0 a i 169.00 i b 0 a i 170.00 i b 0 a i 171.00 i b 0 a i 172.00 i b 0 a i 173.00 i b 0 a i 174.00 i b 0 a i 175.00 i b 0 a i 176.00 i b 0 a i 177.00 i b 0 a i 178.00 i b 0 a i 179.00 i b 0 a i 180.00 i b 0 a i 181.00 i b 0 a i 182.00 i b 0 a i 183.00 i b 0 a i 184.00 i b 0 a i 185.00 i b 0 a i 186.00 i b 0 a i 187.00 i b 0 a i 188.00 i b 0 a i 189.00 i b 0 a i 190.00 i b 0 a i 191.00 i b 0 a i 192.00 i b 0 a i 193.00 i b 0 a i 194.00 i b 0 a i 195.00 i b 0 a i 196.00 i b 0 a i 197.00 i b 0 a i 198.00 i b 0 a i 199.00 i b 0 a i 200.00 i b 0 a i 201.00 i b 0 a i 202.00 i b 0 a i 203.00 i b 0 a i 204.00 i b 0 a i 205.00 i b 0 a i 206.00 i b 0 a i 207.00 i b 0 a i 208.00 i b 0 a i 209.00 i b 0 a i 210.00 i b 0 a i 211.00 i b 0 a i 212.00 i b 0 a i 213.00 i b 0 a i 214.00 i b 0 a i 215.00 i b 0 a i 216.00 i b 0 a i 217.00 i b 0 a i 218.00 i b 0 a i 219.00 i b 0 a i 220.00 i b 0 a i 221.00 i b 0 a i ierr is 0 30.65236 32.84801 35.19140 37.58213 39.92463 42.14596 44.21624 46.15273 47.94784 49.52919 50.78563 51.63316 52.05232 52.07135 51.75401 51.17769 50.42882 49.59902 48.77622 48.01089 47.28660 46.51455 45.58681 44.44051 43.05541 41.46155 39.72085 37.93073 36.24462 34.85264 33.87748 33.30606 33.02027 32.86094 32.68965 32.39817 31.91286 31.23495 30.50040 29.92382 29.72081 30.05236 30.93594 32.23443 33.71852 35.14785 36.37529 37.43847 38.52048 39.81725 41.44477 43.39053 45.49313 47.50856 49.23145 50.58043 51.62479 52.54758 53.57317 54.90315 56.62077 58.60516 60.54899 62.05323 62.76799 62.46982 61.05269 58.49810 54.85617 50.27113 44.99237 39.36287 33.80972 28.87747 25.21916 23.40046 23.44030 24.70135 26.34761 27.72536 28.46300 28.45655 27.79762 26.69430 25.37975 24.05302 22.85221 21.86120 21.11450 20.58547 20.22758 19.99677 19.85584 19.78046 19.77328 19.88115 20.17254 20.66330 21.32190 22.12118 23.04334 24.07392 25.17017 26.22497 27.08897 27.65147 27.92640 28.09372 28.45561 29.30223 30.83067 33.09107 35.98285 39.31829 42.86547 46.38268 49.65563 52.53228 54.90488 56.64647 57.61187 57.71651 56.97857 55.51692 53.52917 51.25743 48.93957 46.78930 44.99147 43.67144 42.83606 42.37601 42.14346 41.97775 41.75367 41.39191 40.85741 40.15318 39.34856 38.62560 38.24945 38.47052 39.40946 41.01098 43.08701 45.40612 47.74196 49.87182 51.60350 52.81517 53.48635 53.64235 53.27644 52.34430 50.81488 48.72001 46.18026 43.39312 40.59063 37.98341 35.72187 33.92066 32.68384 32.10630 32.23162 32.99375 34.20653 35.61824 36.97416 38.08040 38.79059 39.00240 38.72445 38.05478 37.12846 36.04724 34.85857 33.59357 32.29520 31.05960 29.99815 29.20509 28.72307 28.54748 28.66810 29.10900 29.91902 31.11855 32.64877 34.38076 36.17096 37.88643 39.40781 40.62132 41.43026 41.81465 41.85822 41.68081 41.33918 40.81242 40.05594 39.05159 37.83762 36.52194 35.27519 34.32458 33.95447 34.41579 35.73959 37.71617 40.01672 42.33387 44.43802 46.20580 47.59742 48.66698 49.58868 50.60913 51.87788 53.42110 55.14523 2.85082 2.90418 2.95445 3.00045 3.04225 3.08050 3.11588 -3.13481 -3.10622 -3.08260 -3.06397 -3.04943 -3.03781 -3.02790 -3.01908 -3.01119 -3.00434 -2.99908 -2.99671 -2.99922 -3.00856 -3.02543 -3.04873 -3.07633 -3.10546 -3.13317 3.12651 3.10983 3.10169 3.10129 3.10424 3.10504 3.09992 3.08801 3.07139 3.05423 3.04176 3.04074 3.05823 3.09826 -3.12228 -3.04199 -2.95251 -2.86069 -2.76912 -2.67621 -2.57724 -2.46715 -2.34442 -2.21189 -2.07513 -1.94015 -1.81119 -1.68848 -1.56911 -1.44897 -1.32487 -1.19551 -1.06229 -0.92934 -0.80299 -0.68967 -0.59356 -0.51595 -0.45601 -0.41233 -0.38411 -0.37160 -0.37617 -0.40056 -0.44959 -0.53179 -0.65981 -0.84842 -1.10555 -1.41246 -1.71763 -1.97544 -2.17520 -2.32680 -2.44175 -2.52901 -2.59605 -2.64969 -2.69699 -2.74564 -2.80270 -2.87318 -2.95971 -3.06147 3.11013 2.99728 2.89137 2.79829 2.72115 2.65867 2.60359 2.54539 2.47655 2.39548 2.30562 2.21378 2.12822 2.05736 2.00926 1.99161 2.01130 2.07208 2.17108 2.29834 2.43978 2.58102 2.71177 2.82682 2.92479 3.00670 3.07448 3.12997 -3.10930 -3.07697 -3.05575 -3.04440 -3.04149 -3.04589 -3.05692 -3.07440 -3.09857 -3.12956 3.11571 3.07035 3.01682 2.95532 2.88792 2.81807 2.74985 2.68729 2.63397 2.59371 2.57145 2.57169 2.59509 2.63639 2.68615 2.73496 2.77664 2.80868 2.83067 2.84327 2.84790 2.84631 2.83976 2.82793 2.80901 2.78085 2.74210 2.69248 2.63282 2.56490 2.49183 2.41862 2.35240 2.30208 2.27537 2.27475 2.29568 2.32900 2.36503 2.39718 2.42254 2.43988 2.44885 2.44963 2.44340 2.43014 2.40767 2.37236 2.32053 2.25080 2.16455 2.06495 1.95685 1.84624 1.74031 1.64652 1.57081 1.51566 1.47910 1.45576 1.43976 1.42694 1.41542 1.40477 1.39546 1.38869 1.38677 1.39254 1.40752 1.43056 1.45875 1.48983 1.52306 1.55962 1.60212 1.65387 1.71795 1.79626 1.88686 1.98090 2.06560 2.13208 2.17853 2.20865 2.22851 2.24415 2.26099 2.28346 2.31382 2.35096 2.39138 2.43115 2.46799 test of demod starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine demod ------------------------------------- the input value of fc is 0.45454547E-01. the value of the argument fc must be between (1/k) and fd , inclusive. the correct form of the call statement is call demod (y, n, fd, fc, k, ldstak) ierr is 1 test of demods starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine demods ------------------------------------- the input value of fc is 0.45454547E-01. the value of the argument fc must be between (1/k) and fd , inclusive. the correct form of the call statement is call demods (y, n, fd, fc, k, + ampl, phas, ndem, nprt, ldstak) ierr is 1 test of demod starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine demod ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call demod (y, n, fd, fc, k, ldstak) ierr is 1 test of demods starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine demods ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call demods (y, n, fd, fc, k, + ampl, phas, ndem, nprt, ldstak) ierr is 1 test of lpcoef ierr is 0 -0.211E-03-0.974E-03-0.214E-02-0.357E-02-0.502E-02-0.616E-02-0.661E-02-0.596E-02-0.385E-02-0.147E-08 0.575E-02 0.134E-01 0.227E-01 0.333E-01 0.447E-01 0.561E-01 0.668E-01 0.761E-01 0.833E-01 0.878E-01 0.894E-01 0.878E-01 0.833E-01 0.761E-01 0.668E-01 0.561E-01 0.447E-01 0.333E-01 0.227E-01 0.134E-01 0.575E-02-0.147E-08-0.385E-02-0.596E-02-0.661E-02-0.616E-02-0.502E-02-0.357E-02-0.214E-02-0.974E-03 -0.211E-03 test of lopass ierr is 0 -0.211E-03-0.974E-03-0.214E-02-0.357E-02-0.502E-02-0.616E-02-0.661E-02-0.596E-02-0.385E-02-0.147E-08 0.575E-02 0.134E-01 0.227E-01 0.333E-01 0.447E-01 0.561E-01 0.668E-01 0.761E-01 0.833E-01 0.878E-01 0.894E-01 0.878E-01 0.833E-01 0.761E-01 0.668E-01 0.561E-01 0.447E-01 0.333E-01 0.227E-01 0.134E-01 0.575E-02-0.147E-08-0.385E-02-0.596E-02-0.661E-02-0.616E-02-0.502E-02-0.357E-02-0.214E-02-0.974E-03 -0.211E-03 0.383E+02 0.412E+02 0.438E+02 0.460E+02 0.479E+02 0.495E+02 0.508E+02 0.518E+02 0.526E+02 0.533E+02 0.538E+02 0.541E+02 0.542E+02 0.541E+02 0.538E+02 0.534E+02 0.527E+02 0.519E+02 0.510E+02 0.499E+02 0.489E+02 0.478E+02 0.466E+02 0.455E+02 0.443E+02 0.431E+02 0.421E+02 0.411E+02 0.403E+02 0.397E+02 0.393E+02 0.391E+02 0.392E+02 0.393E+02 0.396E+02 0.400E+02 0.406E+02 0.413E+02 0.422E+02 0.432E+02 0.443E+02 0.456E+02 0.470E+02 0.485E+02 0.501E+02 0.518E+02 0.536E+02 0.553E+02 0.569E+02 0.584E+02 0.598E+02 0.610E+02 0.620E+02 0.630E+02 0.639E+02 0.647E+02 0.656E+02 0.665E+02 0.675E+02 0.686E+02 0.698E+02 0.711E+02 0.724E+02 0.736E+02 0.745E+02 0.750E+02 0.749E+02 0.740E+02 0.723E+02 0.697E+02 0.662E+02 0.621E+02 0.575E+02 0.526E+02 0.477E+02 0.429E+02 0.384E+02 0.343E+02 0.308E+02 0.278E+02 0.253E+02 0.235E+02 0.222E+02 0.213E+02 0.207E+02 0.203E+02 0.200E+02 0.197E+02 0.192E+02 0.188E+02 0.183E+02 0.178E+02 0.174E+02 0.172E+02 0.171E+02 0.173E+02 0.177E+02 0.183E+02 0.190E+02 0.199E+02 0.208E+02 0.220E+02 0.233E+02 0.250E+02 0.271E+02 0.296E+02 0.326E+02 0.361E+02 0.400E+02 0.440E+02 0.480E+02 0.518E+02 0.552E+02 0.581E+02 0.605E+02 0.622E+02 0.634E+02 0.641E+02 0.644E+02 0.644E+02 0.642E+02 0.638E+02 0.633E+02 0.627E+02 0.620E+02 0.612E+02 0.603E+02 0.593E+02 0.583E+02 0.572E+02 0.561E+02 0.549E+02 0.537E+02 0.525E+02 0.512E+02 0.501E+02 0.491E+02 0.483E+02 0.480E+02 0.481E+02 0.487E+02 0.499E+02 0.514E+02 0.533E+02 0.551E+02 0.568E+02 0.582E+02 0.590E+02 0.593E+02 0.589E+02 0.580E+02 0.565E+02 0.547E+02 0.524E+02 0.499E+02 0.473E+02 0.446E+02 0.420E+02 0.395E+02 0.373E+02 0.356E+02 0.343E+02 0.336E+02 0.336E+02 0.340E+02 0.349E+02 0.361E+02 0.374E+02 0.385E+02 0.395E+02 0.401E+02 0.403E+02 0.401E+02 0.397E+02 0.390E+02 0.382E+02 0.373E+02 0.364E+02 0.354E+02 0.345E+02 0.335E+02 0.326E+02 0.319E+02 0.313E+02 0.309E+02 0.309E+02 0.311E+02 0.317E+02 0.324E+02 0.333E+02 0.343E+02 0.354E+02 0.364E+02 0.374E+02 0.384E+02 0.393E+02 0.403E+02 0.413E+02 0.421E+02 0.428E+02 0.432E+02 0.433E+02 0.431E+02 0.428E+02 0.423E+02 0.419E+02 0.416E+02 0.415E+02 0.416E+02 0.421E+02 0.429E+02 0.439E+02 0.452E+02 0.468E+02 0.486E+02 0.506E+02 0.528E+02 0.550E+02 0.572E+02 0.594E+02 0.614E+02 test of hipass ierr is 0 0.211E-03 0.974E-03 0.214E-02 0.357E-02 0.502E-02 0.616E-02 0.661E-02 0.596E-02 0.385E-02 0.147E-08 -0.575E-02-0.134E-01-0.227E-01-0.333E-01-0.447E-01-0.561E-01-0.668E-01-0.761E-01-0.833E-01-0.878E-01 0.911E+00-0.878E-01-0.833E-01-0.761E-01-0.668E-01-0.561E-01-0.447E-01-0.333E-01-0.227E-01-0.134E-01 -0.575E-02 0.147E-08 0.385E-02 0.596E-02 0.661E-02 0.616E-02 0.502E-02 0.357E-02 0.214E-02 0.974E-03 0.211E-03 -0.103E+02-0.152E+02-0.218E+02-0.350E+02-0.269E+02-0.949E+01 0.272E+02 0.702E+02 0.504E+02 0.197E+02 -0.680E+01-0.191E+02-0.432E+02-0.491E+02-0.378E+02-0.194E+02 0.173E+02 0.291E+02 0.600E+02 0.511E+02 0.241E+02-0.778E+01-0.266E+02-0.295E+02-0.393E+02-0.321E+02-0.201E+02-0.109E+01 0.197E+02 0.412E+02 0.441E+02 0.855E+01 0.865E+01-0.861E+01-0.274E+02-0.304E+02-0.304E+02-0.893E+01 0.540E+01 0.108E+02 0.186E+02 0.403E+02 0.142E+02-0.342E+01-0.137E+02-0.309E+02-0.422E+02-0.175E+02 0.129E+02 0.477E+02 0.410E+02 0.206E+02 0.449E+01-0.282E+02-0.333E+02-0.577E+02-0.458E+02 0.260E+02 0.869E+02 0.573E+02 0.150E+02-0.297E+01-0.339E+02-0.508E+02-0.643E+02-0.509E+02 0.803E+01 0.580E+02 0.586E+02 0.484E+02 0.237E+02 0.450E+01 0.252E+01-0.569E+01-0.665E+01-0.216E+02-0.224E+02-0.279E+02-0.267E+02-0.210E+02 -0.108E+02 0.105E+02 0.228E+02 0.218E+02 0.268E+02 0.219E+02 0.809E+01-0.956E+01-0.111E+02-0.163E+02 -0.183E+02-0.164E+02-0.124E+02-0.498E+01-0.324E+01 0.181E+02 0.281E+02 0.228E+02 0.111E+02 0.404E+01 -0.523E+01-0.154E+02-0.193E+02-0.232E+02-0.186E+02-0.130E+02 0.366E+01 0.135E+02 0.242E+02 0.230E+02 0.229E+02-0.396E+01-0.277E+02-0.496E+02-0.473E+02-0.532E+01 0.581E+02 0.742E+02 0.388E+02 0.213E+02 0.406E+00-0.271E+02-0.391E+02-0.520E+02-0.470E+02-0.211E+02 0.123E+01 0.392E+02 0.664E+02 0.391E+02 0.105E+02 0.957E+01 0.390E+00-0.135E+02-0.306E+02-0.434E+02-0.448E+02-0.256E+02 0.683E+01 0.457E+02 0.471E+02 0.273E+02 0.767E+01-0.925E+01-0.812E+01-0.263E+02-0.419E+02-0.517E+02-0.217E+02 0.151E+02 0.810E+02 0.547E+02 0.469E+02 0.138E+02-0.524E+01-0.303E+02-0.333E+02-0.296E+02-0.361E+02-0.313E+02 -0.327E+01 0.200E+02 0.261E+02 0.301E+02 0.295E+02 0.173E+02-0.107E+02-0.243E+02-0.317E+02-0.332E+02 -0.330E+02-0.468E+01 0.329E+02 0.454E+02 0.390E+02 0.258E+02 0.449E+01-0.102E+02-0.874E+01-0.224E+02 -0.240E+02-0.299E+02-0.269E+02-0.688E+01 0.111E+02 0.326E+02 0.227E+02 0.303E+02 0.161E+02 0.106E+02 -0.157E+02-0.297E+02-0.328E+02-0.360E+02-0.288E+02 0.805E+01 0.168E+02 0.626E+02 0.385E+02 0.208E+02 -0.556E+01-0.172E+02-0.289E+02-0.370E+02-0.256E+02 0.243E+01 0.223E+02 0.275E+02 0.362E+02 0.228E+02 -0.716E+01-0.227E+02-0.341E+02-0.411E+02-0.399E+02-0.145E+02 0.269E+02 0.594E+02 0.524E+02 0.294E+02 0.643E+01 test of hpcoef ierr is 0 0.211E-03 0.974E-03 0.214E-02 0.357E-02 0.502E-02 0.616E-02 0.661E-02 0.596E-02 0.385E-02 0.147E-08 -0.575E-02-0.134E-01-0.227E-01-0.333E-01-0.447E-01-0.561E-01-0.668E-01-0.761E-01-0.833E-01-0.878E-01 0.911E+00-0.878E-01-0.833E-01-0.761E-01-0.668E-01-0.561E-01-0.447E-01-0.333E-01-0.227E-01-0.134E-01 -0.575E-02 0.147E-08 0.385E-02 0.596E-02 0.661E-02 0.616E-02 0.502E-02 0.357E-02 0.214E-02 0.974E-03 0.211E-03 test of maflt ierr is 0 0.384E+02 0.393E+02 0.395E+02 0.395E+02 0.391E+02 0.385E+02 0.376E+02 0.379E+02 0.388E+02 0.406E+02 0.424E+02 0.435E+02 0.447E+02 0.454E+02 0.457E+02 0.456E+02 0.452E+02 0.449E+02 0.445E+02 0.443E+02 0.449E+02 0.463E+02 0.472E+02 0.477E+02 0.484E+02 0.484E+02 0.477E+02 0.467E+02 0.454E+02 0.455E+02 0.462E+02 0.470E+02 0.478E+02 0.484E+02 0.490E+02 0.488E+02 0.484E+02 0.490E+02 0.508E+02 0.511E+02 0.507E+02 0.506E+02 0.506E+02 0.506E+02 0.505E+02 0.510E+02 0.527E+02 0.554E+02 0.576E+02 0.590E+02 0.593E+02 0.588E+02 0.591E+02 0.591E+02 0.594E+02 0.596E+02 0.597E+02 0.597E+02 0.590E+02 0.580E+02 0.570E+02 0.563E+02 0.553E+02 0.549E+02 0.549E+02 0.551E+02 0.552E+02 0.552E+02 0.545E+02 0.528E+02 0.503E+02 0.478E+02 0.460E+02 0.446E+02 0.441E+02 0.442E+02 0.452E+02 0.457E+02 0.442E+02 0.410E+02 0.383E+02 0.364E+02 0.348E+02 0.339E+02 0.336E+02 0.338E+02 0.341E+02 0.332E+02 0.316E+02 0.300E+02 0.289E+02 0.279E+02 0.269E+02 0.256E+02 0.248E+02 0.252E+02 0.277E+02 0.306E+02 0.330E+02 0.350E+02 0.364E+02 0.369E+02 0.367E+02 0.359E+02 0.352E+02 0.350E+02 0.355E+02 0.372E+02 0.400E+02 0.421E+02 0.437E+02 0.453E+02 0.466E+02 0.474E+02 0.476E+02 0.474E+02 0.467E+02 0.461E+02 0.464E+02 0.480E+02 0.497E+02 0.512E+02 0.525E+02 0.535E+02 0.546E+02 0.551E+02 0.551E+02 0.544E+02 0.541E+02 0.544E+02 0.561E+02 0.571E+02 0.584E+02 0.594E+02 0.602E+02 0.603E+02 0.592E+02 0.566E+02 0.533E+02 0.509E+02 0.496E+02 0.493E+02 0.499E+02 0.509E+02 0.522E+02 0.531E+02 0.527E+02 0.515E+02 0.493E+02 0.464E+02 0.442E+02 0.435E+02 0.437E+02 0.444E+02 0.454E+02 0.464E+02 0.473E+02 0.478E+02 0.479E+02 0.469E+02 0.448E+02 0.426E+02 0.408E+02 0.400E+02 0.399E+02 0.403E+02 0.409E+02 0.420E+02 0.430E+02 0.431E+02 0.418E+02 0.385E+02 0.359E+02 0.335E+02 0.321E+02 0.322E+02 0.331E+02 0.354E+02 0.371E+02 0.385E+02 0.393E+02 0.391E+02 0.382E+02 0.369E+02 0.357E+02 0.352E+02 0.355E+02 0.366E+02 0.382E+02 0.396E+02 0.403E+02 0.406E+02 0.400E+02 0.384E+02 0.365E+02 0.355E+02 0.359E+02 0.377E+02 0.397E+02 0.412E+02 0.426E+02 0.435E+02 0.442E+02 0.445E+02 0.441E+02 0.439E+02 0.446E+02 0.470E+02 0.488E+02 0.509E+02 0.519E+02 0.531E+02 0.537E+02 0.540E+02 0.541E+02 0.548E+02 0.571E+02 0.603E+02 0.623E+02 0.642E+02 0.654E+02 test of slflt ierr is 0 0.383E+02 0.412E+02 0.438E+02 0.460E+02 0.479E+02 0.495E+02 0.508E+02 0.518E+02 0.526E+02 0.533E+02 0.538E+02 0.541E+02 0.542E+02 0.541E+02 0.538E+02 0.534E+02 0.527E+02 0.519E+02 0.510E+02 0.499E+02 0.489E+02 0.478E+02 0.466E+02 0.455E+02 0.443E+02 0.431E+02 0.421E+02 0.411E+02 0.403E+02 0.397E+02 0.393E+02 0.391E+02 0.392E+02 0.393E+02 0.396E+02 0.400E+02 0.406E+02 0.413E+02 0.422E+02 0.432E+02 0.443E+02 0.456E+02 0.470E+02 0.485E+02 0.501E+02 0.518E+02 0.536E+02 0.553E+02 0.569E+02 0.584E+02 0.598E+02 0.610E+02 0.620E+02 0.630E+02 0.639E+02 0.647E+02 0.656E+02 0.665E+02 0.675E+02 0.686E+02 0.698E+02 0.711E+02 0.724E+02 0.736E+02 0.745E+02 0.750E+02 0.749E+02 0.740E+02 0.723E+02 0.697E+02 0.662E+02 0.621E+02 0.575E+02 0.526E+02 0.477E+02 0.429E+02 0.384E+02 0.343E+02 0.308E+02 0.278E+02 0.253E+02 0.235E+02 0.222E+02 0.213E+02 0.207E+02 0.203E+02 0.200E+02 0.197E+02 0.192E+02 0.188E+02 0.183E+02 0.178E+02 0.174E+02 0.172E+02 0.171E+02 0.173E+02 0.177E+02 0.183E+02 0.190E+02 0.199E+02 0.208E+02 0.220E+02 0.233E+02 0.250E+02 0.271E+02 0.296E+02 0.326E+02 0.361E+02 0.400E+02 0.440E+02 0.480E+02 0.518E+02 0.552E+02 0.581E+02 0.605E+02 0.622E+02 0.634E+02 0.641E+02 0.644E+02 0.644E+02 0.642E+02 0.638E+02 0.633E+02 0.627E+02 0.620E+02 0.612E+02 0.603E+02 0.593E+02 0.583E+02 0.572E+02 0.561E+02 0.549E+02 0.537E+02 0.525E+02 0.512E+02 0.501E+02 0.491E+02 0.483E+02 0.480E+02 0.481E+02 0.487E+02 0.499E+02 0.514E+02 0.533E+02 0.551E+02 0.568E+02 0.582E+02 0.590E+02 0.593E+02 0.589E+02 0.580E+02 0.565E+02 0.547E+02 0.524E+02 0.499E+02 0.473E+02 0.446E+02 0.420E+02 0.395E+02 0.373E+02 0.356E+02 0.343E+02 0.336E+02 0.336E+02 0.340E+02 0.349E+02 0.361E+02 0.374E+02 0.385E+02 0.395E+02 0.401E+02 0.403E+02 0.401E+02 0.397E+02 0.390E+02 0.382E+02 0.373E+02 0.364E+02 0.354E+02 0.345E+02 0.335E+02 0.326E+02 0.319E+02 0.313E+02 0.309E+02 0.309E+02 0.311E+02 0.317E+02 0.324E+02 0.333E+02 0.343E+02 0.354E+02 0.364E+02 0.374E+02 0.384E+02 0.393E+02 0.403E+02 0.413E+02 0.421E+02 0.428E+02 0.432E+02 0.433E+02 0.431E+02 0.428E+02 0.423E+02 0.419E+02 0.416E+02 0.415E+02 0.416E+02 0.421E+02 0.429E+02 0.439E+02 0.452E+02 0.468E+02 0.486E+02 0.506E+02 0.528E+02 0.550E+02 0.572E+02 0.594E+02 0.614E+02 test of sample ierr is 0 0.383E+02 0.412E+02 0.438E+02 0.460E+02 0.479E+02 0.495E+02 0.508E+02 test of arflt ierr is 0 -0.108E+02-0.938E+01-0.538E+01 0.342E+01 0.176E+02-0.246E+02-0.162E+02-0.208E+02-0.168E+02-0.206E+02 -0.206E+02-0.188E+02-0.168E+02-0.898E+01 0.162E+01 0.120E+02 0.160E+02 0.342E+01-0.158E+02-0.142E+02 -0.958E+01-0.124E+02-0.210E+02-0.438E+01 0.862E+01 0.352E+02 0.564E+02 0.110E+02-0.758E+01-0.156E+02 -0.120E+02-0.288E+02-0.204E+02-0.578E+01 0.562E+01 0.308E+02 0.202E+02 0.436E+02 0.156E+02-0.638E+01 -0.226E+02-0.228E+02-0.148E+02-0.234E+02-0.108E+02-0.338E+01 0.802E+01 0.172E+02 0.261E+02 0.161E+02 -0.211E+02 0.403E+00-0.168E+02-0.250E+02-0.165E+02-0.143E+02 0.750E+01 0.938E+01 0.666E+01 0.117E+02 0.294E+02-0.912E+01-0.104E+02-0.944E+01-0.197E+02-0.199E+02 0.122E+02 0.283E+02 0.454E+02 0.184E+02 0.234E+01-0.124E+01-0.239E+02-0.906E+01-0.301E+02-0.318E+01 0.618E+02 0.801E+02 0.145E+02-0.952E+01 -0.156E+01-0.211E+02-0.191E+02-0.223E+02-0.797E+00 0.497E+02 0.635E+02 0.329E+02 0.208E+02 0.263E+00 -0.612E+01 0.126E+01-0.788E+01-0.592E+01-0.221E+02-0.156E+02-0.220E+02-0.185E+02-0.144E+02-0.836E+01 0.652E+01 0.582E+01-0.268E+01 0.286E+01-0.508E+01-0.160E+02-0.255E+02-0.167E+02-0.211E+02-0.203E+02 -0.174E+02-0.146E+02-0.958E+01-0.122E+02 0.828E+01 0.578E+01-0.516E+01-0.133E+02-0.129E+02-0.175E+02 -0.215E+02-0.187E+02-0.194E+02-0.114E+02-0.728E+01 0.756E+01 0.904E+01 0.157E+02 0.970E+01 0.119E+02 -0.135E+02-0.200E+02-0.268E+02-0.107E+02 0.302E+02 0.686E+02 0.466E+02 0.144E+01 0.500E+01-0.560E+01 -0.208E+02-0.166E+02-0.226E+02-0.102E+02 0.123E+02 0.187E+02 0.428E+02 0.468E+02 0.270E+01-0.996E+01 0.576E+01-0.338E+01-0.122E+02-0.216E+02-0.244E+02-0.185E+02 0.134E+01 0.224E+02 0.421E+02 0.207E+02 0.943E+00-0.600E+01-0.102E+02 0.182E+01-0.165E+02-0.208E+02-0.213E+02 0.144E+02 0.327E+02 0.758E+02 0.902E+01 0.161E+02-0.135E+02-0.138E+02-0.286E+02-0.177E+02-0.132E+02-0.228E+02-0.148E+02 0.992E+01 0.161E+02 0.834E+01 0.910E+01 0.650E+01-0.468E+01-0.247E+02-0.209E+02-0.198E+02-0.166E+02-0.155E+02 0.126E+02 0.329E+02 0.225E+02 0.816E+01-0.158E+01-0.154E+02-0.177E+02-0.780E+01-0.227E+02-0.165E+02 -0.218E+02-0.154E+02 0.262E+01 0.858E+01 0.195E+02-0.308E+01 0.109E+02-0.748E+01-0.398E+01-0.265E+02 -0.242E+02-0.186E+02-0.195E+02-0.100E+02 0.229E+02 0.988E+01 0.509E+02-0.517E+00-0.354E+01-0.193E+02 -0.152E+02-0.202E+02-0.215E+02-0.556E+01 0.155E+02 0.185E+02 0.119E+02 0.176E+02-0.557E+00-0.220E+02 -0.190E+02-0.204E+02-0.197E+02-0.135E+02 0.121E+02 0.393E+02 0.478E+02 0.222E+02 0.426E+01-0.426E+01 -0.120E+02-0.167E+02-0.208E+02-0.190E+02 0.866E+01 0.539E+02 0.773E+02 0.266E+02 0.341E+02-0.157E+02 0.283E+00-0.289E+02-0.238E+02-0.227E+02 0.166E+02 0.100E+03 0.864E+02 0.519E+02 0.293E+02-0.188E+01 test of dif ierr is 0 0.600E+01 0.500E+01 0.700E+01 0.130E+02 0.220E+02-0.290E+02-0.900E+01-0.100E+02-0.200E+01-0.500E+01 -0.300E+01 0.000E+00 0.200E+01 0.900E+01 0.160E+02 0.200E+02 0.160E+02-0.300E+01-0.210E+02-0.110E+02 -0.200E+01-0.400E+01-0.110E+02 0.100E+02 0.190E+02 0.380E+02 0.440E+02-0.190E+02-0.300E+02-0.260E+02 -0.120E+02-0.240E+02-0.600E+01 0.110E+02 0.180E+02 0.360E+02 0.110E+02 0.300E+02-0.100E+02-0.280E+02 -0.330E+02-0.200E+02-0.400E+01-0.110E+02 0.600E+01 0.110E+02 0.180E+02 0.200E+02 0.209E+02 0.250E+01 -0.357E+02 0.100E+00-0.171E+02-0.185E+02-0.260E+01 0.600E+00 0.222E+02 0.152E+02 0.640E+01 0.890E+01 0.230E+02-0.247E+02-0.161E+02-0.870E+01-0.155E+02-0.950E+01 0.264E+02 0.320E+02 0.363E+02-0.530E+01 -0.192E+02-0.151E+02-0.317E+02-0.420E+01-0.236E+02 0.128E+02 0.727E+02 0.619E+02-0.285E+02-0.411E+02 -0.167E+02-0.296E+02-0.157E+02-0.126E+02 0.139E+02 0.588E+02 0.491E+02-0.110E+01-0.128E+02-0.282E+02 -0.233E+02-0.660E+01-0.131E+02-0.590E+01-0.197E+02-0.530E+01-0.960E+01-0.230E+01 0.270E+01 0.770E+01 0.195E+02 0.110E+02-0.190E+01 0.440E+01-0.530E+01-0.141E+02-0.180E+02-0.200E+01-0.560E+01-0.250E+01 0.140E+01 0.360E+01 0.720E+01 0.170E+01 0.215E+02 0.104E+02-0.470E+01-0.110E+02-0.620E+01-0.830E+01 -0.900E+01-0.260E+01-0.220E+01 0.670E+01 0.810E+01 0.197E+02 0.133E+02 0.146E+02 0.280E+01 0.390E+01 -0.231E+02-0.203E+02-0.190E+02 0.470E+01 0.437E+02 0.646E+02 0.168E+02-0.351E+02-0.175E+02-0.211E+02 -0.279E+02-0.125E+02-0.135E+02 0.430E+01 0.251E+02 0.214E+02 0.370E+02 0.262E+02-0.284E+02-0.297E+02 -0.210E+01-0.104E+02-0.151E+02-0.184E+02-0.139E+02-0.240E+01 0.184E+02 0.321E+02 0.390E+02 0.200E+01 -0.186E+02-0.181E+02-0.151E+02 0.300E+01-0.165E+02-0.142E+02-0.900E+01 0.303E+02 0.364E+02 0.650E+02 -0.278E+02-0.960E+01-0.354E+02-0.215E+02-0.277E+02-0.570E+01 0.110E+01-0.900E+01 0.260E+01 0.263E+02 0.220E+02 0.540E+01 0.400E+01-0.200E+00-0.113E+02-0.268E+02-0.123E+02-0.630E+01-0.500E+00 0.800E+00 0.285E+02 0.374E+02 0.121E+02-0.710E+01-0.140E+02-0.222E+02-0.156E+02 0.500E+00-0.146E+02-0.260E+01 -0.680E+01 0.230E+01 0.194E+02 0.176E+02 0.215E+02-0.970E+01 0.820E+01-0.135E+02-0.460E+01-0.253E+02 -0.129E+02-0.210E+01-0.220E+01 0.820E+01 0.378E+02 0.970E+01 0.468E+02-0.233E+02-0.170E+02-0.260E+02 -0.115E+02-0.119E+02-0.840E+01 0.109E+02 0.276E+02 0.196E+02 0.510E+01 0.880E+01-0.129E+02-0.292E+02 -0.145E+02-0.101E+02-0.540E+01 0.300E+01 0.274E+02 0.436E+02 0.347E+02-0.480E+01-0.208E+02-0.210E+02 -0.203E+02-0.169E+02-0.143E+02-0.670E+01 0.236E+02 0.594E+02 0.590E+02-0.153E+02-0.160E+01-0.508E+02 -0.145E+02-0.379E+02-0.176E+02-0.950E+01 0.336E+02 0.104E+03 0.485E+02-0.540E+01-0.258E+02-0.467E+02 test of difm ierr is 0 0.340E+39 0.340E+39 0.700E+01 0.130E+02 0.220E+02-0.290E+02-0.900E+01-0.100E+02-0.200E+01-0.500E+01 -0.300E+01 0.000E+00 0.200E+01 0.340E+39 0.340E+39 0.200E+02 0.160E+02-0.300E+01-0.210E+02-0.110E+02 -0.200E+01-0.400E+01 0.340E+39 0.340E+39 0.190E+02 0.380E+02 0.440E+02-0.190E+02-0.300E+02-0.260E+02 -0.120E+02 0.340E+39 0.340E+39 0.110E+02 0.180E+02 0.360E+02 0.110E+02 0.300E+02-0.100E+02-0.280E+02 -0.330E+02-0.200E+02-0.400E+01-0.110E+02 0.340E+39 0.340E+39 0.180E+02 0.200E+02 0.209E+02 0.250E+01 -0.357E+02 0.100E+00-0.171E+02-0.185E+02-0.260E+01 0.600E+00 0.222E+02 0.152E+02 0.640E+01 0.890E+01 0.230E+02-0.247E+02-0.161E+02-0.870E+01-0.155E+02-0.950E+01 0.264E+02 0.320E+02 0.363E+02-0.530E+01 -0.192E+02-0.151E+02-0.317E+02-0.420E+01-0.236E+02 0.128E+02 0.727E+02 0.619E+02-0.285E+02-0.411E+02 -0.167E+02-0.296E+02-0.157E+02-0.126E+02 0.139E+02 0.588E+02 0.491E+02-0.110E+01-0.128E+02-0.282E+02 -0.233E+02-0.660E+01-0.131E+02-0.590E+01-0.197E+02-0.530E+01-0.960E+01-0.230E+01 0.270E+01 0.770E+01 0.195E+02 0.110E+02-0.190E+01 0.440E+01-0.530E+01-0.141E+02-0.180E+02-0.200E+01-0.560E+01-0.250E+01 0.140E+01 0.360E+01 0.720E+01 0.170E+01 0.215E+02 0.104E+02-0.470E+01-0.110E+02-0.620E+01-0.830E+01 -0.900E+01-0.260E+01-0.220E+01 0.670E+01 0.810E+01 0.197E+02 0.133E+02 0.146E+02 0.280E+01 0.390E+01 -0.231E+02-0.203E+02-0.190E+02 0.470E+01 0.437E+02 0.646E+02 0.168E+02-0.351E+02-0.175E+02-0.211E+02 -0.279E+02-0.125E+02-0.135E+02 0.430E+01 0.251E+02 0.214E+02 0.370E+02 0.262E+02-0.284E+02-0.297E+02 -0.210E+01-0.104E+02-0.151E+02-0.184E+02-0.139E+02-0.240E+01 0.184E+02 0.321E+02 0.390E+02 0.200E+01 -0.186E+02-0.181E+02-0.151E+02 0.300E+01-0.165E+02-0.142E+02-0.900E+01 0.303E+02 0.364E+02 0.650E+02 -0.278E+02-0.960E+01-0.354E+02-0.215E+02-0.277E+02-0.570E+01 0.110E+01-0.900E+01 0.260E+01 0.263E+02 0.220E+02 0.540E+01 0.400E+01-0.200E+00-0.113E+02-0.268E+02-0.123E+02-0.630E+01-0.500E+00 0.800E+00 0.285E+02 0.374E+02 0.121E+02-0.710E+01-0.140E+02-0.222E+02-0.156E+02 0.500E+00-0.146E+02-0.260E+01 -0.680E+01 0.230E+01 0.194E+02 0.176E+02 0.215E+02-0.970E+01 0.820E+01-0.135E+02-0.460E+01-0.253E+02 -0.129E+02-0.210E+01-0.220E+01 0.820E+01 0.378E+02 0.970E+01 0.468E+02-0.233E+02-0.170E+02-0.260E+02 -0.115E+02-0.119E+02-0.840E+01 0.109E+02 0.276E+02 0.196E+02 0.510E+01 0.880E+01-0.129E+02-0.292E+02 -0.145E+02-0.101E+02-0.540E+01 0.300E+01 0.274E+02 0.436E+02 0.347E+02-0.480E+01-0.208E+02-0.210E+02 -0.203E+02-0.169E+02-0.143E+02-0.670E+01 0.236E+02 0.594E+02 0.590E+02-0.153E+02-0.160E+01-0.508E+02 -0.145E+02-0.379E+02-0.176E+02-0.950E+01 0.336E+02 0.104E+03 0.485E+02-0.540E+01-0.258E+02-0.467E+02 0.340E+39 test of gfslf starpac 2.08s (03/15/90) gain function of 41 term symmetric linear filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ++++++ - i ++ i i + i i + i i + i -5.7476 - + - i i i + i i i i + i -11.4952 - - i i i + i i i i i -17.2428 - - i i i + + i i + i i + i -22.9904 - - i ++ i i + i i + + i i + i -28.7380 - + + + - i + + i i + ++ i i + ++ i i + i -34.4856 - ++ + + + + - i + + + + + i i + + + + + i i + ++ i i + + ++ i -40.2332 - + + ++ ++ - i + + + + + ++ i i + + + + + + ++ ++ + i i + + + + + i i + + i -45.9808 - + - i + i i + + i i i i + i -51.7284 - + + - i i i + i i i i + i -57.4760 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of gfarf starpac 2.08s (03/15/90) gain function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++ - i ++++++++ i i ++++++ i i ++++ i i ++++ i -0.6021 - +++ - i +++ i i +++ i i ++ i i ++ i -1.2041 - +++ - i ++ i i ++ i i + i i ++ i -1.8062 - ++ - i + i i ++ i i ++ i i + i -2.4082 - + - i ++ i i + i i + i i + i -3.0103 - ++ - i + i i + i i + i i + i -3.6124 - + - i + i i + i i + i i + i -4.2144 - + - i + i i + i i + i i + i -4.8165 - + - i + i i + i i + i i + i -5.4185 - + - i + i i + i i ++ i i ++ i -6.0206 - +++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) phase function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.1416 - - i i i i i i i i 2.5133 - - i i i i i i i i 1.8850 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.6283 - - i i i i i i i i 0.0000 - + +++++ - i +++ +++++++++++ i i +++ +++++++++++ i i ++++ ++++++++++++ i i ++++++ ++++++++++++++ i -0.6283 - +++++++++++++++++++++++++++++ - i i i i i i i i -1.2566 - - i i i i i + + i i i -1.8850 - - i i i i i i i i -2.5133 - - i i i i i i i i -3.1416 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of difc ierr is 0 0.100E+01 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.600E+01 0.500E+01 0.700E+01 0.130E+02 0.220E+02-0.290E+02-0.900E+01-0.100E+02-0.200E+01-0.500E+01 -0.300E+01 0.000E+00 0.200E+01 0.900E+01 0.160E+02 0.200E+02 0.160E+02-0.300E+01-0.210E+02-0.110E+02 -0.200E+01-0.400E+01-0.110E+02 0.100E+02 0.190E+02 0.380E+02 0.440E+02-0.190E+02-0.300E+02-0.260E+02 -0.120E+02-0.240E+02-0.600E+01 0.110E+02 0.180E+02 0.360E+02 0.110E+02 0.300E+02-0.100E+02-0.280E+02 -0.330E+02-0.200E+02-0.400E+01-0.110E+02 0.600E+01 0.110E+02 0.180E+02 0.200E+02 0.209E+02 0.250E+01 -0.357E+02 0.100E+00-0.171E+02-0.185E+02-0.260E+01 0.600E+00 0.222E+02 0.152E+02 0.640E+01 0.890E+01 0.230E+02-0.247E+02-0.161E+02-0.870E+01-0.155E+02-0.950E+01 0.264E+02 0.320E+02 0.363E+02-0.530E+01 -0.192E+02-0.151E+02-0.317E+02-0.420E+01-0.236E+02 0.128E+02 0.727E+02 0.619E+02-0.285E+02-0.411E+02 -0.167E+02-0.296E+02-0.157E+02-0.126E+02 0.139E+02 0.588E+02 0.491E+02-0.110E+01-0.128E+02-0.282E+02 -0.233E+02-0.660E+01-0.131E+02-0.590E+01-0.197E+02-0.530E+01-0.960E+01-0.230E+01 0.270E+01 0.770E+01 0.195E+02 0.110E+02-0.190E+01 0.440E+01-0.530E+01-0.141E+02-0.180E+02-0.200E+01-0.560E+01-0.250E+01 0.140E+01 0.360E+01 0.720E+01 0.170E+01 0.215E+02 0.104E+02-0.470E+01-0.110E+02-0.620E+01-0.830E+01 -0.900E+01-0.260E+01-0.220E+01 0.670E+01 0.810E+01 0.197E+02 0.133E+02 0.146E+02 0.280E+01 0.390E+01 -0.231E+02-0.203E+02-0.190E+02 0.470E+01 0.437E+02 0.646E+02 0.168E+02-0.351E+02-0.175E+02-0.211E+02 -0.279E+02-0.125E+02-0.135E+02 0.430E+01 0.251E+02 0.214E+02 0.370E+02 0.262E+02-0.284E+02-0.297E+02 -0.210E+01-0.104E+02-0.151E+02-0.184E+02-0.139E+02-0.240E+01 0.184E+02 0.321E+02 0.390E+02 0.200E+01 -0.186E+02-0.181E+02-0.151E+02 0.300E+01-0.165E+02-0.142E+02-0.900E+01 0.303E+02 0.364E+02 0.650E+02 -0.278E+02-0.960E+01-0.354E+02-0.215E+02-0.277E+02-0.570E+01 0.110E+01-0.900E+01 0.260E+01 0.263E+02 0.220E+02 0.540E+01 0.400E+01-0.200E+00-0.113E+02-0.268E+02-0.123E+02-0.630E+01-0.500E+00 0.800E+00 0.285E+02 0.374E+02 0.121E+02-0.710E+01-0.140E+02-0.222E+02-0.156E+02 0.500E+00-0.146E+02-0.260E+01 -0.680E+01 0.230E+01 0.194E+02 0.176E+02 0.215E+02-0.970E+01 0.820E+01-0.135E+02-0.460E+01-0.253E+02 -0.129E+02-0.210E+01-0.220E+01 0.820E+01 0.378E+02 0.970E+01 0.468E+02-0.233E+02-0.170E+02-0.260E+02 -0.115E+02-0.119E+02-0.840E+01 0.109E+02 0.276E+02 0.196E+02 0.510E+01 0.880E+01-0.129E+02-0.292E+02 -0.145E+02-0.101E+02-0.540E+01 0.300E+01 0.274E+02 0.436E+02 0.347E+02-0.480E+01-0.208E+02-0.210E+02 -0.203E+02-0.169E+02-0.143E+02-0.670E+01 0.236E+02 0.594E+02 0.590E+02-0.153E+02-0.160E+01-0.508E+02 -0.145E+02-0.379E+02-0.176E+02-0.950E+01 0.336E+02 0.104E+03 0.485E+02-0.540E+01-0.258E+02-0.467E+02 test of difmc ierr is 0 0.100E+01 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.000E+00 0.340E+39 0.340E+39 0.700E+01 0.130E+02 0.220E+02-0.290E+02-0.900E+01-0.100E+02-0.200E+01-0.500E+01 -0.300E+01 0.000E+00 0.200E+01 0.340E+39 0.340E+39 0.200E+02 0.160E+02-0.300E+01-0.210E+02-0.110E+02 -0.200E+01-0.400E+01 0.340E+39 0.340E+39 0.190E+02 0.380E+02 0.440E+02-0.190E+02-0.300E+02-0.260E+02 -0.120E+02 0.340E+39 0.340E+39 0.110E+02 0.180E+02 0.360E+02 0.110E+02 0.300E+02-0.100E+02-0.280E+02 -0.330E+02-0.200E+02-0.400E+01-0.110E+02 0.340E+39 0.340E+39 0.180E+02 0.200E+02 0.209E+02 0.250E+01 -0.357E+02 0.100E+00-0.171E+02-0.185E+02-0.260E+01 0.600E+00 0.222E+02 0.152E+02 0.640E+01 0.890E+01 0.230E+02-0.247E+02-0.161E+02-0.870E+01-0.155E+02-0.950E+01 0.264E+02 0.320E+02 0.363E+02-0.530E+01 -0.192E+02-0.151E+02-0.317E+02-0.420E+01-0.236E+02 0.128E+02 0.727E+02 0.619E+02-0.285E+02-0.411E+02 -0.167E+02-0.296E+02-0.157E+02-0.126E+02 0.139E+02 0.588E+02 0.491E+02-0.110E+01-0.128E+02-0.282E+02 -0.233E+02-0.660E+01-0.131E+02-0.590E+01-0.197E+02-0.530E+01-0.960E+01-0.230E+01 0.270E+01 0.770E+01 0.195E+02 0.110E+02-0.190E+01 0.440E+01-0.530E+01-0.141E+02-0.180E+02-0.200E+01-0.560E+01-0.250E+01 0.140E+01 0.360E+01 0.720E+01 0.170E+01 0.215E+02 0.104E+02-0.470E+01-0.110E+02-0.620E+01-0.830E+01 -0.900E+01-0.260E+01-0.220E+01 0.670E+01 0.810E+01 0.197E+02 0.133E+02 0.146E+02 0.280E+01 0.390E+01 -0.231E+02-0.203E+02-0.190E+02 0.470E+01 0.437E+02 0.646E+02 0.168E+02-0.351E+02-0.175E+02-0.211E+02 -0.279E+02-0.125E+02-0.135E+02 0.430E+01 0.251E+02 0.214E+02 0.370E+02 0.262E+02-0.284E+02-0.297E+02 -0.210E+01-0.104E+02-0.151E+02-0.184E+02-0.139E+02-0.240E+01 0.184E+02 0.321E+02 0.390E+02 0.200E+01 -0.186E+02-0.181E+02-0.151E+02 0.300E+01-0.165E+02-0.142E+02-0.900E+01 0.303E+02 0.364E+02 0.650E+02 -0.278E+02-0.960E+01-0.354E+02-0.215E+02-0.277E+02-0.570E+01 0.110E+01-0.900E+01 0.260E+01 0.263E+02 0.220E+02 0.540E+01 0.400E+01-0.200E+00-0.113E+02-0.268E+02-0.123E+02-0.630E+01-0.500E+00 0.800E+00 0.285E+02 0.374E+02 0.121E+02-0.710E+01-0.140E+02-0.222E+02-0.156E+02 0.500E+00-0.146E+02-0.260E+01 -0.680E+01 0.230E+01 0.194E+02 0.176E+02 0.215E+02-0.970E+01 0.820E+01-0.135E+02-0.460E+01-0.253E+02 -0.129E+02-0.210E+01-0.220E+01 0.820E+01 0.378E+02 0.970E+01 0.468E+02-0.233E+02-0.170E+02-0.260E+02 -0.115E+02-0.119E+02-0.840E+01 0.109E+02 0.276E+02 0.196E+02 0.510E+01 0.880E+01-0.129E+02-0.292E+02 -0.145E+02-0.101E+02-0.540E+01 0.300E+01 0.274E+02 0.436E+02 0.347E+02-0.480E+01-0.208E+02-0.210E+02 -0.203E+02-0.169E+02-0.143E+02-0.670E+01 0.236E+02 0.594E+02 0.590E+02-0.153E+02-0.160E+01-0.508E+02 -0.145E+02-0.379E+02-0.176E+02-0.950E+01 0.336E+02 0.104E+03 0.485E+02-0.540E+01-0.258E+02-0.467E+02 0.340E+39 test of gfslfs starpac 2.08s (03/15/90) gain function of 41 term symmetric linear filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.9999998E+00 - +++++++++++++ - i +++++ i i +++ i 0.5000000E+00 - +++ - i ++ i i + i i ++ i i + i i + i i + i i + i 0.1000000E+00 - + - i i i + i 0.5000000E-01 - + - i i i + i i i i i i + i i i 0.1000000E-01 - ++ - i + ++ i i i i + + + i 0.5000000E-02 - + - i +++++ i i + i i + i i + + ++++ i i + + i i + + + +++ i 0.1000000E-02 - + + + ++ - i + + +++ i i + + ++ i 0.5000001E-03 - + + - i + + + + + + i i + + i i + + i i + + i i + i i i i i 0.1000000E-03 - + - i + + i i i 0.5000001E-04 - - i i i i i i 0.2175570E-04 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0200 0.0400 0.0600 0.0800 0.1000 0.1200 0.1400 0.1600 0.1800 0.2000 ierr is 0 0.100E+01 0.100E+01 0.999E+00 0.997E+00 0.994E+00 0.989E+00 0.984E+00 0.976E+00 0.967E+00 0.955E+00 0.940E+00 0.923E+00 0.902E+00 0.878E+00 0.851E+00 0.820E+00 0.786E+00 0.749E+00 0.709E+00 0.666E+00 0.622E+00 0.576E+00 0.528E+00 0.481E+00 0.433E+00 0.386E+00 0.340E+00 0.296E+00 0.253E+00 0.214E+00 0.177E+00 0.144E+00 0.114E+00 0.875E-01 0.646E-01 0.451E-01 0.291E-01 0.162E-01 0.618E-02 0.116E-02 0.618E-02 0.922E-02 0.106E-01 0.107E-01 0.984E-02 0.830E-02 0.635E-02 0.424E-02 0.215E-02 0.231E-03 0.140E-02 0.266E-02 0.354E-02 0.402E-02 0.414E-02 0.392E-02 0.345E-02 0.277E-02 0.198E-02 0.113E-02 0.300E-03 0.460E-03 0.110E-02 0.160E-02 0.193E-02 0.209E-02 0.209E-02 0.193E-02 0.166E-02 0.129E-02 0.865E-03 0.417E-03 0.218E-04 0.422E-03 0.759E-03 0.102E-02 0.118E-02 0.125E-02 0.123E-02 0.112E-02 0.948E-03 0.720E-03 0.459E-03 0.185E-03 0.832E-04 0.327E-03 0.532E-03 0.687E-03 0.784E-03 0.822E-03 0.801E-03 0.727E-03 0.607E-03 0.454E-03 0.279E-03 0.952E-04 0.842E-04 0.247E-03 0.385E-03 0.488E-03 0.553E-03 0.000E+00 0.200E-02 0.400E-02 0.600E-02 0.800E-02 0.100E-01 0.120E-01 0.140E-01 0.160E-01 0.180E-01 0.200E-01 0.220E-01 0.240E-01 0.260E-01 0.280E-01 0.300E-01 0.320E-01 0.340E-01 0.360E-01 0.380E-01 0.400E-01 0.420E-01 0.440E-01 0.460E-01 0.480E-01 0.500E-01 0.520E-01 0.540E-01 0.560E-01 0.580E-01 0.600E-01 0.620E-01 0.640E-01 0.660E-01 0.680E-01 0.700E-01 0.720E-01 0.740E-01 0.760E-01 0.780E-01 0.800E-01 0.820E-01 0.840E-01 0.860E-01 0.880E-01 0.900E-01 0.920E-01 0.940E-01 0.960E-01 0.980E-01 0.100E+00 0.102E+00 0.104E+00 0.106E+00 0.108E+00 0.110E+00 0.112E+00 0.114E+00 0.116E+00 0.118E+00 0.120E+00 0.122E+00 0.124E+00 0.126E+00 0.128E+00 0.130E+00 0.132E+00 0.134E+00 0.136E+00 0.138E+00 0.140E+00 0.142E+00 0.144E+00 0.146E+00 0.148E+00 0.150E+00 0.152E+00 0.154E+00 0.156E+00 0.158E+00 0.160E+00 0.162E+00 0.164E+00 0.166E+00 0.168E+00 0.170E+00 0.172E+00 0.174E+00 0.176E+00 0.178E+00 0.180E+00 0.182E+00 0.184E+00 0.186E+00 0.188E+00 0.190E+00 0.192E+00 0.194E+00 0.196E+00 0.198E+00 0.200E+00 test of gfarfs starpac 2.08s (03/15/90) gain function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.1756 - ++++++ - i +++++++++ i 1.0000 - ++++++++ - i +++++++ i 0.8000 - +++++++ - i +++++ i i ++++++ i 0.6000 - ++++ - i +++++ i i ++++ i i +++ i i +++ i 0.4000 - +++ - i +++ i i ++ i i +++ i i ++ i i + i i ++ i i ++ i 0.2000 - + - i + i i + i i + i i + i i + i i + i 0.1000 - + - i i i + i 0.0800 - + - i i i + i 0.0600 - - i i i + i i i 0.0400 - - i + i i i i i i i i + i i i i i 0.0200 - - i i i i i i i i 0.0126 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0200 0.0400 0.0600 0.0800 0.1000 0.1200 0.1400 0.1600 0.1800 0.2000 1 starpac 2.08s (03/15/90) phase function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.1416 - - i i i i i i i i 2.5133 - - i i i i i i i i 1.8850 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.6283 - - i i i i i i i i 0.0000 - - i i i i i i i i -0.6283 - - i i i i i ++++++++++++++++++++ i i +++++++++++++++++++ i -1.2566 - +++++++++++++++++++++ - i ++++++++++++++++++++ i i +++++++++++++++++++ i i + i i i -1.8850 - - i i i i i i i i -2.5133 - - i i i i i i i i -3.1416 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0200 0.0400 0.0600 0.0800 0.1000 0.1200 0.1400 0.1600 0.1800 0.2000 ierr is 0 0.000E+00 0.126E-01 0.251E-01 0.377E-01 0.503E-01 0.628E-01 0.754E-01 0.879E-01 0.100E+00 0.113E+00 0.126E+00 0.138E+00 0.151E+00 0.163E+00 0.176E+00 0.188E+00 0.201E+00 0.213E+00 0.226E+00 0.238E+00 0.251E+00 0.263E+00 0.276E+00 0.288E+00 0.300E+00 0.313E+00 0.325E+00 0.338E+00 0.350E+00 0.362E+00 0.375E+00 0.387E+00 0.399E+00 0.412E+00 0.424E+00 0.436E+00 0.449E+00 0.461E+00 0.473E+00 0.485E+00 0.497E+00 0.510E+00 0.522E+00 0.534E+00 0.546E+00 0.558E+00 0.570E+00 0.582E+00 0.594E+00 0.606E+00 0.618E+00 0.630E+00 0.642E+00 0.654E+00 0.666E+00 0.677E+00 0.689E+00 0.701E+00 0.713E+00 0.725E+00 0.736E+00 0.748E+00 0.760E+00 0.771E+00 0.783E+00 0.794E+00 0.806E+00 0.817E+00 0.829E+00 0.840E+00 0.852E+00 0.863E+00 0.874E+00 0.886E+00 0.897E+00 0.908E+00 0.919E+00 0.930E+00 0.941E+00 0.952E+00 0.964E+00 0.975E+00 0.985E+00 0.996E+00 0.101E+01 0.102E+01 0.103E+01 0.104E+01 0.105E+01 0.106E+01 0.107E+01 0.108E+01 0.109E+01 0.110E+01 0.111E+01 0.112E+01 0.113E+01 0.114E+01 0.116E+01 0.117E+01 0.118E+01 -0.157E+01-0.156E+01-0.156E+01-0.155E+01-0.155E+01-0.154E+01-0.153E+01-0.153E+01-0.152E+01-0.151E+01 -0.151E+01-0.150E+01-0.150E+01-0.149E+01-0.148E+01-0.148E+01-0.147E+01-0.146E+01-0.146E+01-0.145E+01 -0.145E+01-0.144E+01-0.143E+01-0.143E+01-0.142E+01-0.141E+01-0.141E+01-0.140E+01-0.139E+01-0.139E+01 -0.138E+01-0.138E+01-0.137E+01-0.136E+01-0.136E+01-0.135E+01-0.134E+01-0.134E+01-0.133E+01-0.133E+01 -0.132E+01-0.131E+01-0.131E+01-0.130E+01-0.129E+01-0.129E+01-0.128E+01-0.128E+01-0.127E+01-0.126E+01 -0.126E+01-0.125E+01-0.124E+01-0.124E+01-0.123E+01-0.123E+01-0.122E+01-0.121E+01-0.121E+01-0.120E+01 -0.119E+01-0.119E+01-0.118E+01-0.117E+01-0.117E+01-0.116E+01-0.116E+01-0.115E+01-0.114E+01-0.114E+01 -0.113E+01-0.112E+01-0.112E+01-0.111E+01-0.111E+01-0.110E+01-0.109E+01-0.109E+01-0.108E+01-0.107E+01 -0.107E+01-0.106E+01-0.106E+01-0.105E+01-0.104E+01-0.104E+01-0.103E+01-0.102E+01-0.102E+01-0.101E+01 -0.101E+01-0.999E+00-0.993E+00-0.986E+00-0.980E+00-0.974E+00-0.968E+00-0.961E+00-0.955E+00-0.949E+00 -0.942E+00 0.000E+00 0.200E-02 0.400E-02 0.600E-02 0.800E-02 0.100E-01 0.120E-01 0.140E-01 0.160E-01 0.180E-01 0.200E-01 0.220E-01 0.240E-01 0.260E-01 0.280E-01 0.300E-01 0.320E-01 0.340E-01 0.360E-01 0.380E-01 0.400E-01 0.420E-01 0.440E-01 0.460E-01 0.480E-01 0.500E-01 0.520E-01 0.540E-01 0.560E-01 0.580E-01 0.600E-01 0.620E-01 0.640E-01 0.660E-01 0.680E-01 0.700E-01 0.720E-01 0.740E-01 0.760E-01 0.780E-01 0.800E-01 0.820E-01 0.840E-01 0.860E-01 0.880E-01 0.900E-01 0.920E-01 0.940E-01 0.960E-01 0.980E-01 0.100E+00 0.102E+00 0.104E+00 0.106E+00 0.108E+00 0.110E+00 0.112E+00 0.114E+00 0.116E+00 0.118E+00 0.120E+00 0.122E+00 0.124E+00 0.126E+00 0.128E+00 0.130E+00 0.132E+00 0.134E+00 0.136E+00 0.138E+00 0.140E+00 0.142E+00 0.144E+00 0.146E+00 0.148E+00 0.150E+00 0.152E+00 0.154E+00 0.156E+00 0.158E+00 0.160E+00 0.162E+00 0.164E+00 0.166E+00 0.168E+00 0.170E+00 0.172E+00 0.174E+00 0.176E+00 0.178E+00 0.180E+00 0.182E+00 0.184E+00 0.186E+00 0.188E+00 0.190E+00 0.192E+00 0.194E+00 0.196E+00 0.198E+00 0.200E+00 test of gfslfs starpac 2.08s (03/15/90) gain function of 41 term symmetric linear filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.9999998E+00 - ++++++ - i ++ i i + i 0.5000000E+00 - + - i + i i + i i i i + i i i 0.1000000E+00 - + - i i 0.5000000E-01 - - i + i i i i i i i i i 0.1000000E-01 - + + - i + i i + i 0.5000000E-02 - - i ++ i i + i i + + i i + i i + + + i 0.1000000E-02 - + + - i + ++ i i + ++ i 0.5000001E-03 - + - i ++ + + + + i i + + + + + i i + + + + + i i + ++ i i + + ++ i 0.1000000E-03 - + + ++ ++ - i + + + + + ++ i 0.5000001E-04 - + + + + + + ++ ++ + - i + + + + + i i + + i i + i i + i i + + i 0.1000000E-04 - - i + i i + + i 0.5000001E-05 - - i + i i i i + i 0.1788139E-05 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.100E+01 0.998E+00 0.989E+00 0.972E+00 0.940E+00 0.890E+00 0.820E+00 0.729E+00 0.622E+00 0.505E+00 0.386E+00 0.274E+00 0.177E+00 0.100E+00 0.451E-01 0.108E-01 0.618E-02 0.108E-01 0.830E-02 0.318E-02 0.140E-02 0.383E-02 0.392E-02 0.239E-02 0.300E-03 0.137E-02 0.209E-02 0.181E-02 0.865E-03 0.228E-03 0.102E-02 0.125E-02 0.948E-03 0.322E-03 0.327E-03 0.743E-03 0.801E-03 0.534E-03 0.953E-04 0.320E-03 0.553E-03 0.537E-03 0.312E-03 0.728E-05 0.285E-03 0.419E-03 0.372E-03 0.185E-03 0.539E-04 0.246E-03 0.322E-03 0.264E-03 0.109E-03 0.734E-04 0.209E-03 0.251E-03 0.191E-03 0.622E-04 0.788E-04 0.177E-03 0.197E-03 0.140E-03 0.335E-04 0.770E-04 0.149E-03 0.157E-03 0.105E-03 0.163E-04 0.711E-04 0.124E-03 0.126E-03 0.798E-04 0.671E-05 0.633E-04 0.104E-03 0.103E-03 0.629E-04 0.246E-05 0.545E-04 0.871E-04 0.851E-04 0.518E-04 0.179E-05 0.455E-04 0.731E-04 0.723E-04 0.455E-04 0.392E-05 0.366E-04 0.618E-04 0.636E-04 0.429E-04 0.800E-05 0.282E-04 0.531E-04 0.586E-04 0.435E-04 0.136E-04 0.205E-04 0.470E-04 0.569E-04 0.000E+00 0.500E-02 0.100E-01 0.150E-01 0.200E-01 0.250E-01 0.300E-01 0.350E-01 0.400E-01 0.450E-01 0.500E-01 0.550E-01 0.600E-01 0.650E-01 0.700E-01 0.750E-01 0.800E-01 0.850E-01 0.900E-01 0.950E-01 0.100E+00 0.105E+00 0.110E+00 0.115E+00 0.120E+00 0.125E+00 0.130E+00 0.135E+00 0.140E+00 0.145E+00 0.150E+00 0.155E+00 0.160E+00 0.165E+00 0.170E+00 0.175E+00 0.180E+00 0.185E+00 0.190E+00 0.195E+00 0.200E+00 0.205E+00 0.210E+00 0.215E+00 0.220E+00 0.225E+00 0.230E+00 0.235E+00 0.240E+00 0.245E+00 0.250E+00 0.255E+00 0.260E+00 0.265E+00 0.270E+00 0.275E+00 0.280E+00 0.285E+00 0.290E+00 0.295E+00 0.300E+00 0.305E+00 0.310E+00 0.315E+00 0.320E+00 0.325E+00 0.330E+00 0.335E+00 0.340E+00 0.345E+00 0.350E+00 0.355E+00 0.360E+00 0.365E+00 0.370E+00 0.375E+00 0.380E+00 0.385E+00 0.390E+00 0.395E+00 0.400E+00 0.405E+00 0.410E+00 0.415E+00 0.420E+00 0.425E+00 0.430E+00 0.435E+00 0.440E+00 0.445E+00 0.450E+00 0.455E+00 0.460E+00 0.465E+00 0.470E+00 0.475E+00 0.480E+00 0.485E+00 0.490E+00 0.495E+00 0.500E+00 test of gfarfs starpac 2.08s (03/15/90) gain function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++ - i +++++++++++++ i i ++++++++ i i +++++++ i i +++++ i -1.8039 - +++++ - i ++++ i i +++ i i ++++ i i ++ i -3.6078 - +++ - i ++ i i ++ i i ++ i i ++ i -5.4117 - ++ - i + i i + i i ++ i i + i -7.2156 - + - i + i i + i i i i + i -9.0195 - + - i i i + i i + i i i -10.8234 - - i + i i i i + i i i -12.6273 - - i i i + i i i i i -14.4312 - - i i i + i i i i i -16.2351 - - i i i i i i i i -18.0390 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) phase function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.1416 - - i i i i i i i i 2.5133 - - i i i i i i i i 1.8850 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.6283 - - i i i i i i i i 0.0000 - +++ - i ++++++++ i i ++++++++ i i ++++++++ i i ++++++++ i -0.6283 - ++++++++ - i ++++++++ i i +++++++++ i i ++++++++ i i ++++++++ i -1.2566 - ++++++++ - i ++++++++ i i +++++++ i i + i i i -1.8850 - - i i i i i i i i -2.5133 - - i i i i i i i i -3.1416 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.000E+00 0.314E-01 0.628E-01 0.942E-01 0.126E+00 0.157E+00 0.188E+00 0.219E+00 0.251E+00 0.282E+00 0.313E+00 0.344E+00 0.375E+00 0.406E+00 0.436E+00 0.467E+00 0.497E+00 0.528E+00 0.558E+00 0.588E+00 0.618E+00 0.648E+00 0.677E+00 0.707E+00 0.736E+00 0.765E+00 0.794E+00 0.823E+00 0.852E+00 0.880E+00 0.908E+00 0.936E+00 0.964E+00 0.991E+00 0.102E+01 0.104E+01 0.107E+01 0.110E+01 0.112E+01 0.115E+01 0.118E+01 0.120E+01 0.123E+01 0.125E+01 0.127E+01 0.130E+01 0.132E+01 0.135E+01 0.137E+01 0.139E+01 0.141E+01 0.144E+01 0.146E+01 0.148E+01 0.150E+01 0.152E+01 0.154E+01 0.156E+01 0.158E+01 0.160E+01 0.162E+01 0.164E+01 0.165E+01 0.167E+01 0.169E+01 0.171E+01 0.172E+01 0.174E+01 0.175E+01 0.177E+01 0.178E+01 0.180E+01 0.181E+01 0.182E+01 0.184E+01 0.185E+01 0.186E+01 0.187E+01 0.188E+01 0.189E+01 0.190E+01 0.191E+01 0.192E+01 0.193E+01 0.194E+01 0.194E+01 0.195E+01 0.196E+01 0.196E+01 0.197E+01 0.198E+01 0.198E+01 0.198E+01 0.199E+01 0.199E+01 0.199E+01 0.200E+01 0.200E+01 0.200E+01 0.200E+01 0.200E+01 -0.157E+01-0.156E+01-0.154E+01-0.152E+01-0.151E+01-0.149E+01-0.148E+01-0.146E+01-0.145E+01-0.143E+01 -0.141E+01-0.140E+01-0.138E+01-0.137E+01-0.135E+01-0.134E+01-0.132E+01-0.130E+01-0.129E+01-0.127E+01 -0.126E+01-0.124E+01-0.123E+01-0.121E+01-0.119E+01-0.118E+01-0.116E+01-0.115E+01-0.113E+01-0.112E+01 -0.110E+01-0.108E+01-0.107E+01-0.105E+01-0.104E+01-0.102E+01-0.101E+01-0.990E+00-0.974E+00-0.958E+00 -0.942E+00-0.927E+00-0.911E+00-0.895E+00-0.880E+00-0.864E+00-0.848E+00-0.833E+00-0.817E+00-0.801E+00 -0.785E+00-0.770E+00-0.754E+00-0.738E+00-0.723E+00-0.707E+00-0.691E+00-0.675E+00-0.660E+00-0.644E+00 -0.628E+00-0.613E+00-0.597E+00-0.581E+00-0.565E+00-0.550E+00-0.534E+00-0.518E+00-0.503E+00-0.487E+00 -0.471E+00-0.456E+00-0.440E+00-0.424E+00-0.408E+00-0.393E+00-0.377E+00-0.361E+00-0.346E+00-0.330E+00 -0.314E+00-0.298E+00-0.283E+00-0.267E+00-0.251E+00-0.236E+00-0.220E+00-0.204E+00-0.188E+00-0.173E+00 -0.157E+00-0.141E+00-0.126E+00-0.110E+00-0.942E-01-0.785E-01-0.628E-01-0.471E-01-0.314E-01-0.157E-01 -0.157E+01 0.000E+00 0.500E-02 0.100E-01 0.150E-01 0.200E-01 0.250E-01 0.300E-01 0.350E-01 0.400E-01 0.450E-01 0.500E-01 0.550E-01 0.600E-01 0.650E-01 0.700E-01 0.750E-01 0.800E-01 0.850E-01 0.900E-01 0.950E-01 0.100E+00 0.105E+00 0.110E+00 0.115E+00 0.120E+00 0.125E+00 0.130E+00 0.135E+00 0.140E+00 0.145E+00 0.150E+00 0.155E+00 0.160E+00 0.165E+00 0.170E+00 0.175E+00 0.180E+00 0.185E+00 0.190E+00 0.195E+00 0.200E+00 0.205E+00 0.210E+00 0.215E+00 0.220E+00 0.225E+00 0.230E+00 0.235E+00 0.240E+00 0.245E+00 0.250E+00 0.255E+00 0.260E+00 0.265E+00 0.270E+00 0.275E+00 0.280E+00 0.285E+00 0.290E+00 0.295E+00 0.300E+00 0.305E+00 0.310E+00 0.315E+00 0.320E+00 0.325E+00 0.330E+00 0.335E+00 0.340E+00 0.345E+00 0.350E+00 0.355E+00 0.360E+00 0.365E+00 0.370E+00 0.375E+00 0.380E+00 0.385E+00 0.390E+00 0.395E+00 0.400E+00 0.405E+00 0.410E+00 0.415E+00 0.420E+00 0.425E+00 0.430E+00 0.435E+00 0.440E+00 0.445E+00 0.450E+00 0.455E+00 0.460E+00 0.465E+00 0.470E+00 0.475E+00 0.480E+00 0.485E+00 0.490E+00 0.495E+00 0.500E+00 test of hpcoef ierr is 0 0.100E+01 test of maflt ierr is 0 0.500E+01 0.110E+02 0.160E+02 test of slflt ierr is 0 -0.106E-02-0.232E-02-0.338E-02 test of sample ierr is 0 -0.106E-02-0.232E-02-0.338E-02 test of arflt ierr is 0 0.600E+01 0.500E+01 test of dif ierr is 0 0.600E+01 0.500E+01 test of difm ierr is 0 0.340E+39 0.340E+39 0.340E+39 test of gfslf starpac 2.08s (03/15/90) gain function of 1 term symmetric linear filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.5000 - - i i i i i i i i 0.4000 - - i i i i i i i i 0.3000 - - i i i i i i i i 0.2000 - - i i i i i i i i 0.1000 - - i i i i i i i i 0.0000 - +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ - i i i i i i i i -0.1000 - - i i i i i i i i -0.2000 - - i i i i i i i i -0.3000 - - i i i i i i i i -0.4000 - - i i i i i i i i -0.5000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of gfarf starpac 2.08s (03/15/90) gain function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++ - i +++++++++++++ i i ++++++++ i i +++++++ i i +++++ i -1.8039 - +++++ - i ++++ i i +++ i i ++++ i i ++ i -3.6078 - +++ - i ++ i i ++ i i ++ i i ++ i -5.4117 - ++ - i + i i + i i ++ i i + i -7.2156 - + - i + i i + i i i i + i -9.0195 - + - i i i + i i + i i i -10.8234 - - i + i i i i + i i i -12.6273 - - i i i + i i i i i -14.4312 - - i i i + i i i i i -16.2351 - - i i i i i i i i -18.0390 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) phase function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.1416 - - i i i i i i i i 2.5133 - - i i i i i i i i 1.8850 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.6283 - - i i i i i i i i 0.0000 - +++ - i ++++++++ i i ++++++++ i i ++++++++ i i ++++++++ i -0.6283 - ++++++++ - i ++++++++ i i +++++++++ i i ++++++++ i i ++++++++ i -1.2566 - ++++++++ - i ++++++++ i i +++++++ i i + i i i -1.8850 - - i i i i i i i i -2.5133 - - i i i i i i i i -3.1416 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of difc ierr is 0 0.100E+01 0.600E+01 0.500E+01 test of difmc ierr is 0 0.100E+01 0.340E+39 0.340E+39 0.340E+39 test of gfslfs starpac 2.08s (03/15/90) gain function of 1 term symmetric linear filter the plot has been supressed because fewer than two non zero gain function values were computed. ierr is 0 0.211E-03 0.000E+00 test of gfarfs starpac 2.08s (03/15/90) gain function of 1 term autoregressive, or difference, filter the plot has been supressed because fewer than two non zero gain function values were computed. ierr is 0 0.000E+00 -0.157E+01 0.000E+00 test of lpcoef starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine lpcoef ------------------------------------- the input value of fc is 1.0000000 . the value of the argument fc must be between .00000000 and .50000000 , inclusive. the value of the variable k must be odd. the input value of k is -1. the correct form of the call statement is call lpcoef (fc, k, hlp) ierr is 1 test of lopass starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine lopass ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the input value of fc is 1.0000000 . the value of the argument fc must be between .00000000 and .50000000 , inclusive. the value of the variable k must be odd. the input value of k is -1. the correct form of the call statement is call lopass (y, n, fc, k, hlp, yf, nyf) ierr is 1 test of hipass starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine hipass ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the input value of fc is 1.0000000 . the value of the argument fc must be between .00000000 and .50000000 , inclusive. the value of the variable k must be odd. the input value of k is -1. the correct form of the call statement is call hipass (y, n, fc, k, hhp, yf, nyf) ierr is 1 test of hpcoef starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine hpcoef ------------------------------------- the input value of k is -1. the value of the argument k must be greater than or equal to 1. the value of the variable k must be odd. the input value of k is -1. the correct form of the call statement is call hpcoef (hlp, k, hhp) ierr is 1 test of maflt starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine maflt ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the value of the variable k must be odd. the input value of k is -1. the correct form of the call statement is call maflt (y, n, k, yf, nyf) ierr is 1 test of slflt starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine slflt ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the value of the variable k must be odd. the input value of k is -1. the correct form of the call statement is call slflt (y, n, k, h, yf, nyf) ierr is 1 test of sample starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine sample ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the correct form of the call statement is call sample (y, n, ns, ys, nys) ierr is 1 test of arflt starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine arflt ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the correct form of the call statement is call arflt (y, n, iar, phi, yf, nyf) ierr is 1 test of dif starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine dif ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the correct form of the call statement is call dif (y, n, yf, nyf) ierr is 1 test of difm starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine difm ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the correct form of the call statement is call difm (y, ymiss, n, yf, yfmiss, nyf) ierr is 1 test of gfslf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine gfslf ------------------------------------- the input value of k is -1. the value of the argument k must be greater than or equal to 1. the value of the variable k must be odd. the input value of k is -1. the correct form of the call statement is call gfslf (h, k) ierr is 1 test of gfarf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine gfarf ------------------------------------- the input value of iar is 0. the value of the argument iar must be greater than or equal to 1. the correct form of the call statement is call gfarf (phi, iar) ierr is 1 test of difc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine difc ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the order of each difference factor (iod) and number of times it is applied (nd) must be greater than equal to one. the input values of these arrays are dif. fact. nd iod 1 -1 -1 the correct form of the call statement is call difc (y, n, + nfac, nd, iod, iar, phi, lphi, + yf, nyf, ldstak) ierr is 1 test of difmc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine difmc ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 3. the order of each difference factor (iod) and number of times it is applied (nd) must be greater than equal to one. the input values of these arrays are dif. fact. nd iod 1 -1 -1 the correct form of the call statement is call difmc (y, ymiss, n, + nfac, nd, iod, iar, phi, lphi, + yf, yfmiss, nyf, ldstak) ierr is 1 test of gfslfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine gfslfs ------------------------------------- the input value of k is -1. the value of the argument k must be greater than or equal to 1. the value of the variable k must be odd. the input value of k is -1. the input value of nf is 0. the value of the argument nf must be greater than or equal to 1. the correct form of the call statement is call gfslfs (h, k, nf, fmin, fmax, gain, freq, nprt, ldstak) ierr is 1 test of gfarfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine gfarfs ------------------------------------- the input value of nf is 0. the value of the argument nf must be greater than or equal to 1. the correct form of the call statement is call gfarfs (phi, iar, + nf, fmin, fmax, gain, phas, freq, nprt, ldstak) ierr is 1 test of difc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine difc ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 8. the correct form of the call statement is call difc (y, n, + nfac, nd, iod, iar, phi, lphi, + yf, nyf, ldstak) ierr is 1 test of difmc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine difmc ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 8. the correct form of the call statement is call difmc (y, ymiss, n, + nfac, nd, iod, iar, phi, lphi, + yf, yfmiss, nyf, ldstak) ierr is 1 test of gfslfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine gfslfs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 161. the correct form of the call statement is call gfslfs (h, k, nf, fmin, fmax, gain, freq, nprt, ldstak) ierr is 1 test of gfarfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine gfarfs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 161. the correct form of the call statement is call gfarfs (phi, iar, + nf, fmin, fmax, gain, phas, freq, nprt, ldstak) ierr is 1 test runs for the histogram family of routines. test 1. generate one of each of the possible error messages. try zero or fewer elements. starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine hist ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call hist (y, n, ldstak) print the data to insure the original order has been restored. 0.60670 0.60870 0.60860 0.61340 0.61080 0.61380 0.61250 0.61220 0.61100 0.61040 0.72130 0.70780 0.70210 0.70040 0.69810 0.72420 0.72680 0.74180 0.74070 0.71990 0.62250 0.62540 0.62520 0.62670 0.62180 0.61780 0.62160 0.61920 0.61910 0.62500 0.61880 0.62330 0.62250 0.62040 0.62070 0.61680 0.61410 0.62910 0.62310 0.62220 0.62520 0.63080 0.63760 0.63300 0.63030 0.63010 0.63900 0.64230 0.63000 0.62600 0.62920 0.62980 0.62900 0.62620 0.59520 0.59510 0.63140 0.64400 0.64390 0.63260 0.63920 0.64170 0.64120 0.65300 0.64110 0.63550 0.63440 0.66230 0.62760 0.63070 0.63540 0.61970 0.61530 0.63400 0.63380 0.62840 0.61620 0.62520 0.63490 0.63440 0.63610 0.63730 0.63370 0.63830 the value of ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine histc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call histc (y, n, ncell, ylb, yub, ldstak) print the data to insure the original order has been restored. 0.60670 0.60870 0.60860 0.61340 0.61080 0.61380 0.61250 0.61220 0.61100 0.61040 0.72130 0.70780 0.70210 0.70040 0.69810 0.72420 0.72680 0.74180 0.74070 0.71990 0.62250 0.62540 0.62520 0.62670 0.62180 0.61780 0.62160 0.61920 0.61910 0.62500 0.61880 0.62330 0.62250 0.62040 0.62070 0.61680 0.61410 0.62910 0.62310 0.62220 0.62520 0.63080 0.63760 0.63300 0.63030 0.63010 0.63900 0.64230 0.63000 0.62600 0.62920 0.62980 0.62900 0.62620 0.59520 0.59510 0.63140 0.64400 0.64390 0.63260 0.63920 0.64170 0.64120 0.65300 0.64110 0.63550 0.63440 0.66230 0.62760 0.63070 0.63540 0.61970 0.61530 0.63400 0.63380 0.62840 0.61620 0.62520 0.63490 0.63440 0.63610 0.63730 0.63370 0.63830 the value of ierr is 1 1test with insufficient work area starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine hist ------------------------------------- the input value of ldstak is 54. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 55. the correct form of the call statement is call hist (y, n, ldstak) the value of ierr is 1 test with exactly the right amount of work area. starpac 2.08s (03/15/90) histogram number of observations = 84 minimum observation = 5.95099986E-01 maximum observation = 7.41800010E-01 histogram lower bound = 5.95099986E-01 histogram upper bound = 7.41800010E-01 number of cells = 9 observations used = 84 25 pct trimmed mean = 6.28859460E-01 min. observation used = 5.95099986E-01 standard deviation = 3.24052125E-02 max. observation used = 7.41800010E-01 mean dev./std. dev. = 6.50403082E-01 mean value = 6.37340546E-01 sqrt(beta one) = 1.93101048E+00 median value = 6.29150033E-01 beta two = 5.92837667E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 6.032500E-01 0.095 1.000 0.095 8 ++++++++ 6.195500E-01 0.464 0.905 0.369 31 +++++++++++++++++++++++++++++++ 6.358501E-01 0.857 0.536 0.393 33 +++++++++++++++++++++++++++++++++ 6.521501E-01 0.869 0.143 0.012 1 + 6.684501E-01 0.881 0.131 0.012 1 + 6.847501E-01 0.881 0.119 0.000 0 7.010502E-01 0.929 0.119 0.048 4 ++++ 7.173502E-01 0.964 0.071 0.036 3 +++ 7.336502E-01 1.000 0.036 0.036 3 +++ the value of ierr is 0 1test with insufficient work area starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine histc ------------------------------------- the input value of ldstak is 54. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 55. the correct form of the call statement is call histc (y, n, ncell, ylb, yub, ldstak) the value of ierr is 1 test with exactly the right amount of work area. starpac 2.08s (03/15/90) histogram number of observations = 84 minimum observation = 6.06700003E-01 maximum observation = 6.29999995E-01 histogram lower bound = 6.00000024E-01 histogram upper bound = 6.29999995E-01 number of cells = 10 observations used = 43 25 pct trimmed mean = 6.20917439E-01 min. observation used = 6.06700003E-01 standard deviation = 6.63616182E-03 max. observation used = 6.29999995E-01 mean dev./std. dev. = 8.39997053E-01 mean value = 6.20260537E-01 sqrt(beta one) = 3.41047406E-01 median value = 6.21599972E-01 beta two = 2.00412726E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 6.015000E-01 0.000 1.000 0.000 0 6.045001E-01 0.000 1.000 0.000 0 6.075001E-01 0.070 1.000 0.070 3 +++ 6.105001E-01 0.140 0.930 0.070 3 +++ 6.135001E-01 0.256 0.860 0.116 5 +++++ 6.165001E-01 0.349 0.744 0.093 4 ++++ 6.195002E-01 0.488 0.651 0.140 6 ++++++ 6.225002E-01 0.651 0.512 0.163 7 +++++++ 6.255002E-01 0.837 0.349 0.186 8 ++++++++ 6.285002E-01 1.000 0.163 0.163 7 +++++++ the value of ierr is 0 1try constant y. (not an error) starpac 2.08s (03/15/90) histogram number of observations = 10 minimum observation = 1.00000000E+00 maximum observation = 1.00000000E+00 histogram lower bound = 1.00000000E+00 histogram upper bound = 1.00000000E+00 number of cells = 7 observations used = 10 25 pct trimmed mean = 1.00000000E+00 min. observation used = 1.00000000E+00 standard deviation = 0.00000000E+00 max. observation used = 1.00000000E+00 mean dev./std. dev. = 0.00000000E+00 mean value = 1.00000000E+00 sqrt(beta one) = 0.00000000E+00 median value = 1.00000000E+00 beta two = 0.00000000E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 1.000000E+00 1.000 1.000 1.000 10 ++++++++++ 1.000000E+00 1.000 0.000 0.000 0 1.000000E+00 1.000 0.000 0.000 0 1.000000E+00 1.000 0.000 0.000 0 1.000000E+00 1.000 0.000 0.000 0 1.000000E+00 1.000 0.000 0.000 0 1.000000E+00 1.000 0.000 0.000 0 the value of ierr is 0 1try constant y. (not an error) starpac 2.08s (03/15/90) histogram number of observations = 10 minimum observation = 1.00000000E+00 maximum observation = 1.00000000E+00 histogram lower bound = 6.00000024E-01 histogram upper bound = 6.29999995E-01 number of cells = 10 observations used = 10 25 pct trimmed mean = 1.00000000E+00 min. observation used = 1.00000000E+00 standard deviation = 0.00000000E+00 max. observation used = 1.00000000E+00 mean dev./std. dev. = 0.00000000E+00 mean value = 1.00000000E+00 sqrt(beta one) = 0.00000000E+00 median value = 1.00000000E+00 beta two = 0.00000000E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 6.015000E-01 0.000 1.000 0.000 0 6.045001E-01 0.000 1.000 0.000 0 6.075001E-01 0.000 1.000 0.000 0 6.105001E-01 0.000 1.000 0.000 0 6.135001E-01 0.000 1.000 0.000 0 6.165001E-01 0.000 1.000 0.000 0 6.195002E-01 0.000 1.000 0.000 0 6.225002E-01 0.000 1.000 0.000 0 6.255002E-01 0.000 1.000 0.000 0 6.285002E-01 1.000 1.000 1.000 10 ++++++++++ the value of ierr is 0 try no data within user supplied limits. starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine histc ------------------------------------- the number of values in the vector y in the range 4.0000000E+00 to 1.0000000E+01, inclusive, must equal or exceed 1. the number of values in this range is 0. the correct form of the call statement is call histc (y, n, ncell, ylb, yub, ldstak) the value of ierr is 1 test 4. make working runs of all routines to check the output. run hist on the davis-harrison pikes peak data. starpac 2.08s (03/15/90) histogram number of observations = 84 minimum observation = 5.95099986E-01 maximum observation = 7.41800010E-01 histogram lower bound = 5.95099986E-01 histogram upper bound = 7.41800010E-01 number of cells = 9 observations used = 84 25 pct trimmed mean = 6.28859460E-01 min. observation used = 5.95099986E-01 standard deviation = 3.24052125E-02 max. observation used = 7.41800010E-01 mean dev./std. dev. = 6.50403082E-01 mean value = 6.37340546E-01 sqrt(beta one) = 1.93101048E+00 median value = 6.29150033E-01 beta two = 5.92837667E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 6.032500E-01 0.095 1.000 0.095 8 ++++++++ 6.195500E-01 0.464 0.905 0.369 31 +++++++++++++++++++++++++++++++ 6.358501E-01 0.857 0.536 0.393 33 +++++++++++++++++++++++++++++++++ 6.521501E-01 0.869 0.143 0.012 1 + 6.684501E-01 0.881 0.131 0.012 1 + 6.847501E-01 0.881 0.119 0.000 0 7.010502E-01 0.929 0.119 0.048 4 ++++ 7.173502E-01 0.964 0.071 0.036 3 +++ 7.336502E-01 1.000 0.036 0.036 3 +++ print the data to insure the original order has been restored. 0.60670 0.60870 0.60860 0.61340 0.61080 0.61380 0.61250 0.61220 0.61100 0.61040 0.72130 0.70780 0.70210 0.70040 0.69810 0.72420 0.72680 0.74180 0.74070 0.71990 0.62250 0.62540 0.62520 0.62670 0.62180 0.61780 0.62160 0.61920 0.61910 0.62500 0.61880 0.62330 0.62250 0.62040 0.62070 0.61680 0.61410 0.62910 0.62310 0.62220 0.62520 0.63080 0.63760 0.63300 0.63030 0.63010 0.63900 0.64230 0.63000 0.62600 0.62920 0.62980 0.62900 0.62620 0.59520 0.59510 0.63140 0.64400 0.64390 0.63260 0.63920 0.64170 0.64120 0.65300 0.64110 0.63550 0.63440 0.66230 0.62760 0.63070 0.63540 0.61970 0.61530 0.63400 0.63380 0.62840 0.61620 0.62520 0.63490 0.63440 0.63610 0.63730 0.63370 0.63830 the value of ierr is 0 run histc on the davis-harrison pikes peak data. starpac 2.08s (03/15/90) histogram number of observations = 84 minimum observation = 6.06700003E-01 maximum observation = 6.29999995E-01 histogram lower bound = 6.00000024E-01 histogram upper bound = 6.29999995E-01 number of cells = 10 observations used = 43 25 pct trimmed mean = 6.20917439E-01 min. observation used = 6.06700003E-01 standard deviation = 6.63616182E-03 max. observation used = 6.29999995E-01 mean dev./std. dev. = 8.39997053E-01 mean value = 6.20260537E-01 sqrt(beta one) = 3.41047406E-01 median value = 6.21599972E-01 beta two = 2.00412726E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 6.015000E-01 0.000 1.000 0.000 0 6.045001E-01 0.000 1.000 0.000 0 6.075001E-01 0.070 1.000 0.070 3 +++ 6.105001E-01 0.140 0.930 0.070 3 +++ 6.135001E-01 0.256 0.860 0.116 5 +++++ 6.165001E-01 0.349 0.744 0.093 4 ++++ 6.195002E-01 0.488 0.651 0.140 6 ++++++ 6.225002E-01 0.651 0.512 0.163 7 +++++++ 6.255002E-01 0.837 0.349 0.186 8 ++++++++ 6.285002E-01 1.000 0.163 0.163 7 +++++++ print the data to insure the original order has been restored. 0.60670 0.60870 0.60860 0.61340 0.61080 0.61380 0.61250 0.61220 0.61100 0.61040 0.72130 0.70780 0.70210 0.70040 0.69810 0.72420 0.72680 0.74180 0.74070 0.71990 0.62250 0.62540 0.62520 0.62670 0.62180 0.61780 0.62160 0.61920 0.61910 0.62500 0.61880 0.62330 0.62250 0.62040 0.62070 0.61680 0.61410 0.62910 0.62310 0.62220 0.62520 0.63080 0.63760 0.63300 0.63030 0.63010 0.63900 0.64230 0.63000 0.62600 0.62920 0.62980 0.62900 0.62620 0.59520 0.59510 0.63140 0.64400 0.64390 0.63260 0.63920 0.64170 0.64120 0.65300 0.64110 0.63550 0.63440 0.66230 0.62760 0.63070 0.63540 0.61970 0.61530 0.63400 0.63380 0.62840 0.61620 0.62520 0.63490 0.63440 0.63610 0.63730 0.63370 0.63830 the value of ierr is 0 run hist on -1, 8*0, 1. starpac 2.08s (03/15/90) histogram number of observations = 10 minimum observation = -1.00000000E+00 maximum observation = 1.00000000E+00 histogram lower bound = -1.00000000E+00 histogram upper bound = 1.00000000E+00 number of cells = 7 observations used = 10 25 pct trimmed mean = 0.00000000E+00 min. observation used = -1.00000000E+00 standard deviation = 4.71404523E-01 max. observation used = 1.00000000E+00 mean dev./std. dev. = 4.24264073E-01 mean value = 0.00000000E+00 sqrt(beta one) = 0.00000000E+00 median value = 0.00000000E+00 beta two = 4.99999952E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ -8.571429E-01 0.100 1.000 0.100 1 + -5.714287E-01 0.100 0.900 0.000 0 -2.857144E-01 0.100 0.900 0.000 0 0.000000E+00 0.900 0.900 0.800 8 ++++++++ 2.857143E-01 0.900 0.100 0.000 0 5.714286E-01 0.900 0.100 0.000 0 8.571429E-01 1.000 0.100 0.100 1 + the value of ierr is 0 run hist on -1, 8*0, 1. starpac 2.08s (03/15/90) histogram number of observations = 10 minimum observation = -1.00000000E+00 maximum observation = 1.00000000E+00 histogram lower bound = -1.00000000E+00 histogram upper bound = 1.00000000E+00 number of cells = 7 observations used = 10 25 pct trimmed mean = 0.00000000E+00 min. observation used = -1.00000000E+00 standard deviation = 4.71404523E-01 max. observation used = 1.00000000E+00 mean dev./std. dev. = 4.24264073E-01 mean value = 0.00000000E+00 sqrt(beta one) = 0.00000000E+00 median value = 0.00000000E+00 beta two = 4.99999952E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ -8.571429E-01 0.100 1.000 0.100 1 + -5.714287E-01 0.100 0.900 0.000 0 -2.857144E-01 0.100 0.900 0.000 0 0.000000E+00 0.900 0.900 0.800 8 ++++++++ 2.857143E-01 0.900 0.100 0.000 0 5.714286E-01 0.900 0.100 0.000 0 8.571429E-01 1.000 0.100 0.100 1 + run hist on -1, 8*0, 1. starpac 2.08s (03/15/90) histogram number of observations = 10 minimum observation = -1.00000000E+00 maximum observation = 1.00000000E+00 histogram lower bound = -1.00000000E+00 histogram upper bound = 1.00000000E+00 number of cells = 1 observations used = 10 25 pct trimmed mean = 0.00000000E+00 min. observation used = -1.00000000E+00 standard deviation = 4.71404523E-01 max. observation used = 1.00000000E+00 mean dev./std. dev. = 4.24264073E-01 mean value = 0.00000000E+00 sqrt(beta one) = 0.00000000E+00 median value = 0.00000000E+00 beta two = 4.99999952E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 0.000000E+00 1.000 1.000 1.000 10 ++++++++++ run hist on -1, 8*0, 1. starpac 2.08s (03/15/90) histogram number of observations = 10 minimum observation = 0.00000000E+00 maximum observation = 0.00000000E+00 histogram lower bound = -5.00000000E-01 histogram upper bound = 5.00000000E-01 number of cells = 7 observations used = 8 25 pct trimmed mean = 0.00000000E+00 min. observation used = 0.00000000E+00 standard deviation = 0.00000000E+00 max. observation used = 0.00000000E+00 mean dev./std. dev. = 0.00000000E+00 mean value = 0.00000000E+00 sqrt(beta one) = 0.00000000E+00 median value = 0.00000000E+00 beta two = 0.00000000E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ -4.285715E-01 0.000 1.000 0.000 0 -2.857143E-01 0.000 1.000 0.000 0 -1.428572E-01 0.000 1.000 0.000 0 0.000000E+00 1.000 1.000 1.000 8 ++++++++ 1.428571E-01 1.000 0.000 0.000 0 2.857143E-01 1.000 0.000 0.000 0 4.285715E-01 1.000 0.000 0.000 0 run hist on -1, 8*0, 1. starpac 2.08s (03/15/90) histogram number of observations = 10 minimum observation = 1.00000000E+00 maximum observation = 1.00000000E+00 histogram lower bound = 1.00000000E+00 histogram upper bound = 4.00000000E+00 number of cells = 7 observations used = 1 25 pct trimmed mean = 1.00000000E+00 min. observation used = 1.00000000E+00 standard deviation = 0.00000000E+00 max. observation used = 1.00000000E+00 mean dev./std. dev. = 0.00000000E+00 mean value = 1.00000000E+00 sqrt(beta one) = 0.00000000E+00 median value = 1.00000000E+00 beta two = 0.00000000E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ 1.214286E+00 1.000 1.000 1.000 1 + 1.642857E+00 1.000 0.000 0.000 0 2.071429E+00 1.000 0.000 0.000 0 2.500000E+00 1.000 0.000 0.000 0 2.928571E+00 1.000 0.000 0.000 0 3.357143E+00 1.000 0.000 0.000 0 3.785714E+00 1.000 0.000 0.000 0 run hist on 200 pseudo-randon numbers starpac 2.08s (03/15/90) histogram number of observations = 200 minimum observation = -2.07073784E+00 maximum observation = 3.05190754E+00 histogram lower bound = -2.07073784E+00 histogram upper bound = 3.05190754E+00 number of cells = 9 observations used = 200 25 pct trimmed mean = -3.59937251E-02 min. observation used = -2.07073784E+00 standard deviation = 1.01829910E+00 max. observation used = 3.05190754E+00 mean dev./std. dev. = 8.08220685E-01 mean value = 8.36084131E-03 sqrt(beta one) = 3.15783143E-01 median value = -5.38992658E-02 beta two = 2.78905058E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ -1.786146E+00 0.050 1.000 0.050 10 ++++++++++ -1.216964E+00 0.190 0.950 0.140 28 ++++++++++++++++++++++++++++ -6.477807E-01 0.390 0.810 0.200 40 ++++++++++++++++++++++++++++++++++++++++ -7.859790E-02 0.620 0.610 0.230 46 ++++++++++++++++++++++++++++++++++++++++++++++ 4.905849E-01 0.770 0.380 0.150 30 ++++++++++++++++++++++++++++++ 1.059768E+00 0.915 0.230 0.145 29 +++++++++++++++++++++++++++++ 1.628951E+00 0.955 0.085 0.040 8 ++++++++ 2.198133E+00 0.985 0.045 0.030 6 ++++++ 2.767316E+00 1.000 0.015 0.015 3 +++ 1miscellaneous errors - test 1 call to lls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine lls ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the correct form of the call statement is call lls (y, xm, n, ixm, npar, res, lsdtak) ierr = 1 call to llss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llss ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to npar . the correct form of the call statement is call llss (y, xm, n, ixm, npar, res, lsdtak, + nprt, par, rsd, pv, sdpv, sdres, vcv, ivcv) ierr = 1 call to llsw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsw ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the correct form of the call statement is call llsw (y, wt, xm, n, ixm, npar, res, lsdtak) ierr = 1 call to llsws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsws ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to npar . the correct form of the call statement is call llsws (y, wt, xm, n, ixm, npar, res, lsdtak, + nprt, par, rsd, pv, sdpv, sdres, vcv, ivcv) ierr = 1 call to llsp starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsp ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the correct form of the call statement is call llsp (y, x, n, ndeg, res, lsdtak) ierr = 1 call to llsps starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsps ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of lpar is -1. the length of par , as indicated by the argument lpar , must be greater than or equal to ndeg+1 . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to ndeg+1 . the correct form of the call statement is call llsps (y, x, n, ndeg, res, lsdtak, + nprt, lpar, par, npar, rsd, pv, sdpv, + sdres, vcv, ivcv) ierr = 1 call to llspw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspw ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the correct form of the call statement is call llspw (y, wt, x, n, ndeg, res, lsdtak) ierr = 1 call to llspws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspws ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to 1. the input value of lpar is -1. the length of par , as indicated by the argument lpar , must be greater than or equal to ndeg+1 . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to ndeg+1 . the correct form of the call statement is call llspws (y, wt, x, n, ndeg, res, lsdtak, + nprt, lpar, par, npar, rsd, pv, sdpv, + sdres, vcv, ivcv) ierr = 1 1miscellaneous errors - test 2 call to lls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine lls ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call lls (y, xm, n, ixm, npar, res, lsdtak) ierr = 1 call to llss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llss ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call llss (y, xm, n, ixm, npar, res, lsdtak, + nprt, par, rsd, pv, sdpv, sdres, vcv, ivcv) ierr = 1 call to llsw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsw ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call llsw (y, wt, xm, n, ixm, npar, res, lsdtak) ierr = 1 call to llsws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsws ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call llsws (y, wt, xm, n, ixm, npar, res, lsdtak, + nprt, par, rsd, pv, sdpv, sdres, vcv, ivcv) ierr = 1 call to llsp starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsp ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call llsp (y, x, n, ndeg, res, lsdtak) ierr = 1 call to llsps starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsps ------------------------------------- the input value of lpar is -10. the length of par , as indicated by the argument lpar , must be greater than or equal to ndeg+1 . the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call llsps (y, x, n, ndeg, res, lsdtak, + nprt, lpar, par, npar, rsd, pv, sdpv, + sdres, vcv, ivcv) ierr = 1 call to llspw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspw ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call llspw (y, wt, x, n, ndeg, res, lsdtak) ierr = 1 call to llspws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspws ------------------------------------- the input value of lpar is -10. the length of par , as indicated by the argument lpar , must be greater than or equal to ndeg+1 . the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 100. the correct form of the call statement is call llspws (y, wt, x, n, ndeg, res, lsdtak, + nprt, lpar, par, npar, rsd, pv, sdpv, + sdres, vcv, ivcv) ierr = 1 1negative weights call to llsw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsw ------------------------------------- negative values were found in the vector wt . all weights must be greater than or equal to zero. the correct form of the call statement is call llsw (y, wt, xm, n, ixm, npar, res, lsdtak) ierr = 1 call to llsws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsws ------------------------------------- negative values were found in the vector wt . all weights must be greater than or equal to zero. the correct form of the call statement is call llsws (y, wt, xm, n, ixm, npar, res, lsdtak, + nprt, par, rsd, pv, sdpv, sdres, vcv, ivcv) ierr = 1 call to llspw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspw ------------------------------------- negative values were found in the vector wt . all weights must be greater than or equal to zero. the correct form of the call statement is call llspw (y, wt, x, n, ndeg, res, lsdtak) ierr = 1 call to llspws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspws ------------------------------------- negative values were found in the vector wt . all weights must be greater than or equal to zero. the correct form of the call statement is call llspws (y, wt, x, n, ndeg, res, lsdtak, + nprt, lpar, par, npar, rsd, pv, sdpv, + sdres, vcv, ivcv) ierr = 1 1too few positive weights call to llsw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsw ------------------------------------- the number of nonzero weights found is 1. the number of nonzero weights in wt must be greater than or equal to npar . the correct form of the call statement is call llsw (y, wt, xm, n, ixm, npar, res, lsdtak) ierr = 1 call to llsws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llsws ------------------------------------- the number of nonzero weights found is 1. the number of nonzero weights in wt must be greater than or equal to npar . the correct form of the call statement is call llsws (y, wt, xm, n, ixm, npar, res, lsdtak, + nprt, par, rsd, pv, sdpv, sdres, vcv, ivcv) ierr = 1 call to llspw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspw ------------------------------------- the number of nonzero weights found is 1. the number of nonzero weights in wt must be greater than or equal to ndeg+1 . the correct form of the call statement is call llspw (y, wt, x, n, ndeg, res, lsdtak) ierr = 1 call to llspws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine llspws ------------------------------------- the number of nonzero weights found is 1. the number of nonzero weights in wt must be greater than or equal to ndeg+1 . the correct form of the call statement is call llspws (y, wt, x, n, ndeg, res, lsdtak, + nprt, lpar, par, npar, rsd, pv, sdpv, + sdres, vcv, ivcv) ierr = 1 1valid problem call to lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 11.200000 31.900000 167.10001 166.22433 4.1659665 .87567139 0.19 2 48.599998 10.600000 13.200000 174.39999 175.37187 5.5242224 -.97187805 -0.32 3 42.599998 10.600000 28.700001 160.80000 162.29243 3.7470179 -1.4924316 -0.30 4 39.000000 10.400000 26.100000 162.00000 161.23793 2.6437988 .76206970 0.13 5 34.700001 9.3000002 30.100000 140.80000 145.41245 4.7570844 -4.6124420 -1.12 6 44.500000 10.800000 8.5000000 174.60001 180.06189 4.7618556 -5.4618835 -1.33 7 39.099998 10.700000 24.299999 163.70000 165.82922 3.3493509 -2.1292267 -0.40 8 40.099998 10.000000 18.600000 174.50000 162.84686 3.2184489 11.653137 2.16 9 45.900002 12.000000 20.400000 185.70000 184.32285 4.7430372 1.3771515 0.33 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 72.71 174.6 276.5 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----****-+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******** - - - - ******* - - - - * - - - - *** - - - 6+ ** + 2.25+ + - ** - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 251201.453 251201.453 1 159.904999 8 6348.22 0.000 1593.89 0.000 2 822.291443 126011.875 2 65.2783661 7 20.7805 0.006 9.10940 0.018 3 116.029968 84046.5938 3 56.8197632 6 2.93224 0.148 3.27387 0.123 4 143.066574 63070.7109 4 39.5704002 5 3.61549 0.116 3.61549 0.116 residual 197.8520 5 total 252480.6 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -.35058692 1.0242574 -3.4117327 .27696842 3 -.35253084 -.74422741 20.517742 -.77393371 4 -.48059082 .69287580 -.43258217 .15600578 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0142899 36.1906395 1.658 0.158 6.1 26.9439850 38.0299568 .7085 0.505 2 .239835739 1.01205599 .2370 0.822 5.5 1.57313156 .874442697 1.799 0.122 3 10.7183666 4.52965164 2.366 0.064 6.9 6.99273443 4.89365387 1.429 0.203 4 -.750994742 .394975662 -1.901 0.116 6.9 residual standard deviation 6.290501 7.537762 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 res 0.87567138671875E+00 -0.97187805175781E+00 -0.14924316406250E+01 0.76206970214844E+00 -0.46124420166016E+01 -0.54618835449219E+01 -0.21292266845703E+01 0.11653137207031E+02 0.13771514892578E+01 1valid problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -.35058692 1.0242574 -3.4117327 .27696842 3 -.35253084 -.74422741 20.517742 -.77393371 4 -.48059082 .69287580 -.43258217 .15600578 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0142899 36.1906395 1.658 0.158 6.1 26.9439850 38.0299568 .7085 0.505 2 .239835739 1.01205599 .2370 0.822 5.5 1.57313156 .874442697 1.799 0.122 3 10.7183666 4.52965164 2.366 0.064 6.9 6.99273443 4.89365387 1.429 0.203 4 -.750994742 .394975662 -1.901 0.116 6.9 residual standard deviation 6.290501 7.537762 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1valid problem call to llsw starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 42.200001 11.200000 31.900000 167.10001 166.22433 4.1659665 .87567139 0.19 0.100E+01 2 48.599998 10.600000 13.200000 174.39999 175.37187 5.5242224 -.97187805 -0.32 0.100E+01 3 42.599998 10.600000 28.700001 160.80000 162.29243 3.7470179 -1.4924316 -0.30 0.100E+01 4 39.000000 10.400000 26.100000 162.00000 161.23793 2.6437988 .76206970 0.13 0.100E+01 5 34.700001 9.3000002 30.100000 140.80000 145.41245 4.7570844 -4.6124420 -1.12 0.100E+01 6 44.500000 10.800000 8.5000000 174.60001 180.06189 4.7618556 -5.4618835 -1.33 0.100E+01 7 39.099998 10.700000 24.299999 163.70000 165.82922 3.3493509 -2.1292267 -0.40 0.100E+01 8 40.099998 10.000000 18.600000 174.50000 162.84686 3.2184489 11.653137 2.16 0.100E+01 9 45.900002 12.000000 20.400000 185.70000 184.32285 4.7430372 1.3771515 0.33 0.100E+01 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 72.71 174.6 276.5 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----****-+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******** - - - - ******* - - - - * - - - - *** - - - 6+ ** + 2.25+ + - ** - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 251201.453 251201.453 1 159.904999 8 6348.22 0.000 1593.89 0.000 2 822.291443 126011.875 2 65.2783661 7 20.7805 0.006 9.10940 0.018 3 116.029968 84046.5938 3 56.8197632 6 2.93224 0.148 3.27387 0.123 4 143.066574 63070.7109 4 39.5704002 5 3.61549 0.116 3.61549 0.116 residual 197.8520 5 total 252480.6 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -.35058692 1.0242574 -3.4117327 .27696842 3 -.35253084 -.74422741 20.517742 -.77393371 4 -.48059082 .69287580 -.43258217 .15600578 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0142899 36.1906395 1.658 0.158 6.1 26.9439850 38.0299568 .7085 0.505 2 .239835739 1.01205599 .2370 0.822 5.5 1.57313156 .874442697 1.799 0.122 3 10.7183666 4.52965164 2.366 0.064 6.9 6.99273443 4.89365387 1.429 0.203 4 -.750994742 .394975662 -1.901 0.116 6.9 residual standard deviation 6.290501 7.537762 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 res 0.87567138671875E+00 -0.97187805175781E+00 -0.14924316406250E+01 0.76206970214844E+00 -0.46124420166016E+01 -0.54618835449219E+01 -0.21292266845703E+01 0.11653137207031E+02 0.13771514892578E+01 1valid problem call to llsws starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -.35058692 1.0242574 -3.4117327 .27696842 3 -.35253084 -.74422741 20.517742 -.77393371 4 -.48059082 .69287580 -.43258217 .15600578 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0142899 36.1906395 1.658 0.158 6.1 26.9439850 38.0299568 .7085 0.505 2 .239835739 1.01205599 .2370 0.822 5.5 1.57313156 .874442697 1.799 0.122 3 10.7183666 4.52965164 2.366 0.064 6.9 6.99273443 4.89365387 1.429 0.203 4 -.750994742 .394975662 -1.901 0.116 6.9 residual standard deviation 6.290501 7.537762 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1valid problem call to llsp starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 167.10001 171.03033 4.9113727 -3.9303284 -0.63 2 48.599998 174.39999 177.40045 8.6875563 -3.0004578 nc * 3 42.599998 160.80000 171.78400 3.8340197 -10.983994 -1.58 4 39.000000 162.00000 162.43387 4.6638465 -.43386841 -0.07 5 34.700001 140.80000 141.71158 8.0777168 -.91157532 nc * 6 44.500000 174.60001 174.57690 5.8069530 0.23101807E-01 0.00 7 39.099998 163.70000 162.78012 4.9523115 .91987610 0.15 8 40.099998 174.50000 165.94867 3.4023774 8.5513306 1.19 9 45.900002 185.70000 175.93610 5.1488585 9.7639008 1.61 * nc - value not computed because the standard deviation of the residual is zero. 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - *- - * - - * - - * - 0.75+ + 0.75+ + - - - - - - - - - * * * - - ** * - - - - - -0.75+* + -0.75+ * + - - - - - - - - - * - - * - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 70.86 168.5 266.1 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----************---+---------++ 3.75++---------+---------+----+----+---------+---------++ - ***** - - - - * - - - - ** - - - - ********* - - - 6+ *********** + 2.25+ + - ***** - - - - **** - - - - - - * - - - - * - 11+ + 0.75+ + - - - - - - - - - - - * * * - - - - - 16+ + -0.75+ * + - - - - - - - - - - - * - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 251201.453 251201.453 1 160.002838 8 3981.97 0.000 999.316 0.000 2 822.291443 126011.875 2 65.3901825 7 13.0347 0.015 5.09685 0.056 3 139.108398 84054.2891 3 53.1038170 6 2.20511 0.198 1.12791 0.394 4 3.19962549 63041.5156 4 63.0846558 5 0.507196E-01 0.831 0.507194E-01 0.831 residual 315.4233 5 total 252480.6 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 7442863.0 -543561.19 13137.016 -105.08862 2 -.99948329 39737.977 -961.35895 7.6976404 3 .99800873 -.99951726 23.280094 -.18657875 4 -.99567556 .99812961 -.99954426 0.14967029E-02 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 -854.994141 2728.16113 -.3134 0.767 5.1 -308.328308 232.294388 -1.327 0.233 2 60.5287361 199.343872 .3036 0.774 5.0 20.4859905 11.1695433 1.834 0.116 3 -1.18674350 4.82494497 -.2460 0.815 4.9 -.216170192 .133501887 -1.619 0.157 4 0.778577290E-02 0.386872441E-01 .2012 0.848 4.8 residual standard deviation 7.942585 7.279804 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.7534 approximate condition number 0.9933138E+08 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 3 res -0.39303283691406E+01 -0.30004577636719E+01 -0.10983993530273E+02 -0.43386840820312E+00 -0.91157531738281E+00 0.23101806640625E-01 0.91987609863281E+00 0.85513305664062E+01 0.97639007568359E+01 1valid problem call to llsps starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 7442863.0 -543561.19 13137.016 -105.08862 2 -.99948329 39737.977 -961.35895 7.6976404 3 .99800873 -.99951726 23.280094 -.18657875 4 -.99567556 .99812961 -.99954426 0.14967029E-02 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 -854.994141 2728.16113 -.3134 0.767 5.1 -308.328308 232.294388 -1.327 0.233 2 60.5287361 199.343872 .3036 0.774 5.0 20.4859905 11.1695433 1.834 0.116 3 -1.18674350 4.82494497 -.2460 0.815 4.9 -.216170192 .133501887 -1.619 0.157 4 0.778577290E-02 0.386872441E-01 .2012 0.848 4.8 residual standard deviation 7.942585 7.279804 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.7534 approximate condition number 0.9933138E+08 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. the correct form of the call statement is call llsps (y, x, n, ndeg, res, lsdtak, + nprt, lpar, par, npar, rsd, pv, sdpv, + sdres, vcv, ivcv) ierr = 3 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value -854.9941 60.52874 -1.186743 0.7785773E-02 pv sdpv res sdres 171.0303 4.911373 -3.930328 -.6296544 177.4005 8.687556 -3.000458 0.3402823E+39 171.7840 3.834020 -10.98399 -1.579084 162.4339 4.663846 -.4338684 -0.6748521E-01 141.7116 8.077717 -.9115753 0.3402823E+39 174.5769 5.806953 0.2310181E-01 0.4263229E-02 162.7801 4.952312 .9198761 .1481375 165.9487 3.402377 8.551331 1.191501 175.9361 5.148859 9.763901 1.614498 variance covariance matrix column 1 2 3 4 1 7442863.0 -543561.19 13137.016 -105.08862 2 -543561.19 39737.977 -961.35895 7.6976404 3 13137.016 -961.35895 23.280094 -.18657875 4 -105.08862 7.6976404 -.18657875 0.14967029E-02 rsd = 7.942585 1valid problem call to llspw starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 42.200001 167.10001 171.03033 4.9113727 -3.9303284 -0.63 0.100E+01 2 48.599998 174.39999 177.40045 8.6875563 -3.0004578 nc * 0.100E+01 3 42.599998 160.80000 171.78400 3.8340197 -10.983994 -1.58 0.100E+01 4 39.000000 162.00000 162.43387 4.6638465 -.43386841 -0.07 0.100E+01 5 34.700001 140.80000 141.71158 8.0777168 -.91157532 nc * 0.100E+01 6 44.500000 174.60001 174.57690 5.8069530 0.23101807E-01 0.00 0.100E+01 7 39.099998 163.70000 162.78012 4.9523115 .91987610 0.15 0.100E+01 8 40.099998 174.50000 165.94867 3.4023774 8.5513306 1.19 0.100E+01 9 45.900002 185.70000 175.93610 5.1488585 9.7639008 1.61 0.100E+01 * nc - value not computed because the standard deviation of the residual is zero. 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - *- - * - - * - - * - 0.75+ + 0.75+ + - - - - - - - - - * * * - - ** * - - - - - -0.75+* + -0.75+ * + - - - - - - - - - * - - * - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 70.86 168.5 266.1 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----************---+---------++ 3.75++---------+---------+----+----+---------+---------++ - ***** - - - - * - - - - ** - - - - ********* - - - 6+ *********** + 2.25+ + - ***** - - - - **** - - - - - - * - - - - * - 11+ + 0.75+ + - - - - - - - - - - - * * * - - - - - 16+ + -0.75+ * + - - - - - - - - - - - * - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 251201.453 251201.453 1 160.002838 8 3981.97 0.000 999.316 0.000 2 822.291443 126011.875 2 65.3901825 7 13.0347 0.015 5.09685 0.056 3 139.108398 84054.2891 3 53.1038170 6 2.20511 0.198 1.12791 0.394 4 3.19962549 63041.5156 4 63.0846558 5 0.507196E-01 0.831 0.507194E-01 0.831 residual 315.4233 5 total 252480.6 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 7442863.0 -543561.19 13137.016 -105.08862 2 -.99948329 39737.977 -961.35895 7.6976404 3 .99800873 -.99951726 23.280094 -.18657875 4 -.99567556 .99812961 -.99954426 0.14967029E-02 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 -854.994141 2728.16113 -.3134 0.767 5.1 -308.328308 232.294388 -1.327 0.233 2 60.5287361 199.343872 .3036 0.774 5.0 20.4859905 11.1695433 1.834 0.116 3 -1.18674350 4.82494497 -.2460 0.815 4.9 -.216170192 .133501887 -1.619 0.157 4 0.778577290E-02 0.386872441E-01 .2012 0.848 4.8 residual standard deviation 7.942585 7.279804 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.7534 approximate condition number 0.9933138E+08 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. the correct form of the call statement is call llspw (y, wt, x, n, ndeg, res, lsdtak) ierr = 3 res -0.39303283691406E+01 -0.30004577636719E+01 -0.10983993530273E+02 -0.43386840820312E+00 -0.91157531738281E+00 0.23101806640625E-01 0.91987609863281E+00 0.85513305664062E+01 0.97639007568359E+01 1valid problem call to llspws starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 7442863.0 -543561.19 13137.016 -105.08862 2 -.99948329 39737.977 -961.35895 7.6976404 3 .99800873 -.99951726 23.280094 -.18657875 4 -.99567556 .99812961 -.99954426 0.14967029E-02 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 -854.994141 2728.16113 -.3134 0.767 5.1 -308.328308 232.294388 -1.327 0.233 2 60.5287361 199.343872 .3036 0.774 5.0 20.4859905 11.1695433 1.834 0.116 3 -1.18674350 4.82494497 -.2460 0.815 4.9 -.216170192 .133501887 -1.619 0.157 4 0.778577290E-02 0.386872441E-01 .2012 0.848 4.8 residual standard deviation 7.942585 7.279804 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.7534 approximate condition number 0.9933138E+08 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 3 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value -854.9941 60.52874 -1.186743 0.7785773E-02 pv sdpv res sdres 171.0303 4.911373 -3.930328 -.6296544 177.4005 8.687556 -3.000458 0.3402823E+39 171.7840 3.834020 -10.98399 -1.579084 162.4339 4.663846 -.4338684 -0.6748521E-01 141.7116 8.077717 -.9115753 0.3402823E+39 174.5769 5.806953 0.2310181E-01 0.4263229E-02 162.7801 4.952312 .9198761 .1481375 165.9487 3.402377 8.551331 1.191501 175.9361 5.148859 9.763901 1.614498 variance covariance matrix column 1 2 3 4 1 7442863.0 -543561.19 13137.016 -105.08862 2 -543561.19 39737.977 -961.35895 7.6976404 3 13137.016 -961.35895 23.280094 -.18657875 4 -105.08862 7.6976404 -.18657875 0.14967029E-02 rsd = 7.942585 1zero residual problem call to lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 42.64768 par( 2) = -.4517429 par( 3) = 15.81106 par( 4) = -1.052287 ierr = 0 res 0.00000000000000E+00 0.00000000000000E+00 0.00000000000000E+00 0.15258789062500E-04 1zero residual problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 42.64768 par( 2) = -.4517429 par( 3) = 15.81106 par( 4) = -1.052287 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 42.64768 -.4517429 15.81106 -1.052287 pv sdpv res sdres 167.1000 .0000000 .0000000 .0000000 174.4000 .0000000 .0000000 .0000000 160.8000 .0000000 .0000000 .0000000 162.0000 .0000000 0.1525879E-04 .0000000 variance covariance matrix column 1 2 3 4 1 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 rsd = .0000000 1zero residual problem call to llsp starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = -63555.89 par( 2) = 4450.415 par( 3) = -103.2209 par( 4) = .7948677 ierr = 0 res -0.15563964843750E-02 -0.21881103515625E-01 -0.46844482421875E-02 -0.78125000000000E-02 1zero residual problem call to llsps starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = -63555.89 par( 2) = 4450.415 par( 3) = -103.2209 par( 4) = .7948677 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value -63555.89 4450.415 -103.2209 .7948677 pv sdpv res sdres 167.1016 .0000000 -0.1556396E-02 .0000000 174.4219 .0000000 -0.2188110E-01 .0000000 160.8047 .0000000 -0.4684448E-02 .0000000 162.0078 .0000000 -0.7812500E-02 .0000000 variance covariance matrix column 1 2 3 4 1 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 rsd = .0000000 1zero residual problem call to llsw starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 42.64768 par( 2) = -.4517429 par( 3) = 15.81106 par( 4) = -1.052287 ierr = 0 res 0.00000000000000E+00 0.00000000000000E+00 0.00000000000000E+00 0.15258789062500E-04 -0.15411682128906E+01 -0.97600708007812E+01 -0.48922424316406E+01 0.11429199218750E+02 -0.44786987304688E+01 1zero residual problem call to llsws starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 42.64768 par( 2) = -.4517429 par( 3) = 15.81106 par( 4) = -1.052287 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 42.64768 -.4517429 15.81106 -1.052287 pv sdpv res sdres 167.1000 .0000000 .0000000 .0000000 174.4000 .0000000 .0000000 .0000000 160.8000 .0000000 .0000000 .0000000 162.0000 .0000000 0.1525879E-04 .0000000 142.3412 .0000000 -1.541168 .0000000 184.3601 .0000000 -9.760071 .0000000 168.5922 .0000000 -4.892242 .0000000 163.0708 .0000000 11.42920 .0000000 190.1787 .0000000 -4.478699 .0000000 variance covariance matrix column 1 2 3 4 1 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 rsd = .0000000 1zero residual problem call to llspw starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = -63555.89 par( 2) = 4450.415 par( 3) = -103.2209 par( 4) = .7948677 ierr = 0 res -0.15563964843750E-02 -0.21881103515625E-01 -0.46844482421875E-02 -0.78125000000000E-02 0.34349530029297E+03 0.45654693603516E+02 -0.80390930175781E+00 -0.49726562500000E+01 0.68676559448242E+02 1zero residual problem call to llspws starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = -63555.89 par( 2) = 4450.415 par( 3) = -103.2209 par( 4) = .7948677 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value -63555.89 4450.415 -103.2209 .7948677 pv sdpv res sdres 167.1016 .0000000 -0.1556396E-02 .0000000 174.4219 .0000000 -0.2188110E-01 .0000000 160.8047 .0000000 -0.4684448E-02 .0000000 162.0078 .0000000 -0.7812500E-02 .0000000 -202.6953 .0000000 343.4953 .0000000 128.9453 .0000000 45.65469 .0000000 164.5039 .0000000 -.8039093 .0000000 179.4727 .0000000 -4.972656 .0000000 117.0234 .0000000 68.67656 .0000000 variance covariance matrix column 1 2 3 4 1 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 rsd = .0000000 1rank deficient problem call to lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** *********** * error * *********** the design matrix is singular to within machine precision. check the design matrix for a linear relationship between some of the columns. ierr = 2 1poorly scaled problem call to lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 11.200000 31.900000 0.16710001E-05 0.16622436E-05 0.41659639E-07 0.87565013E-08 0.19 2 48.599998 10.600000 13.200000 0.17440000E-05 0.17537188E-05 0.55242033E-07 -0.97188604E-08 -0.32 3 42.599998 10.600000 28.700001 0.16080000E-05 0.16229245E-05 0.37469931E-07 -0.14924467E-07 -0.30 4 39.000000 10.400000 26.100000 0.16200000E-05 0.16123795E-05 0.26437565E-07 0.76205424E-08 0.13 5 34.700001 9.3000002 30.100000 0.14080000E-05 0.14541249E-05 0.47570751E-07 -0.46124910E-07 -1.12 6 44.500000 10.800000 8.5000000 0.17460001E-05 0.18006190E-05 0.47618386E-07 -0.54618909E-07 -1.33 7 39.099998 10.700000 24.299999 0.16370000E-05 0.16582924E-05 0.33493343E-07 -0.21292408E-07 -0.40 8 40.099998 10.000000 18.600000 0.17450000E-05 0.16284689E-05 0.32184136E-07 0.11653106E-06 2.16 9 45.900002 12.000000 20.400000 0.18569999E-05 0.18432289E-05 0.47430454E-07 0.13771000E-07 0.33 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 0.7271E-06 0.1746E-05 0.2765E-05 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----****-+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******** - - - - ******* - - - - * - - - - *** - - - 6+ ** + 2.25+ + - ** - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 0.251201455E-10 0.251201455E-10 1 0.159905014E-13 8 6348.23 0.000 1593.89 0.000 2 0.822291725E-13 0.126011875E-10 2 0.652783432E-14 7 20.7805 0.006 9.10942 0.018 3 0.116029826E-13 0.840465978E-11 3 0.568197621E-14 6 2.93224 0.148 3.27387 0.123 4 0.143066829E-13 0.630707118E-11 4 0.395703427E-14 5 3.61551 0.116 3.61551 0.116 residual 0.1978517E-13 5 total 0.2524806E-10 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 0.13097604E-12 -.12840913E-14 -.57790644E-14 -.68697585E-15 2 -.35058689 0.10242559E-15 -.34117277E-15 0.27696803E-16 3 -.35253084 -.74422735 0.20517713E-14 -.77393261E-16 4 -.48059088 .69287574 -.43258220 0.15600555E-16 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 0.600143267E-06 0.361906132E-06 1.658 0.158 6.1 0.269440193E-06 0.380299412E-06 .7085 0.505 2 0.239836062E-08 0.101205533E-07 .2370 0.822 5.5 0.157313167E-07 0.874442296E-08 1.799 0.122 3 0.107183638E-06 0.452964812E-07 2.366 0.064 6.2 0.699273244E-07 0.489365171E-07 1.429 0.203 4 -0.750994644E-08 0.394975386E-08 -1.901 0.116 6.5 residual standard deviation 0.6290496E-07 0.7537759E-07 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 1poorly scaled problem call to lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 0.11200000E+10 31.900000 167.10001 166.22432 4.1659946 .87568665 0.19 2 48.599998 0.10600001E+10 13.200000 174.39999 175.37187 5.5242624 -.97187805 -0.32 3 42.599998 0.10600001E+10 28.700001 160.80000 162.29242 3.7470467 -1.4924164 -0.30 4 39.000000 0.10399999E+10 26.100000 162.00000 161.23793 2.6438375 .76206970 0.13 5 34.700001 0.93000000E+09 30.100000 140.80000 145.41245 4.7571077 -4.6124420 -1.12 6 44.500000 0.10800000E+10 8.5000000 174.60001 180.06189 4.7618828 -5.4618835 -1.33 7 39.099998 0.10700000E+10 24.299999 163.70000 165.82922 3.3494177 -2.1292267 -0.40 8 40.099998 0.10000000E+10 18.600000 174.50000 162.84686 3.2184525 11.653137 2.16 9 45.900002 0.12000000E+10 20.400000 185.70000 184.32286 4.7430692 1.3771362 0.33 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 72.71 174.6 276.5 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----****-+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******** - - - - ******* - - - - * - - - - *** - - - 6+ ** + 2.25+ + - ** - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 251201.453 251201.453 1 159.896439 8 6348.22 0.000 1593.89 0.000 2 822.291443 126011.875 2 65.2685852 7 20.7805 0.006 9.10883 0.018 3 115.993980 84046.5859 3 56.8143501 6 2.93133 0.148 3.27300 0.123 4 143.034180 63070.6953 4 39.5703888 5 3.61468 0.116 3.61468 0.116 residual 197.8519 5 total 252480.6 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7620 -12.840927 -.57790709E-06 -6.8697662 2 -.35058686 1.0242575 -.34117328E-07 .27696851 3 -.35253084 -.74422747 0.20517738E-14 -.77393398E-08 4 -.48059082 .69287592 -.43258241 .15600577 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0143051 36.1906319 1.658 0.158 6.2 26.9440002 38.0299759 .7085 0.505 2 .239835024 1.01205611 .2370 0.822 5.4 1.57313156 .874443173 1.799 0.122 3 0.107183681E-06 0.452965097E-07 2.366 0.064 6.1 0.699273315E-07 0.489365597E-07 1.429 0.203 4 -.750995040 .394975662 -1.901 0.116 5.9 residual standard deviation 6.290500 7.537766 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 0.1146105E+11 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 1poorly scaled problem call to lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 0.11200000E+10 31.900000 0.16710001E-05 0.16622436E-05 0.41660130E-07 0.87565013E-08 0.19 2 48.599998 0.10600001E+10 13.200000 0.17440000E-05 0.17537188E-05 0.55242374E-07 -0.97188604E-08 -0.32 3 42.599998 0.10600001E+10 28.700001 0.16080000E-05 0.16229245E-05 0.37470524E-07 -0.14924467E-07 -0.30 4 39.000000 0.10399999E+10 26.100000 0.16200000E-05 0.16123795E-05 0.26438331E-07 0.76205424E-08 0.13 5 34.700001 0.93000000E+09 30.100000 0.14080000E-05 0.14541249E-05 0.47571167E-07 -0.46124910E-07 -1.12 6 44.500000 0.10800000E+10 8.5000000 0.17460001E-05 0.18006191E-05 0.47618734E-07 -0.54619022E-07 -1.33 7 39.099998 0.10700000E+10 24.299999 0.16370000E-05 0.16582924E-05 0.33493858E-07 -0.21292408E-07 -0.40 8 40.099998 0.10000000E+10 18.600000 0.17450000E-05 0.16284688E-05 0.32184637E-07 0.11653117E-06 2.16 9 45.900002 0.12000000E+10 20.400000 0.18569999E-05 0.18432288E-05 0.47430621E-07 0.13771114E-07 0.33 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 0.7271E-06 0.1746E-05 0.2765E-05 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----****-+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******** - - - - ******* - - - - * - - - - *** - - - 6+ ** + 2.25+ + - ** - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 0.251201455E-10 0.251201455E-10 1 0.159896510E-13 8 6348.21 0.000 1593.88 0.000 2 0.822291725E-13 0.126011875E-10 2 0.652686108E-14 7 20.7805 0.006 9.10882 0.018 3 0.115993827E-13 0.840465805E-11 3 0.568144088E-14 6 2.93133 0.148 3.27300 0.123 4 0.143034303E-13 0.630706945E-11 4 0.395704274E-14 5 3.61468 0.116 3.61468 0.116 residual 0.1978521E-13 5 total 0.2524806E-10 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 0.13097633E-12 -.12840940E-14 -.57790763E-22 -.68697728E-15 2 -.35058689 0.10242586E-15 -.34117362E-23 0.27696877E-16 3 -.35253084 -.74422753 0.20517758E-30 -.77393472E-24 4 -.48059076 .69287586 -.43258235 0.15600593E-16 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 0.600143494E-06 0.361906501E-06 1.658 0.158 6.4 0.269440307E-06 0.380299554E-06 .7085 0.505 2 0.239835174E-08 0.101205657E-07 .2370 0.822 5.4 0.157313167E-07 0.874442740E-08 1.799 0.122 3 0.107183655E-14 0.452965321E-15 2.366 0.064 6.3 0.699273079E-15 0.489365344E-15 1.429 0.203 4 -0.750994911E-08 0.394975874E-08 -1.901 0.116 6.9 residual standard deviation 0.6290503E-07 0.7537762E-07 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 0.1146105E+11 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 1minimum work area size call to lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 11.200000 31.900000 167.10001 166.22433 4.1659665 .87567139 0.19 2 48.599998 10.600000 13.200000 174.39999 175.37187 5.5242224 -.97187805 -0.32 3 42.599998 10.600000 28.700001 160.80000 162.29243 3.7470179 -1.4924316 -0.30 4 39.000000 10.400000 26.100000 162.00000 161.23793 2.6437988 .76206970 0.13 5 34.700001 9.3000002 30.100000 140.80000 145.41245 4.7570844 -4.6124420 -1.12 6 44.500000 10.800000 8.5000000 174.60001 180.06189 4.7618556 -5.4618835 -1.33 7 39.099998 10.700000 24.299999 163.70000 165.82922 3.3493509 -2.1292267 -0.40 8 40.099998 10.000000 18.600000 174.50000 162.84686 3.2184489 11.653137 2.16 9 45.900002 12.000000 20.400000 185.70000 184.32285 4.7430372 1.3771515 0.33 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 72.71 174.6 276.5 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----****-+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******** - - - - ******* - - - - * - - - - *** - - - 6+ ** + 2.25+ + - ** - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 251201.453 251201.453 1 159.904999 8 6348.22 0.000 1593.89 0.000 2 822.291443 126011.875 2 65.2783661 7 20.7805 0.006 9.10940 0.018 3 116.029968 84046.5938 3 56.8197632 6 2.93224 0.148 3.27387 0.123 4 143.066574 63070.7109 4 39.5704002 5 3.61549 0.116 3.61549 0.116 residual 197.8520 5 total 252480.6 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -.35058692 1.0242574 -3.4117327 .27696842 3 -.35253084 -.74422741 20.517742 -.77393371 4 -.48059082 .69287580 -.43258217 .15600578 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0142899 36.1906395 1.658 0.158 6.1 26.9439850 38.0299568 .7085 0.505 2 .239835739 1.01205599 .2370 0.822 5.5 1.57313156 .874442697 1.799 0.122 3 10.7183666 4.52965164 2.366 0.064 6.9 6.99273443 4.89365387 1.429 0.203 4 -.750994742 .394975662 -1.901 0.116 6.9 residual standard deviation 6.290501 7.537762 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 res 0.87567138671875E+00 -0.97187805175781E+00 -0.14924316406250E+01 0.76206970214844E+00 -0.46124420166016E+01 -0.54618835449219E+01 -0.21292266845703E+01 0.11653137207031E+02 0.13771514892578E+01 1weighted analysis call to llsws starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 42.200001 11.200000 31.900000 167.10001 166.22435 4.1659813 .87565613 0.19 0.100E+03 2 48.599998 10.600000 13.200000 174.39999 175.37189 5.5242519 -.97189331 -0.32 0.100E+03 3 42.599998 10.600000 28.700001 160.80000 162.29247 3.7470453 -1.4924622 -0.30 0.100E+03 4 39.000000 10.400000 26.100000 162.00000 161.23795 2.6438088 .76205444 0.13 0.100E+03 5 34.700001 9.3000002 30.100000 140.80000 145.41249 4.7571197 -4.6124878 -1.12 0.100E+03 6 44.500000 10.800000 8.5000000 174.60001 180.06192 4.7618675 -5.4619141 -1.33 0.100E+03 7 39.099998 10.700000 24.299999 163.70000 165.82922 3.3493779 -2.1292267 -0.40 0.100E+03 8 40.099998 10.000000 18.600000 174.50000 162.84688 3.2184596 11.653122 2.16 0.100E+03 9 45.900002 12.000000 20.400000 185.70000 184.32288 4.7430577 1.3771210 0.33 0.100E+03 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 72.71 174.6 276.5 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - * - - - - * - - - - * - - - 6+ * + 2.25+ + - * - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 25120148.0 25120148.0 1 15989.8164 8 6348.20 0.000 1593.88 0.000 2 82224.6562 12601186.0 2 6527.69580 7 20.7793 0.006 9.10892 0.018 3 11601.2783 8404658.00 3 5682.09912 6 2.93180 0.148 3.27373 0.123 4 14307.3516 6307070.50 4 3957.04810 5 3.61566 0.116 3.61566 0.116 residual 19785.24 5 total 0.2524806E+08 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7654 -12.840961 -57.790836 -6.8697834 2 -.35058719 1.0242583 -3.4117339 .27696878 3 -.35253099 -.74422705 20.517757 -.77393442 4 -.48059091 .69287568 -.43258205 .15600607 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0142975 36.1906815 1.658 0.158 6.9 26.9439850 38.0299416 .7085 0.505 2 .239836931 1.01205647 .2370 0.822 5.3 1.57313180 .874441922 1.799 0.122 3 10.7183628 4.52965307 2.366 0.064 6.3 6.99273443 4.89365005 1.429 0.203 4 -.750994623 .394976020 -1.901 0.116 6.1 residual standard deviation 62.90507 75.37760 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01430 .2398369 10.71836 -.7509946 pv sdpv res sdres 166.2243 4.165981 .8756561 .1857846 175.3719 5.524252 -.9718933 -.3230125 162.2925 3.747045 -1.492462 -.2953771 161.2379 2.643809 .7620544 .1335074 145.4125 4.757120 -4.612488 -1.120662 180.0619 4.761868 -5.461914 -1.328815 165.8292 3.349378 -2.129227 -.3998793 162.8469 3.218460 11.65312 2.156064 184.3229 4.743058 1.377121 .3332774 variance covariance matrix column 1 2 3 4 1 1309.7654 -12.840961 -57.790836 -6.8697834 2 -12.840961 1.0242583 -3.4117339 .27696878 3 -57.790836 -3.4117339 20.517757 -.77393442 4 -6.8697834 .27696878 -.77393442 .15600607 rsd = 62.90507 1weighted analysis call to llsws starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 42.200001 11.200000 31.900000 167.10001 152.23416 2.5089650 14.865845 nc * 0.000E+00 2 48.599998 10.600000 13.200000 174.39999 174.26176 1.5050535 .13822937 0.17 0.100E+03 3 42.599998 10.600000 28.700001 160.80000 160.64685 1.3645017 .15315247 0.15 0.100E+03 4 39.000000 10.400000 26.100000 162.00000 163.80338 .99248928 -1.8033752 -1.30 0.100E+03 5 34.700001 9.3000002 30.100000 140.80000 170.28090 3.3345726 -29.480896 nc * 0.000E+00 6 44.500000 10.800000 8.5000000 174.60001 175.09802 1.5254644 -.49801636 -0.66 0.100E+03 7 39.099998 10.700000 24.299999 163.70000 162.34999 1.2521139 1.3500061 1.17 0.100E+03 8 40.099998 10.000000 18.600000 174.50000 173.83984 1.6112192 .66015625 1.20 0.100E+03 9 45.900002 12.000000 20.400000 185.70000 154.43661 4.1321015 31.263382 nc * 0.000E+00 * nc - value not computed because the weight is zero. 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * *- - * * - 0.75+ + 0.75+ + - - - - -* * - - * * - - - - - - - - - -0.75+ * + -0.75+ * + - - - - - * - - * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 2.0 5.0 8.0 80.32 171.5 262.6 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - * - - - - * - - - - * - - - 6+ * + 2.25+ + - * - - - - * - - - - - - - - - - * * - 11+ + 0.75+ + - - - - - - - * * - - - - - - - - - 16+ + -0.75+ * + - - - - - - - * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 17001666.0 17001666.0 1 4648.55127 5 58615.6 0.000 14673.4 0.000 2 7325.75195 8504496.00 2 3979.25098 4 25.2566 0.037 26.0442 0.037 3 4161.43115 5671051.50 3 3918.52441 3 14.3471 0.063 26.4381 0.036 4 11175.4668 4256082.50 4 290.053436 2 38.5290 0.025 38.5290 0.025 residual 580.1069 2 total 0.1702491E+08 6 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 855.62866 -.84037107 -76.028954 -1.0056456 2 -.10098005 0.80944188E-01 -.28908029 0.22876609E-01 3 -.89447784 -.34967044 8.4437132 -.27027283E-01 4 -.27034712 .63229287 -.73140025E-01 0.16171902E-01 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 275.883057 29.2511311 9.432 0.011 6.0 226.794022 103.516617 2.191 0.116 2 .229851723 .284506917 .8079 0.504 5.0 1.34653819 .810197651 1.662 0.195 3 -9.65772915 2.90580678 -3.324 0.080 5.7 -10.9770241 10.6524630 -1.030 0.379 4 -.789406478 .127168790 -6.208 0.025 5.9 residual standard deviation 17.03095 62.60183 based on degrees of freedom 6 - 4 = 2 + 6 - 3 = 3 multiple correlation coefficient squared 0.9750 approximate condition number 1341.682 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 275.8831 .2298517 -9.657729 -.7894065 pv sdpv res sdres 152.2342 2.508965 14.86584 0.3402823E+39 174.2618 1.505054 .1382294 .1734181 160.6469 1.364502 .1531525 .1502745 163.8034 .9924893 -1.803375 -1.303002 170.2809 3.334573 -29.48090 0.3402823E+39 175.0980 1.525464 -.4980164 -.6576272 162.3500 1.252114 1.350006 1.169398 173.8398 1.611219 .6601562 1.196321 154.4366 4.132102 31.26338 0.3402823E+39 variance covariance matrix column 1 2 3 4 1 855.62866 -.84037107 -76.028954 -1.0056456 2 -.84037107 0.80944188E-01 -.28908029 0.22876609E-01 3 -76.028954 -.28908029 8.4437132 -.27027283E-01 4 -1.0056456 0.22876609E-01 -.27027283E-01 0.16171902E-01 rsd = 17.03095 1weighted analysis call to llsws starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 42.200001 11.200000 31.900000 167.10001 166.23965 6.0500526 .86035156 0.06 0.100E+01 2 48.599998 10.600000 13.200000 174.39999 178.00922 9.0460386 -3.6092224 -0.48 0.200E+01 3 42.599998 10.600000 28.700001 160.80000 163.37321 6.1841927 -2.5732117 -0.35 0.300E+01 4 39.000000 10.400000 26.100000 162.00000 161.71521 3.1914115 .28479004 0.04 0.400E+01 5 34.700001 9.3000002 30.100000 140.80000 146.54007 5.3794475 -5.7400665 -1.12 0.500E+01 6 44.500000 10.800000 8.5000000 174.60001 181.32912 5.5169249 -6.7291107 -1.70 0.600E+01 7 39.099998 10.700000 24.299999 163.70000 165.85390 3.9397488 -2.1539001 -0.44 0.700E+01 8 40.099998 10.000000 18.600000 174.50000 164.25346 3.5700777 10.246536 2.20 0.800E+01 9 45.900002 12.000000 20.400000 185.70000 184.02005 4.8449855 1.6799469 0.63 0.900E+01 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - *- - * - - - - - -* * - - ** - - * * - - ** - -0.75+ * + -0.75+ * + - - - - - * - - * - - - - - - * - - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 73.27 174.7 276.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - * - - - - * - - - - * - - - 6+ * + 2.25+ + - * - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - * - - - - - - - - * * - - - - * * - 16+ + -0.75+ * + - - - - - - - * - - - - - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 1287307.62 1287307.62 1 946.462219 8 4660.05 0.000 1170.62 0.000 2 5398.56299 646353.125 2 310.447815 7 19.5428 0.007 7.46984 0.027 3 281.550934 430995.906 3 315.263977 6 1.01921 0.359 1.43337 0.322 4 510.367554 323374.531 4 276.243256 5 1.84753 0.232 1.84753 0.232 residual 1381.216 5 total 1294880. 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1792.5814 -32.084167 -12.916672 -14.851823 2 -.48594370 2.4318161 -7.6983209 .61746800 3 -.51982634E-01 -.84115827 34.443386 -1.6222810 4 -.63412952 .71579188 -.49970168 .30600232 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 67.1219788 42.3388863 1.585 0.174 6.6 30.6297455 34.9731369 .8758 0.415 2 .496986389 1.55942810 .3187 0.763 5.3 2.01416016 1.16333330 1.731 0.134 3 9.11872005 5.86884880 1.554 0.181 5.9 5.13263130 5.43072844 .9451 0.381 4 -.751874864 .553174734 -1.359 0.232 6.1 residual standard deviation 16.62057 17.75555 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8176 approximate condition number 475.3752 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 67.12198 .4969864 9.118720 -.7518749 pv sdpv res sdres 166.2397 6.050053 .8603516 0.5557714E-01 178.0092 9.046039 -3.609222 -.4810551 163.3732 6.184193 -2.573212 -.3507000 161.7152 3.191411 .2847900 0.3711562E-01 146.5401 5.379447 -5.740067 -1.119064 181.3291 5.516925 -6.729111 -1.703486 165.8539 3.939749 -2.153900 -.4401980 164.2535 3.570078 10.24654 2.195325 184.0201 4.844985 1.679947 .6252198 variance covariance matrix column 1 2 3 4 1 1792.5814 -32.084167 -12.916672 -14.851823 2 -32.084167 2.4318161 -7.6983209 .61746800 3 -12.916672 -7.6983209 34.443386 -1.6222810 4 -14.851823 .61746800 -1.6222810 .30600232 rsd = 16.62057 1check print control - nprt = 1000 call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 11.200000 31.900000 167.10001 166.22433 4.1659665 .87567139 0.19 2 48.599998 10.600000 13.200000 174.39999 175.37187 5.5242224 -.97187805 -0.32 3 42.599998 10.600000 28.700001 160.80000 162.29243 3.7470179 -1.4924316 -0.30 4 39.000000 10.400000 26.100000 162.00000 161.23793 2.6437988 .76206970 0.13 5 34.700001 9.3000002 30.100000 140.80000 145.41245 4.7570844 -4.6124420 -1.12 6 44.500000 10.800000 8.5000000 174.60001 180.06189 4.7618556 -5.4618835 -1.33 7 39.099998 10.700000 24.299999 163.70000 165.82922 3.3493509 -2.1292267 -0.40 8 40.099998 10.000000 18.600000 174.50000 162.84686 3.2184489 11.653137 2.16 9 45.900002 12.000000 20.400000 185.70000 184.32285 4.7430372 1.3771515 0.33 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1check print control - nprt = 2000 call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 42.200001 11.200000 31.900000 167.10001 166.22433 4.1659665 .87567139 0.19 2 48.599998 10.600000 13.200000 174.39999 175.37187 5.5242224 -.97187805 -0.32 3 42.599998 10.600000 28.700001 160.80000 162.29243 3.7470179 -1.4924316 -0.30 4 39.000000 10.400000 26.100000 162.00000 161.23793 2.6437988 .76206970 0.13 5 34.700001 9.3000002 30.100000 140.80000 145.41245 4.7570844 -4.6124420 -1.12 6 44.500000 10.800000 8.5000000 174.60001 180.06189 4.7618556 -5.4618835 -1.33 7 39.099998 10.700000 24.299999 163.70000 165.82922 3.3493509 -2.1292267 -0.40 8 40.099998 10.000000 18.600000 174.50000 162.84686 3.2184489 11.653137 2.16 9 45.900002 12.000000 20.400000 185.70000 184.32285 4.7430372 1.3771515 0.33 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1check print control - nprt = 200 call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - * - - * - - - - - - - - - - - - - 0.75+ + 0.75+ + - - - - -* *- - * * - - * - - * - - * * * - - ** * - -0.75+ + -0.75+ + - - - - - * * - - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 72.71 174.6 276.5 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----****-+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ******** - - - - ******* - - - - * - - - - *** - - - 6+ ** + 2.25+ + - ** - - * - - * - - - - - - - - - - - 11+ + 0.75+ + - - - - - - - * * - - - - * - - - - * * * - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1check print control - nprt = 20 call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 251201.453 251201.453 1 159.904999 8 6348.22 0.000 1593.89 0.000 2 822.291443 126011.875 2 65.2783661 7 20.7805 0.006 9.10940 0.018 3 116.029968 84046.5938 3 56.8197632 6 2.93224 0.148 3.27387 0.123 4 143.066574 63070.7109 4 39.5704002 5 3.61549 0.116 3.61549 0.116 residual 197.8520 5 total 252480.6 9 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1check print control - nprt = 2 call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -.35058692 1.0242574 -3.4117327 .27696842 3 -.35253084 -.74422741 20.517742 -.77393371 4 -.48059082 .69287580 -.43258217 .15600578 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 60.0142899 36.1906395 1.658 0.158 6.1 26.9439850 38.0299568 .7085 0.505 2 .239835739 1.01205599 .2370 0.822 5.5 1.57313156 .874442697 1.799 0.122 3 10.7183666 4.52965164 2.366 0.064 6.9 6.99273443 4.89365387 1.429 0.203 4 -.750994742 .394975662 -1.901 0.116 6.9 residual standard deviation 6.290501 7.537762 based on degrees of freedom 9 - 4 = 5 + 9 - 3 = 6 multiple correlation coefficient squared 0.8453 approximate condition number 459.1503 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1check print control - nprt = 0 call to llss ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 value 60.01429 .2398357 10.71837 -.7509947 pv sdpv res sdres 166.2243 4.165967 .8756714 .1857876 175.3719 5.524222 -.9718781 -.3230031 162.2924 3.747018 -1.492432 -.2953703 161.2379 2.643799 .7620697 .1335102 145.4124 4.757084 -4.612442 -1.120643 180.0619 4.761856 -5.461884 -1.328806 165.8292 3.349351 -2.129227 -.3998786 162.8469 3.218449 11.65314 2.156067 184.3228 4.743037 1.377151 .3332837 variance covariance matrix column 1 2 3 4 1 1309.7623 -12.840931 -57.790730 -6.8697681 2 -12.840931 1.0242574 -3.4117327 .27696842 3 -57.790730 -3.4117327 20.517742 -.77393371 4 -6.8697681 .27696842 -.77393371 .15600578 rsd = 6.290501 1check minimum problem size call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.0000000 167.10001 170.75000 3.6499937 -3.6499939 -1.00 2 1.0000000 174.39999 170.75000 3.6499937 3.6499939 1.00 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - - - - 0.75+ *+ 0.75+ * + - - - - - - - - - - - - - - - - -0.75+ + -0.75+ + -* - - * - - - - - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 1.5 2.0 85.38 170.8 256.1 autocorrelation function of residuals normal probability plot of std res 1++---------+--*************----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - - 11+ + 0.75+ * + - - - - - - - - - - - - - - - - 16+ + -0.75+ + - - - * - - - - - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 58311.1211 58311.1211 1 26.6449108 1 2188.45 0.014 2188.45 0.014 residual 26.64491 1 total 58337.77 2 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 1 13.322454 ------------------------- estimates from fit ------------------------ estimated parameter sd of par t(par=0) prob(t) acc dig* 1 170.750000 3.64999366 46.78 0.014 6.9 residual standard deviation 5.161871 based on degrees of freedom 2 - 1 = 1 multiple correlation coefficient squared 0.0000 approximate condition number 1.000000 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 value 170.7500 pv sdpv res sdres 170.7500 3.649994 -3.649994 -.9999999 170.7500 3.649994 3.649994 .9999999 variance covariance matrix column 1 1 13.322454 rsd = 5.161871 1check minimum problem size call to llspws starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 42.200001 167.10001 170.75000 3.6499941 -3.6499939 -1.00 0.100E+03 2 48.599998 174.39999 170.75000 3.6499941 3.6499939 1.00 0.100E+03 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - - - - 0.75+ *+ 0.75+ * + - - - - - - - - - - - - - - - - -0.75+ + -0.75+ + -* - - * - - - - - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 1.5 2.0 85.38 170.8 256.1 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - - 11+ + 0.75+ * + - - - - - - - - - - - - - - - - 16+ + -0.75+ + - - - * - - - - - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 5831113.00 5831113.00 1 2664.49121 1 2188.45 0.014 2188.45 0.014 residual 2664.491 1 total 5833777. 2 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 1 13.322457 ------------------------- estimates from fit ------------------------ estimated parameter sd of par t(par=0) prob(t) acc dig* 1 170.750000 3.64999413 46.78 0.014 6.9 residual standard deviation 51.61871 based on degrees of freedom 2 - 1 = 1 multiple correlation coefficient squared 0.0000 approximate condition number 1.000000 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 value 170.7500 pv sdpv res sdres 170.7500 3.649994 -3.649994 -1.000000 170.7500 3.649994 3.649994 1.000000 variance covariance matrix column 1 1 13.322457 rsd = 51.61871 1check minimum problem size call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 167.1000 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 value 167.1000 pv sdpv res sdres 167.1000 .0000000 .0000000 .0000000 variance covariance matrix column 1 1 .00000000 rsd = .0000000 1check minimum problem size call to llspws starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 167.1000 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 value 167.1000 pv sdpv res sdres 167.1000 .0000000 .0000000 .0000000 variance covariance matrix column 1 1 .00000000 rsd = .0000000 1ill-conditioned problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 1.062500 par( 2) = 1.046875 par( 3) = .9775391 par( 4) = 1.002930 par( 5) = .9998398 par( 6) = 1.000003 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value 1.062500 1.046875 .9775391 1.002930 .9998398 1.000003 pv sdpv res sdres 1.062500 .0000000 -0.6250000E-01 .0000000 6.089687 .0000000 -0.8968687E-01 .0000000 63.08738 .0000000 -0.8738327E-01 .0000000 364.0679 .0000000 -0.6787109E-01 .0000000 1365.040 .0000000 -0.4040527E-01 .0000000 3906.011 .0000000 -0.1147461E-01 .0000000 9330.985 .0000000 0.1464844E-01 .0000000 19607.96 .0000000 0.3515625E-01 .0000000 37448.95 .0000000 0.5078125E-01 .0000000 66429.94 .0000000 0.6250000E-01 .0000000 111110.9 .0000000 0.7031250E-01 .0000000 177155.9 .0000000 0.6250000E-01 .0000000 271452.9 .0000000 0.6250000E-01 .0000000 402233.9 .0000000 0.6250000E-01 .0000000 579194.9 .0000000 0.6250000E-01 .0000000 813615.9 .0000000 0.6250000E-01 .0000000 1118481. .0000000 .0000000 .0000000 1508598. .0000000 .0000000 .0000000 2000719. .0000000 .0000000 .0000000 2613660. .0000000 .0000000 .0000000 3368421. .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 0.6237904E-01 1ill-conditioned problem call to llsps starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 1.062500 par( 2) = 1.046875 par( 3) = .9775391 par( 4) = 1.002930 par( 5) = .9998398 par( 6) = 1.000003 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value 1.062500 1.046875 .9775391 1.002930 .9998398 1.000003 pv sdpv res sdres 1.062500 .0000000 -0.6250000E-01 .0000000 6.089687 .0000000 -0.8968687E-01 .0000000 63.08738 .0000000 -0.8738327E-01 .0000000 364.0679 .0000000 -0.6787109E-01 .0000000 1365.040 .0000000 -0.4040527E-01 .0000000 3906.011 .0000000 -0.1147461E-01 .0000000 9330.985 .0000000 0.1464844E-01 .0000000 19607.96 .0000000 0.3515625E-01 .0000000 37448.95 .0000000 0.5078125E-01 .0000000 66429.94 .0000000 0.6250000E-01 .0000000 111110.9 .0000000 0.7031250E-01 .0000000 177155.9 .0000000 0.6250000E-01 .0000000 271452.9 .0000000 0.6250000E-01 .0000000 402233.9 .0000000 0.6250000E-01 .0000000 579194.9 .0000000 0.6250000E-01 .0000000 813615.9 .0000000 0.6250000E-01 .0000000 1118481. .0000000 .0000000 .0000000 1508598. .0000000 .0000000 .0000000 2000719. .0000000 .0000000 .0000000 2613660. .0000000 .0000000 .0000000 3368421. .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 0.6237904E-01 1ill-conditioned problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 28.00000 par( 2) = -4.500000 par( 3) = 1.625000 par( 4) = .9682617 par( 5) = 1.000702 par( 6) = .9999944 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value 28.00000 -4.500000 1.625000 .9682617 1.000702 .9999944 pv sdpv res sdres 28.00000 .0000000 -28.57090 .0000000 28.09396 .0000000 -24.12351 .0000000 81.25714 .0000000 -18.60447 .0000000 379.3235 .0000000 -14.48785 .0000000 1378.143 .0000000 -15.52991 .0000000 3917.579 .0000000 -11.42725 .0000000 9341.511 .0000000 -11.56152 .0000000 19617.83 .0000000 -9.097656 .0000000 37458.44 .0000000 -10.00781 .0000000 66439.26 .0000000 -9.343750 .0000000 111120.2 .0000000 -7.601562 .0000000 177165.2 .0000000 -10.07812 .0000000 271462.3 .0000000 -9.468750 .0000000 402243.3 .0000000 -9.343750 .0000000 579204.4 .0000000 -10.93750 .0000000 813625.3 .0000000 -7.312500 .0000000 1118490. .0000000 -10.62500 .0000000 1508607. .0000000 -10.87500 .0000000 2000728. .0000000 -6.375000 .0000000 2613668. .0000000 -8.500000 .0000000 3368428. .0000000 -7.000000 .0000000 4288312. .0000000 -6.000000 .0000000 5399049. .0000000 -5.500000 .0000000 6728910. .0000000 -6.000000 .0000000 8308830. .0000000 -4.500000 .0000000 0.1017253E+08 .0000000 -5.000000 .0000000 0.1235663E+08 .0000000 -3.000000 .0000000 0.1490079E+08 .0000000 -2.000000 .0000000 0.1784779E+08 .0000000 -2.000000 .0000000 0.2124369E+08 .0000000 -2.000000 .0000000 0.2513793E+08 .0000000 .0000000 .0000000 0.2958346E+08 .0000000 -2.000000 .0000000 0.3463683E+08 .0000000 .0000000 .0000000 0.4035837E+08 .0000000 .0000000 .0000000 0.4681225E+08 .0000000 4.000000 .0000000 0.5406663E+08 .0000000 4.000000 .0000000 0.6219378E+08 .0000000 .0000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137666E+08 .0000000 -8.000000 .0000000 0.9259852E+08 .0000000 .0000000 .0000000 0.1050256E+09 .0000000 .0000000 .0000000 0.1187526E+09 .0000000 .0000000 .0000000 0.1338788E+09 .0000000 8.000000 .0000000 0.1505087E+09 .0000000 .0000000 .0000000 0.1687515E+09 .0000000 16.00000 .0000000 0.1887219E+09 .0000000 16.00000 .0000000 0.2105399E+09 .0000000 .0000000 .0000000 0.2343308E+09 .0000000 .0000000 .0000000 0.2602253E+09 .0000000 .0000000 .0000000 0.2883602E+09 .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 10.15884 1ill-conditioned problem call to llsps starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 28.00000 par( 2) = -4.500000 par( 3) = 1.625000 par( 4) = .9682617 par( 5) = 1.000702 par( 6) = .9999944 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value 28.00000 -4.500000 1.625000 .9682617 1.000702 .9999944 pv sdpv res sdres 28.00000 .0000000 -28.57090 .0000000 28.09396 .0000000 -24.12351 .0000000 81.25714 .0000000 -18.60447 .0000000 379.3235 .0000000 -14.48785 .0000000 1378.143 .0000000 -15.52991 .0000000 3917.579 .0000000 -11.42725 .0000000 9341.511 .0000000 -11.56152 .0000000 19617.83 .0000000 -9.097656 .0000000 37458.44 .0000000 -10.00781 .0000000 66439.26 .0000000 -9.343750 .0000000 111120.2 .0000000 -7.601562 .0000000 177165.2 .0000000 -10.07812 .0000000 271462.3 .0000000 -9.468750 .0000000 402243.3 .0000000 -9.343750 .0000000 579204.4 .0000000 -10.93750 .0000000 813625.3 .0000000 -7.312500 .0000000 1118490. .0000000 -10.62500 .0000000 1508607. .0000000 -10.87500 .0000000 2000728. .0000000 -6.375000 .0000000 2613668. .0000000 -8.500000 .0000000 3368428. .0000000 -7.000000 .0000000 4288312. .0000000 -6.000000 .0000000 5399049. .0000000 -5.500000 .0000000 6728910. .0000000 -6.000000 .0000000 8308830. .0000000 -4.500000 .0000000 0.1017253E+08 .0000000 -5.000000 .0000000 0.1235663E+08 .0000000 -3.000000 .0000000 0.1490079E+08 .0000000 -2.000000 .0000000 0.1784779E+08 .0000000 -2.000000 .0000000 0.2124369E+08 .0000000 -2.000000 .0000000 0.2513793E+08 .0000000 .0000000 .0000000 0.2958346E+08 .0000000 -2.000000 .0000000 0.3463683E+08 .0000000 .0000000 .0000000 0.4035837E+08 .0000000 .0000000 .0000000 0.4681225E+08 .0000000 4.000000 .0000000 0.5406663E+08 .0000000 4.000000 .0000000 0.6219378E+08 .0000000 .0000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137666E+08 .0000000 -8.000000 .0000000 0.9259852E+08 .0000000 .0000000 .0000000 0.1050256E+09 .0000000 .0000000 .0000000 0.1187526E+09 .0000000 .0000000 .0000000 0.1338788E+09 .0000000 8.000000 .0000000 0.1505087E+09 .0000000 .0000000 .0000000 0.1687515E+09 .0000000 16.00000 .0000000 0.1887219E+09 .0000000 16.00000 .0000000 0.2105399E+09 .0000000 .0000000 .0000000 0.2343308E+09 .0000000 .0000000 .0000000 0.2602253E+09 .0000000 .0000000 .0000000 0.2883602E+09 .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 10.15884 1ill-conditioned problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 28.00000 par( 2) = -4.500000 par( 3) = 1.625000 par( 4) = .9682617 par( 5) = 1.000702 par( 6) = .9999944 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value 28.00000 -4.500000 1.625000 .9682617 1.000702 .9999944 pv sdpv res sdres 28.00000 .0000000 -28.57090 .0000000 28.09396 .0000000 -24.12351 .0000000 81.25714 .0000000 -18.60447 .0000000 379.3235 .0000000 -14.48785 .0000000 1378.143 .0000000 -15.52991 .0000000 3917.579 .0000000 -11.42725 .0000000 9341.511 .0000000 -11.56152 .0000000 19617.83 .0000000 -9.097656 .0000000 37458.44 .0000000 -10.00781 .0000000 66439.26 .0000000 -9.343750 .0000000 111120.2 .0000000 -7.601562 .0000000 177165.2 .0000000 -10.07812 .0000000 271462.3 .0000000 -9.468750 .0000000 402243.3 .0000000 -9.343750 .0000000 579204.4 .0000000 -10.93750 .0000000 813625.3 .0000000 -7.312500 .0000000 1118490. .0000000 -10.62500 .0000000 1508607. .0000000 -10.87500 .0000000 2000728. .0000000 -6.375000 .0000000 2613668. .0000000 -8.500000 .0000000 3368428. .0000000 -7.000000 .0000000 4288312. .0000000 -6.000000 .0000000 5399049. .0000000 -5.500000 .0000000 6728910. .0000000 -6.000000 .0000000 8308830. .0000000 -4.500000 .0000000 0.1017253E+08 .0000000 -5.000000 .0000000 0.1235663E+08 .0000000 -3.000000 .0000000 0.1490079E+08 .0000000 -2.000000 .0000000 0.1784779E+08 .0000000 -2.000000 .0000000 0.2124369E+08 .0000000 -2.000000 .0000000 0.2513793E+08 .0000000 .0000000 .0000000 0.2958346E+08 .0000000 -2.000000 .0000000 0.3463683E+08 .0000000 .0000000 .0000000 0.4035837E+08 .0000000 .0000000 .0000000 0.4681225E+08 .0000000 4.000000 .0000000 0.5406663E+08 .0000000 4.000000 .0000000 0.6219378E+08 .0000000 .0000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137666E+08 .0000000 -8.000000 .0000000 0.9259852E+08 .0000000 .0000000 .0000000 0.1050256E+09 .0000000 .0000000 .0000000 0.1187526E+09 .0000000 .0000000 .0000000 0.1338788E+09 .0000000 8.000000 .0000000 0.1505087E+09 .0000000 .0000000 .0000000 0.1687515E+09 .0000000 16.00000 .0000000 0.1887219E+09 .0000000 16.00000 .0000000 0.2105399E+09 .0000000 .0000000 .0000000 0.2343308E+09 .0000000 .0000000 .0000000 0.2602253E+09 .0000000 .0000000 .0000000 0.2883602E+09 .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 10.15884 1ill-conditioned problem call to llsps starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 28.00000 par( 2) = -4.500000 par( 3) = 1.625000 par( 4) = .9682617 par( 5) = 1.000702 par( 6) = .9999944 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value 28.00000 -4.500000 1.625000 .9682617 1.000702 .9999944 pv sdpv res sdres 28.00000 .0000000 -28.57090 .0000000 28.09396 .0000000 -24.12351 .0000000 81.25714 .0000000 -18.60447 .0000000 379.3235 .0000000 -14.48785 .0000000 1378.143 .0000000 -15.52991 .0000000 3917.579 .0000000 -11.42725 .0000000 9341.511 .0000000 -11.56152 .0000000 19617.83 .0000000 -9.097656 .0000000 37458.44 .0000000 -10.00781 .0000000 66439.26 .0000000 -9.343750 .0000000 111120.2 .0000000 -7.601562 .0000000 177165.2 .0000000 -10.07812 .0000000 271462.3 .0000000 -9.468750 .0000000 402243.3 .0000000 -9.343750 .0000000 579204.4 .0000000 -10.93750 .0000000 813625.3 .0000000 -7.312500 .0000000 1118490. .0000000 -10.62500 .0000000 1508607. .0000000 -10.87500 .0000000 2000728. .0000000 -6.375000 .0000000 2613668. .0000000 -8.500000 .0000000 3368428. .0000000 -7.000000 .0000000 4288312. .0000000 -6.000000 .0000000 5399049. .0000000 -5.500000 .0000000 6728910. .0000000 -6.000000 .0000000 8308830. .0000000 -4.500000 .0000000 0.1017253E+08 .0000000 -5.000000 .0000000 0.1235663E+08 .0000000 -3.000000 .0000000 0.1490079E+08 .0000000 -2.000000 .0000000 0.1784779E+08 .0000000 -2.000000 .0000000 0.2124369E+08 .0000000 -2.000000 .0000000 0.2513793E+08 .0000000 .0000000 .0000000 0.2958346E+08 .0000000 -2.000000 .0000000 0.3463683E+08 .0000000 .0000000 .0000000 0.4035837E+08 .0000000 .0000000 .0000000 0.4681225E+08 .0000000 4.000000 .0000000 0.5406663E+08 .0000000 4.000000 .0000000 0.6219378E+08 .0000000 .0000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137666E+08 .0000000 -8.000000 .0000000 0.9259852E+08 .0000000 .0000000 .0000000 0.1050256E+09 .0000000 .0000000 .0000000 0.1187526E+09 .0000000 .0000000 .0000000 0.1338788E+09 .0000000 8.000000 .0000000 0.1505087E+09 .0000000 .0000000 .0000000 0.1687515E+09 .0000000 16.00000 .0000000 0.1887219E+09 .0000000 16.00000 .0000000 0.2105399E+09 .0000000 .0000000 .0000000 0.2343308E+09 .0000000 .0000000 .0000000 0.2602253E+09 .0000000 .0000000 .0000000 0.2883602E+09 .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 10.15884 1ill-conditioned problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = 14.00000 par( 2) = -.5000000 par( 3) = 1.078125 par( 4) = .9995117 par( 5) = .9999466 par( 6) = 1.000001 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value 14.00000 -.5000000 1.078125 .9995117 .9999466 1.000001 pv sdpv res sdres 14.00000 .0000000 -14.57090 .0000000 17.57758 .0000000 -13.60714 .0000000 73.30777 .0000000 -10.65509 .0000000 373.1858 .0000000 -8.350098 .0000000 1373.206 .0000000 -10.59314 .0000000 3913.361 .0000000 -7.209717 .0000000 9337.645 .0000000 -7.695312 .0000000 19614.05 .0000000 -5.314453 .0000000 37454.56 .0000000 -6.125000 .0000000 66435.17 .0000000 -5.257812 .0000000 111115.9 .0000000 -3.257812 .0000000 177160.7 .0000000 -5.484375 .0000000 271457.5 .0000000 -4.656250 .0000000 402238.4 .0000000 -4.406250 .0000000 579199.4 .0000000 -5.937500 .0000000 813620.4 .0000000 -2.375000 .0000000 1118485. .0000000 -5.875000 .0000000 1508602. .0000000 -6.375000 .0000000 2000724. .0000000 -2.375000 .0000000 2613664. .0000000 -4.750000 .0000000 3368426. .0000000 -4.000000 .0000000 4288310. .0000000 -4.000000 .0000000 5399048. .0000000 -4.000000 .0000000 6728908. .0000000 -5.000000 .0000000 8308829. .0000000 -4.000000 .0000000 0.1017253E+08 .0000000 -6.000000 .0000000 0.1235664E+08 .0000000 -5.000000 .0000000 0.1490079E+08 .0000000 -3.000000 .0000000 0.1784779E+08 .0000000 -4.000000 .0000000 0.2124369E+08 .0000000 -4.000000 .0000000 0.2513793E+08 .0000000 .0000000 .0000000 0.2958346E+08 .0000000 -4.000000 .0000000 0.3463683E+08 .0000000 .0000000 .0000000 0.4035837E+08 .0000000 .0000000 .0000000 0.4681225E+08 .0000000 4.000000 .0000000 0.5406664E+08 .0000000 .0000000 .0000000 0.6219378E+08 .0000000 .0000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137666E+08 .0000000 .0000000 .0000000 0.9259851E+08 .0000000 8.000000 .0000000 0.1050256E+09 .0000000 .0000000 .0000000 0.1187526E+09 .0000000 8.000000 .0000000 0.1338788E+09 .0000000 .0000000 .0000000 0.1505087E+09 .0000000 .0000000 .0000000 0.1687515E+09 .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 6.280981 1ill-conditioned problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = -2.000000 par( 2) = -.5000000 par( 3) = 1.250000 par( 4) = .9882812 par( 5) = 1.000237 par( 6) = .9999981 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value -2.000000 -.5000000 1.250000 .9882812 1.000237 .9999981 pv sdpv res sdres -2.000000 .0000000 1.429096 .0000000 1.738516 .0000000 2.231931 .0000000 57.90997 .0000000 4.742702 .0000000 358.4523 .0000000 6.383392 .0000000 1359.309 .0000000 3.304199 .0000000 3900.427 .0000000 5.724365 .0000000 9325.761 .0000000 4.188477 .0000000 19603.27 .0000000 5.464844 .0000000 37444.91 .0000000 3.527344 .0000000 66426.65 .0000000 3.265625 .0000000 111108.5 .0000000 4.156250 .0000000 177154.3 .0000000 .8750000 .0000000 271452.2 .0000000 .6562500 .0000000 402234.0 .0000000 -0.3125000E-01 .0000000 579195.9 .0000000 -2.500000 .0000000 813617.8 .0000000 .2500000 .0000000 1118484. .0000000 -4.000000 .0000000 1508601. .0000000 -5.125000 .0000000 2000723. .0000000 -1.750000 .0000000 2613664. .0000000 -4.750000 .0000000 3368426. .0000000 -4.500000 .0000000 4288312. .0000000 -5.000000 .0000000 5399048. .0000000 -5.000000 .0000000 6728910. .0000000 -6.500000 .0000000 8308832. .0000000 -6.500000 .0000000 0.1017253E+08 .0000000 -8.000000 .0000000 0.1235664E+08 .0000000 -7.000000 .0000000 0.1490080E+08 .0000000 -7.000000 .0000000 0.1784780E+08 .0000000 -8.000000 .0000000 0.2124369E+08 .0000000 -6.000000 .0000000 0.2513794E+08 .0000000 -6.000000 .0000000 0.2958346E+08 .0000000 -10.00000 .0000000 0.3463684E+08 .0000000 -8.000000 .0000000 0.4035838E+08 .0000000 -4.000000 .0000000 0.4681226E+08 .0000000 .0000000 .0000000 0.5406664E+08 .0000000 -4.000000 .0000000 0.6219378E+08 .0000000 -4.000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137666E+08 .0000000 .0000000 .0000000 0.9259851E+08 .0000000 8.000000 .0000000 0.1050256E+09 .0000000 .0000000 .0000000 0.1187526E+09 .0000000 8.000000 .0000000 0.1338788E+09 .0000000 8.000000 .0000000 0.1505086E+09 .0000000 16.00000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 6.185488 1ill-conditioned problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = .0000000 par( 2) = 1.000000 par( 3) = 1.093750 par( 4) = .9912109 par( 5) = 1.000282 par( 6) = .9999969 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value .0000000 1.000000 1.093750 .9912109 1.000282 .9999969 pv sdpv res sdres .0000000 .0000000 -.5709035 .0000000 5.085240 .0000000 -1.114794 .0000000 62.30910 .0000000 .3435707 .0000000 363.6286 .0000000 1.207123 .0000000 1365.007 .0000000 -2.393799 .0000000 3906.412 .0000000 -.2602539 .0000000 9331.818 .0000000 -1.869141 .0000000 19609.20 .0000000 -.4707031 .0000000 37450.55 .0000000 -2.121094 .0000000 66431.85 .0000000 -1.937500 .0000000 111113.1 .0000000 -.4765625 .0000000 177158.3 .0000000 -3.109375 .0000000 271455.4 .0000000 -2.562500 .0000000 402236.4 .0000000 -2.437500 .0000000 579197.4 .0000000 -4.000000 .0000000 813618.4 .0000000 -.3750000 .0000000 1118483. .0000000 -3.750000 .0000000 1508600. .0000000 -4.125000 .0000000 2000721. .0000000 .2500000 .0000000 2613662. .0000000 -1.750000 .0000000 3368422. .0000000 -.7500000 .0000000 4288307. .0000000 -.5000000 .0000000 5399044. .0000000 -.5000000 .0000000 6728904. .0000000 -1.000000 .0000000 8308826. .0000000 -.5000000 .0000000 0.1017253E+08 .0000000 -3.000000 .0000000 0.1235663E+08 .0000000 -1.000000 .0000000 0.1490079E+08 .0000000 .0000000 .0000000 0.1784779E+08 .0000000 .0000000 .0000000 0.2124369E+08 .0000000 .0000000 .0000000 0.2513793E+08 .0000000 2.000000 .0000000 0.2958346E+08 .0000000 -2.000000 .0000000 0.3463683E+08 .0000000 .0000000 .0000000 0.4035837E+08 .0000000 .0000000 .0000000 0.4681226E+08 .0000000 .0000000 .0000000 0.5406663E+08 .0000000 4.000000 .0000000 0.6219378E+08 .0000000 4.000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137665E+08 .0000000 8.000000 .0000000 0.9259851E+08 .0000000 8.000000 .0000000 0.1050256E+09 .0000000 8.000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 3.388929 1ill-conditioned problem call to llss starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** the least squares fit of the data to the model is exact to within machine precision. statistical analysis is not possible. the values computed for the parameters are - par( 1) = -2.000000 par( 2) = 3.125000 par( 3) = .7812500 par( 4) = 1.008301 par( 5) = .9998703 par( 6) = 1.000001 ierr = 0 returned results from least squares fit --------------------------------------- parameters from fit index 1 2 3 4 5 6 value -2.000000 3.125000 .7812500 1.008301 .9998703 1.000001 pv sdpv res sdres -2.000000 .0000000 1.429096 .0000000 4.914422 .0000000 -.9439747 .0000000 63.43935 .0000000 -.7866745 .0000000 365.6200 .0000000 -.7843018 .0000000 1367.499 .0000000 -4.885864 .0000000 3909.115 .0000000 -2.963135 .0000000 9334.504 .0000000 -4.554688 .0000000 19611.70 .0000000 -2.968750 .0000000 37452.74 .0000000 -4.304688 .0000000 66433.64 .0000000 -3.726562 .0000000 111114.4 .0000000 -1.820312 .0000000 177159.2 .0000000 -3.984375 .0000000 271455.8 .0000000 -2.937500 .0000000 402236.4 .0000000 -2.406250 .0000000 579197.0 .0000000 -3.562500 .0000000 813617.6 .0000000 .4375000 .0000000 1118482. .0000000 -2.625000 .0000000 1508599. .0000000 -2.750000 .0000000 2000719. .0000000 1.875000 .0000000 2613660. .0000000 -.2500000 .0000000 3368421. .0000000 .7500000 .0000000 4288306. .0000000 1.000000 .0000000 5399042. .0000000 1.500000 .0000000 6728903. .0000000 .5000000 .0000000 8308823. .0000000 2.000000 .0000000 0.1017252E+08 .0000000 .0000000 .0000000 0.1235663E+08 .0000000 1.000000 .0000000 0.1490079E+08 .0000000 2.000000 .0000000 0.1784779E+08 .0000000 2.000000 .0000000 0.2124369E+08 .0000000 2.000000 .0000000 0.2513793E+08 .0000000 4.000000 .0000000 0.2958346E+08 .0000000 -2.000000 .0000000 0.3463683E+08 .0000000 .0000000 .0000000 0.4035837E+08 .0000000 .0000000 .0000000 0.4681225E+08 .0000000 4.000000 .0000000 0.5406663E+08 .0000000 4.000000 .0000000 0.6219378E+08 .0000000 4.000000 .0000000 0.7127018E+08 .0000000 8.000000 .0000000 0.8137666E+08 .0000000 .0000000 .0000000 0.9259852E+08 .0000000 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 2 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 3 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 4 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 5 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 6 .00000000 .00000000 .00000000 .00000000 .00000000 .00000000 rsd = 3.050457 nonlinear least squares estimation subroutine test number 1 normal problem test of nls starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for observations failing step size selection criteria approximating * parameter starting value scale derivative count notes row number index fixed (par) (scale) (stp) f c 1 no .72500002 default 0.10000015E-01 0 2 no 4.0000000 default 0.12264252E-02 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 1 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3381E-01 0.4572E-02 .6894 .7111 y 0.1787E-01 y current parameter values index 1 2 value .7679511 3.859573 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3285E-01 0.4317E-02 0.5565E-01 0.5565E-01 y 0.3357E-03 y current parameter values index 1 2 value .7688670 3.860392 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741233 0.22074621E-01 -0.36123276E-01 -1.48 2 1.4710000 3.4210000 3.4111581 0.16468925E-01 0.98419189E-02 0.35 3 1.4900000 3.5969999 3.5844142 0.15617380E-01 0.12585640E-01 0.44 4 1.5650001 4.3400002 4.3326435 0.14066310E-01 0.73566437E-02 0.25 5 1.6109999 4.8820000 4.8453059 0.16511230E-01 0.36694050E-01 1.29 6 1.6799999 5.6599998 5.6968317 0.26187044E-01 -0.36831856E-01 -1.86 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 0.3335921E-03 -0.9340724E-03 2 -.9907541 0.2664487E-02 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76886702 0.18264502E-01 42.10 .71815658 .81957746 2 no 3.8603923 0.51618669E-01 74.79 3.7170758 4.0037088 residual sum of squares 0.4317312E-02 residual standard deviation 0.3285313E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.83128 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688670 -0.3612328E-01 -1.000000 -1.000000 -1.000000 2 3.860392 0.9841919E-02 -1.000000 -1.000000 -1.000000 3 0.1258564E-01 -1.000000 -1.000000 -1.000000 4 0.7356644E-02 -1.000000 -1.000000 -1.000000 5 0.3669405E-01 -1.000000 -1.000000 -1.000000 6 -0.3683186E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 2 normal problem test of nlsc input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for approximating parameter starting value scale derivative index fixed (par) (scale) (stp) 1 no .72500002 1.0000000 0.34526698E-03 2 no 4.0000000 1.0000000 0.34526698E-03 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3387E-01 0.4589E-02 .6883 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679835 3.859367 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 9 0.3285E-01 0.4317E-02 -0.2912E-05 0.5388E-07 y 0.3520E-05 y current parameter values index 1 2 value .7688621 3.860408 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 -1.000000 -1.000000 -1.000000 2 3.860408 0.9843349E-02 -1.000000 -1.000000 -1.000000 3 0.1258683E-01 -1.000000 -1.000000 -1.000000 4 0.7354259E-02 -1.000000 -1.000000 -1.000000 5 0.3668928E-01 -1.000000 -1.000000 -1.000000 6 -0.3684092E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 3 normal problem test of nlss input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for approximating parameter starting value scale derivative index fixed (par) (scale) (stp) 1 no .72500002 1.0000000 0.34526698E-03 2 no 4.0000000 1.0000000 0.34526698E-03 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3387E-01 0.4589E-02 .6883 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679835 3.859367 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 9 0.3285E-01 0.4317E-02 -0.2912E-05 0.5388E-07 y 0.3520E-05 y current parameter values index 1 2 value .7688621 3.860408 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 2.174119 0.3402823E+39 0.3402823E+39 2 3.860408 0.9843349E-02 3.411157 0.3402823E+39 0.3402823E+39 3 0.1258683E-01 3.584413 0.3402823E+39 0.3402823E+39 4 0.7354259E-02 4.332646 0.3402823E+39 0.3402823E+39 5 0.3668928E-01 4.845311 0.3402823E+39 0.3402823E+39 6 -0.3684092E-01 5.696841 0.3402823E+39 0.3402823E+39 variance covariance matrix column 1 2 3 4 5 6 1 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285311E-01 nnzw = -1 npare = 2 nonlinear least squares estimation subroutine test number 4 normal problem test of nlsw starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for observations failing step size selection criteria approximating * parameter starting value scale derivative count notes row number index fixed (par) (scale) (stp) f c 1 no .72500002 default 0.10000015E-01 0 2 no 4.0000000 default 0.12264252E-02 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 1 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3381E-01 0.4572E-02 .6894 .7111 y 0.1787E-01 y current parameter values index 1 2 value .7679511 3.859573 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3285E-01 0.4317E-02 0.5565E-01 0.5565E-01 y 0.3357E-03 y current parameter values index 1 2 value .7688670 3.860392 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741233 0.22074621E-01 -0.36123276E-01 -1.48 0.100E+01 2 1.4710000 3.4210000 3.4111581 0.16468925E-01 0.98419189E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844142 0.15617380E-01 0.12585640E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326435 0.14066310E-01 0.73566437E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453059 0.16511230E-01 0.36694050E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968317 0.26187044E-01 -0.36831856E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 0.3335921E-03 -0.9340724E-03 2 -.9907541 0.2664487E-02 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76886702 0.18264502E-01 42.10 .71815658 .81957746 2 no 3.8603923 0.51618669E-01 74.79 3.7170758 4.0037088 residual sum of squares 0.4317312E-02 residual standard deviation 0.3285313E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.83128 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688670 -0.3612328E-01 -1.000000 -1.000000 -1.000000 2 3.860392 0.9841919E-02 -1.000000 -1.000000 -1.000000 3 0.1258564E-01 -1.000000 -1.000000 -1.000000 4 0.7356644E-02 -1.000000 -1.000000 -1.000000 5 0.3669405E-01 -1.000000 -1.000000 -1.000000 6 -0.3683186E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 5 normal problem test of nlswc input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for approximating parameter starting value scale derivative index fixed (par) (scale) (stp) 1 no .72500002 1.0000000 0.34526698E-03 2 no 4.0000000 1.0000000 0.34526698E-03 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3387E-01 0.4589E-02 .6883 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679835 3.859367 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 9 0.3285E-01 0.4317E-02 -0.2912E-05 0.5388E-07 y 0.3520E-05 y current parameter values index 1 2 value .7688621 3.860408 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 0.100E+01 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 0.100E+01 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 0.100E+01 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 0.100E+01 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 0.100E+01 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * 0.100E+01 * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 -1.000000 -1.000000 -1.000000 2 3.860408 0.9843349E-02 -1.000000 -1.000000 -1.000000 3 0.1258683E-01 -1.000000 -1.000000 -1.000000 4 0.7354259E-02 -1.000000 -1.000000 -1.000000 5 0.3668928E-01 -1.000000 -1.000000 -1.000000 6 -0.3684092E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 6 normal problem test of nlsws input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for approximating parameter starting value scale derivative index fixed (par) (scale) (stp) 1 no .72500002 1.0000000 0.34526698E-03 2 no 4.0000000 1.0000000 0.34526698E-03 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3387E-01 0.4589E-02 .6883 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679835 3.859367 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 9 0.3285E-01 0.4317E-02 -0.2912E-05 0.5388E-07 y 0.3520E-05 y current parameter values index 1 2 value .7688621 3.860408 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 0.100E+01 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 0.100E+01 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 0.100E+01 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 0.100E+01 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 0.100E+01 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * 0.100E+01 * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 2.174119 0.3402823E+39 0.3402823E+39 2 3.860408 0.9843349E-02 3.411157 0.3402823E+39 0.3402823E+39 3 0.1258683E-01 3.584413 0.3402823E+39 0.3402823E+39 4 0.7354259E-02 4.332646 0.3402823E+39 0.3402823E+39 5 0.3668928E-01 4.845311 0.3402823E+39 0.3402823E+39 6 -0.3684092E-01 5.696841 0.3402823E+39 0.3402823E+39 variance covariance matrix column 1 2 3 4 5 6 1 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285311E-01 nnzw = 6 npare = 2 nonlinear least squares estimation subroutine test number 7 normal problem test of nlsd starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ derivative parameter starting value scale assessment index fixed (par) (scale) 1 no .72500002 default ok 2 no 4.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value 1.309000 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.1790E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3285E-01 0.4317E-02 0.6087E-01 0.6086E-01 y 0.3206E-03 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741149 0.22079054E-01 -0.36114931E-01 -1.48 2 1.4710000 3.4210000 3.4111543 0.16469598E-01 0.98457336E-02 0.35 3 1.4900000 3.5969999 3.5844114 0.15615326E-01 0.12588501E-01 0.44 4 1.5650001 4.3400002 4.3326454 0.14065785E-01 0.73547363E-02 0.25 5 1.6109999 4.8820000 4.8453116 0.16512049E-01 0.36688328E-01 1.29 6 1.6799999 5.6599998 5.6968441 0.26183691E-01 -0.36844254E-01 -1.86 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 0.3342289E-03 -0.9369369E-03 2 -.9907720 0.2675650E-02 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18281927E-01 42.06 .71810019 .81961775 2 no 3.8604167 0.51726684E-01 74.63 3.7168002 4.0040331 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 -1.000000 -1.000000 -1.000000 2 3.860417 0.9845734E-02 -1.000000 -1.000000 -1.000000 3 0.1258850E-01 -1.000000 -1.000000 -1.000000 4 0.7354736E-02 -1.000000 -1.000000 -1.000000 5 0.3668833E-01 -1.000000 -1.000000 -1.000000 6 -0.3684425E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 8 normal problem test of nlsdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 -1.000000 -1.000000 -1.000000 2 3.860417 0.9845734E-02 -1.000000 -1.000000 -1.000000 3 0.1258850E-01 -1.000000 -1.000000 -1.000000 4 0.7354736E-02 -1.000000 -1.000000 -1.000000 5 0.3668833E-01 -1.000000 -1.000000 -1.000000 6 -0.3684425E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 9 normal problem test of nlsds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = -1 npare = 2 nonlinear least squares estimation subroutine test number 10 normal problem test of nlswd input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ derivative parameter starting value scale assessment index fixed (par) (scale) 1 no .72500002 default ok 2 no 4.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value 1.309000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.1790E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3285E-01 0.4317E-02 0.6087E-01 0.6086E-01 y 0.3206E-03 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22079054E-01 -0.36114931E-01 -1.48 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16469598E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15615326E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14065785E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16512049E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26183691E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 0.3342289E-03 -0.9369369E-03 2 -.9907720 0.2675650E-02 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18281927E-01 42.06 .71810019 .81961775 2 no 3.8604167 0.51726684E-01 74.63 3.7168002 4.0040331 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 -1.000000 -1.000000 -1.000000 2 3.860417 0.9845734E-02 -1.000000 -1.000000 -1.000000 3 0.1258850E-01 -1.000000 -1.000000 -1.000000 4 0.7354736E-02 -1.000000 -1.000000 -1.000000 5 0.3668833E-01 -1.000000 -1.000000 -1.000000 6 -0.3684425E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 11 normal problem test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 -1.000000 -1.000000 -1.000000 2 3.860417 0.9845734E-02 -1.000000 -1.000000 -1.000000 3 0.1258850E-01 -1.000000 -1.000000 -1.000000 4 0.7354736E-02 -1.000000 -1.000000 -1.000000 5 0.3668833E-01 -1.000000 -1.000000 -1.000000 6 -0.3684425E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 nonlinear least squares estimation subroutine test number 12 normal problem test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 test of nl2sol and nl2sno called directly I Initial X(i) D(i) 1 0.300000E+01 0.707E+01 2 0.100000E+01 0.507E+01 it nf f reldf preldf reldx 0 1 0.847E+02 1 2 0.678E+01 0.920E+00 0.980E+00 0.272E+00 2 3 0.120E+01 0.823E+00 0.847E+00 0.618E+00 3 4 0.523E+00 0.564E+00 0.815E+00 0.417E+00 4 5 0.400E+00 0.235E+00 0.284E+00 0.174E+00 5 6 0.390E+00 0.260E-01 0.515E-01 0.978E-01 6 7 0.387E+00 0.846E-02 0.846E-02 0.416E-01 7 8 0.387E+00 0.412E-04 0.378E-04 0.267E-02 8 9 0.387E+00 0.463E-06 0.391E-06 0.296E-03 X- and relative function convergence. function 0.386599E+00 reldx 0.295807E-03 func. evals 9 grad. evals 9 preldf 0.391251E-06 npreldf 0.391253E-06 2 extra function evaluations for covariance. 3 extra gradient evaluations for covariance. I Final X(I) D(I) G(I) 1 -0.155489E+00 0.121E+01 -0.101E-03 2 0.694560E+00 0.146E+01 -0.527E-04 Covariance = scale * H**-1 * (J' * J) * H**-1 row 1 0.3447E+00 row 2 -0.2141E+00 0.4437E+00 I Initial X(i) D(i) 1 0.300000E+01 0.707E+01 2 0.100000E+01 0.507E+01 it nf f reldf preldf reldx 0 1 0.847E+02 1 2 0.678E+01 0.920E+00 0.980E+00 0.272E+00 2 3 0.120E+01 0.823E+00 0.847E+00 0.618E+00 3 4 0.523E+00 0.564E+00 0.815E+00 0.417E+00 4 5 0.400E+00 0.235E+00 0.284E+00 0.174E+00 5 6 0.390E+00 0.261E-01 0.515E-01 0.979E-01 6 7 0.387E+00 0.846E-02 0.846E-02 0.416E-01 7 8 0.387E+00 0.420E-04 0.402E-04 0.274E-02 8 9 0.387E+00 0.154E-06 0.646E-07 0.115E-03 9 10 0.387E+00 0.154E-06 0.579E-07 0.179E-03 10 11 0.387E+00 -0.154E-06 0.143E-07 0.114E-03 X- and relative function convergence. function 0.386599E+00 reldx 0.113773E-03 func. evals 11 grad. evals 10 preldf 0.143261E-07 npreldf 0.143261E-07 5 extra function evaluations for covariance. I Final X(I) D(I) G(I) 1 -0.155456E+00 0.153E+01 0.126E-04 2 0.694497E+00 0.139E+01 0.752E-04 Covariance = scale * H**-1 * (J' * J) * H**-1 row 1 0.3378E+00 row 2 -0.2066E+00 0.4359E+00 1nonlinear least squares estimation subroutine test number 1 error handling test 1 - problem specification test of nls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nls ------------------------------------- the input value of npar is 0. the value of the argument npar must be greater than or equal to one . the input value of m is -1. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call nls (y, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlsc input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsc ------------------------------------- the input value of npar is 0. the value of the argument npar must be greater than or equal to one . the input value of m is -1. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call nlsc (y, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, + scale, delta, ivaprx, nprt) output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlss input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlss ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to npar . the input value of m is -1. the value of the argument m must be greater than or equal to one . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to npare . the correct form of the call statement is call nlss (y, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, + scale, delta, ivaprx, nprt, + npare, rsd, pv, sdpv, sdres, vcv, ivcv) output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlsw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsw ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to npar . the number of nonzero weights found is 2. the number of nonzero weights in wt must be greater than or equal to npar . the input value of m is -1. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call nlsw (y, wt, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlswc input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswc ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to npar . the number of nonzero weights found is 2. the number of nonzero weights in wt must be greater than or equal to npar . the input value of m is -1. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call nlswc (y, wt, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, scale, + delta, ivaprx, nprt) output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlsws input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsws ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to npar . the number of nonzero weights found is 2. the number of nonzero weights in wt must be greater than or equal to npar . the input value of m is -1. the value of the argument m must be greater than or equal to one . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to npare . the correct form of the call statement is call nlsws (y, wt, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, + scale, delta, ivaprx, nprt, + nnzw, npare, rsd, pv, sdpv, sdres, vcv, ivcv) output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlsd starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsd ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to npar . the input value of m is -1. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call nlsd (y, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlsdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsdc ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to npar . the input value of m is -1. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call nlsdc (y, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak, + ifixed, idrvck, mit, stopss, stopp, + scale, delta, ivaprx, nprt) output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlsds output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsds ------------------------------------- the input value of m is -1. the value of the argument m must be greater than or equal to one . the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to npare . the correct form of the call statement is call nlsds (y, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak, + ifixed, idrvck, mit, stopss, stopp, + scale, delta, ivaprx, nprt, + npare, rsd, pv, sdpv, sdres, vcv, ivcv) output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlswd starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswd ------------------------------------- the input value of m is -1. the value of the argument m must be greater than or equal to one . the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the correct form of the call statement is call nlswd (y, wt, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswdc ------------------------------------- the input value of m is -1. the value of the argument m must be greater than or equal to one . the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the correct form of the call statement is call nlswdc (y, wt, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak, + ifixed, idrvck, mit, stopss, stopp, + scale, delta, ivaprx, nprt) output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswds ------------------------------------- the input value of m is -1. the value of the argument m must be greater than or equal to one . the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the input value of ivcv is -10. the first dimension of vcv , as indicated by the argument ivcv , must be greater than or equal to npare . the correct form of the call statement is call nlswds (y, wt, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak, + ifixed, idrvck, mit, stopss, stopp, + scale, delta, ivaprx, nprt, + nnzw, npare, rsd, pv, sdpv, sdres, vcv, ivcv) output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 1nonlinear least squares estimation subroutine test number 2 error handling test 2 - weights and control values test of nlsws input - ifixed(1) = 1 , stp(1) = 1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsws ------------------------------------- the number of elements in ifixed equal to zero is 0. the number of elements equal to zero must be greater than or equal to one . the number of values in vector stp less than or equal to zero is 1. since the first value of the vector stp is greater than zero all of the values must be greater than zero . the number of values in vector scale less than or equal to zero is 1. since the first value of the vector scale is greater than zero all of the values must be greater than zero . the correct form of the call statement is call nlsws (y, wt, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak, + ifixed, stp, mit, stopss, stopp, + scale, delta, ivaprx, nprt, + nnzw, npare, rsd, pv, sdpv, sdres, vcv, ivcv) output - ifixed(1) = 1 , stp(1) = 1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlswd input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswd ------------------------------------- negative values were found in the vector wt . all weights must be greater than or equal to zero. the correct form of the call statement is call nlswd (y, wt, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak) starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswds ------------------------------------- the number of elements in ifixed equal to zero is 0. the number of elements equal to zero must be greater than or equal to one . the number of values in vector scale less than or equal to zero is 1. since the first value of the vector scale is greater than zero all of the values must be greater than zero . the correct form of the call statement is call nlswds (y, wt, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak, + ifixed, idrvck, mit, stopss, stopp, + scale, delta, ivaprx, nprt, + nnzw, npare, rsd, pv, sdpv, sdres, vcv, ivcv) output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 1 1nonlinear least squares estimation subroutine test number 3 error handling test 3 - too few positive weights test of nlsw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlsw ------------------------------------- the number of nonzero weights found is 1. the number of nonzero weights in wt must be greater than or equal to npar . the correct form of the call statement is call nlsw (y, wt, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlswds starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswds ------------------------------------- the number of nonzero weights found is 1. the number of nonzero weights in wt must be greater than or equal to npar . the correct form of the call statement is call nlswds (y, wt, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak, + ifixed, idrvck, mit, stopss, stopp, + scale, delta, ivaprx, nprt, + nnzw, npare, rsd, pv, sdpv, sdres, vcv, ivcv) returned results (-1 indicates value not changed by called subroutine) ierr is 1 1nonlinear least squares estimation subroutine test number 4 error handling test 4 - definite error in derivative test of nlsd starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ derivative parameter starting value scale assessment index fixed (par) (scale) 1 no .72500002 default incorrect 2 no 4.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value 1.309000 number of observations (n) 6 the correct form of the call statement is call nlsd (y, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 1nonlinear least squares estimation subroutine test number 5 error handling test 5 - possible error in derivative test of nlsdc input - ifixed(1) = -1 , idrvck = 1 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 10000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ * derivative parameter starting value scale assessment index fixed (par) (scale) 1 no .00000000 1.0000000 ok 2 no 4.0000000 1.0000000 questionable (1) * numbers in parentheses refer to the following notes. (1) user-supplied and approximated derivatives agree, but both are zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value 1.309000 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 103.9 residual standard deviation for input parameter values (rsd) 5.097 output - ifixed(1) = -1 , idrvck = 1 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 10000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 6 error handling test 6 - insufficient work area length test of nls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nls ------------------------------------- the input value of ldstak is 392. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 393. the correct form of the call statement is call nls (y, xm, n, m, ixm, nlsmdl, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 test of nlswd starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nlswd ------------------------------------- the input value of ldstak is 128. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 129. the correct form of the call statement is call nlswd (y, wt, xm, n, m, ixm, nlsmdl, nlsdrv, + par, npar, res, ldstak) returned results (-1 indicates value not changed by called subroutine) ierr is 1 test control criteria 1nonlinear least squares estimation subroutine test number 1 maximum number of iterations = 0 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 0, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** output - ifixed(1) = -1 , idrvck = 0 , mit = 0, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 2 maximum number of iterations = 1 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 1, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 1 maximum number of model subroutine calls allowed 2 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration limit reached 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued WARNING WARNING ** error summary ** program did not converge in the number of iterations or number of model subroutine calls allowed. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76798517 2 no 3.8593092 residual sum of squares 0.4597142E-02 residual standard deviation 0.3390111E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 22.35560 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1709964 nc * -0.32996416E-01 nc * 0.100E+01 2 1.4710000 3.4210000 3.4058218 nc * 0.15178204E-01 nc * 0.100E+01 3 1.4900000 3.5969999 3.5787568 nc * 0.18243074E-01 nc * 0.100E+01 4 1.5650001 4.3400002 4.3255754 nc * 0.14424801E-01 nc * 0.100E+01 5 1.6109999 4.8820000 4.8372493 nc * 0.44750690E-01 nc * 0.100E+01 6 1.6799999 5.6599998 5.6871014 nc * -0.27101517E-01 nc * 0.100E+01 * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , idrvck = 0 , mit = 1, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 6 1nonlinear least squares estimation subroutine test number 3 maximum number of iterations = 1 test of nlsws input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 1, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for approximating parameter starting value scale derivative index fixed (par) (scale) (stp) 1 no 1.0000000 1.0000000 0.34526698E-03 2 no 2.0000000 1.0000000 0.34526698E-03 3 no 3.0000000 1.0000000 0.34526698E-03 number of observations (n) 10 number of non zero weighted observations (nnzw) 10 number of independent variables (m) 3 maximum number of iterations allowed (mit) 1 maximum number of model subroutine calls allowed 2 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.2152E+08 residual standard deviation for input parameter values (rsd) 1753. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 952.6 0.6352E+07 .7048 1.000 y .2313 n current parameter values index 1 2 3 value .9981565 1.992511 1.613707 limit on number of calls to the model subroutine reached 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued WARNING WARNING ** error summary ** program did not converge in the number of iterations or number of model subroutine calls allowed. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .99815649 2 no 1.9925114 3 no 1.6137067 residual sum of squares 6352109. residual standard deviation 952.5986 based on degrees of freedom 10 - 3 = 7 approximate condition number 4413.858 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.0000000 1.1250000 1.2500000 .00000000 6.3915024 nc * -6.3915024 nc * 0.100E+01 2 2.0000000 2.1250000 2.2500000 .00000000 24.611528 nc * -24.611528 nc * 0.100E+01 3 3.0000000 3.1250000 3.2500000 .00000000 64.616592 nc * -64.616592 nc * 0.100E+01 4 4.0000000 4.1250000 4.2500000 .00000000 136.08894 nc * -136.08894 nc * 0.100E+01 5 5.0000000 5.1250000 5.2500000 .00000000 248.71082 nc * -248.71082 nc * 0.100E+01 6 6.0000000 6.1250000 6.2500000 .00000000 412.16446 nc * -412.16446 nc * 0.100E+01 7 7.0000000 7.1250000 7.2500000 .00000000 636.13202 nc * -636.13202 nc * 0.100E+01 8 8.0000000 8.1250000 8.2500000 .00000000 930.29590 nc * -930.29590 nc * 0.100E+01 9 9.0000000 9.1250000 9.2500000 .00000000 1304.3383 nc * -1304.3383 nc * 0.100E+01 10 10.000000 10.125000 10.250000 .00000000 1767.9414 nc * -1767.9414 nc * 0.100E+01 * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 1, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 6 par res pv sdpv sdres 1 .9981565 -6.391502 6.391502 0.3402823E+39 0.3402823E+39 2 1.992511 -24.61153 24.61153 0.3402823E+39 0.3402823E+39 3 1.613707 -64.61659 64.61659 0.3402823E+39 0.3402823E+39 4 -136.0889 136.0889 0.3402823E+39 0.3402823E+39 5 -248.7108 248.7108 0.3402823E+39 0.3402823E+39 6 -412.1645 412.1645 0.3402823E+39 0.3402823E+39 7 -636.1320 636.1320 0.3402823E+39 0.3402823E+39 8 -930.2959 930.2959 0.3402823E+39 0.3402823E+39 9 -1304.338 1304.338 0.3402823E+39 0.3402823E+39 10 -1767.941 1767.941 0.3402823E+39 0.3402823E+39 variance covariance matrix column 1 2 3 4 5 6 1 0.34028235E+39 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 2 0.34028235E+39 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 3 0.34028235E+39 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 952.5986 nnzw = 10 npare = 3 1nonlinear least squares estimation subroutine test number 4 --stopss = 0.11920929E-06 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.11920929E-06, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1192E-06 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.11920929E-06, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 5 --stopss = .10000000 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = .10000000 , stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) .1000 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 5 0.3285E-01 0.4317E-02 0.6087E-01 0.6086E-01 y 0.4528E-03 y current parameter values index 1 2 value .7688590 3.860417 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.89716 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = .10000000 , stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 6 --stopss = 0.10728836E-06 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.10728836E-06, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.10728836E-06, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 7 --stopss = .11000000 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = .11000000 , stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = .11000000 , stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 8 --stopp = .00000000 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = .00000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) .0000 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = .00000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 9 --stopp = 1.0000000 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 1.0000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 1.000 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 4 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1709964 0.21322643E-01 -0.32996416E-01 -1.25 0.100E+01 2 1.4710000 3.4210000 3.4058218 0.16262228E-01 0.15178204E-01 0.51 0.100E+01 3 1.4900000 3.5969999 3.5787568 0.15529348E-01 0.18243074E-01 0.61 0.100E+01 4 1.5650001 4.3400002 4.3255754 0.14479102E-01 0.14424801E-01 0.47 0.100E+01 5 1.6109999 4.8820000 4.8372493 0.16986372E-01 0.44750690E-01 1.53 0.100E+01 6 1.6799999 5.6599998 5.6871014 0.26282089E-01 -0.27101517E-01 -1.27 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - * - - * - - - - - 0.75+ + 0.75+ + - * * * - - ** * - - - - - - - - - - - - - -0.75+ + -0.75+ + - - - - -* *- - * * - - - - - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.085 4.808 8.531 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+--***----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - ** - - - - *********** - - - - ***** - - - 6+ + 2.25+ + - - - - - - - - - - - * - - - - - 11+ + 0.75+ + - - - * * * - - - - - - - - - - - - - 16+ + -0.75+ + - - - - - - - * * - - - - - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76798517 0.17594624E-01 43.65 .71913463 .81683570 2 no 3.8593092 0.49866125E-01 77.39 3.7208586 3.9977598 residual sum of squares 0.4597142E-02 residual standard deviation 0.3390111E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 22.35560 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 1.0000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 10 --stopp = -1.0000000 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = -1.0000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = -1.0000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 11 --stopp = 1.1000000 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 1.1000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 1.1000000 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 0 1nonlinear least squares estimation subroutine test number 12 --nprt = 100000 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 100000 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 100000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 13 --nprt = 10000 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 10000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 10000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 14 --nprt = 1000 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 1000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 1000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 15 --nprt = 100 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 100 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 100 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 16 --nprt = 10 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 10 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 10 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 17 --nprt = 1 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 1 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 18 --nprt = 11000 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 19 --nprt = 11001 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11001 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 11001 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 20 --nprt = 0 test of nlsc input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 -1.000000 -1.000000 -1.000000 2 3.860408 0.9843349E-02 -1.000000 -1.000000 -1.000000 3 0.1258683E-01 -1.000000 -1.000000 -1.000000 4 0.7354259E-02 -1.000000 -1.000000 -1.000000 5 0.3668928E-01 -1.000000 -1.000000 -1.000000 6 -0.3684092E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 1nonlinear least squares estimation subroutine test number 21 --nprt = 0 test of nlss input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 2.174119 0.3402823E+39 0.3402823E+39 2 3.860408 0.9843349E-02 3.411157 0.3402823E+39 0.3402823E+39 3 0.1258683E-01 3.584413 0.3402823E+39 0.3402823E+39 4 0.7354259E-02 4.332646 0.3402823E+39 0.3402823E+39 5 0.3668928E-01 4.845311 0.3402823E+39 0.3402823E+39 6 -0.3684092E-01 5.696841 0.3402823E+39 0.3402823E+39 variance covariance matrix column 1 2 3 4 5 6 1 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285311E-01 nnzw = -1 npare = 2 1nonlinear least squares estimation subroutine test number 22 --nprt = 0 test of nlswc input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 0.100E+01 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 0.100E+01 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 0.100E+01 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 0.100E+01 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 0.100E+01 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * 0.100E+01 * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 -1.000000 -1.000000 -1.000000 2 3.860408 0.9843349E-02 -1.000000 -1.000000 -1.000000 3 0.1258683E-01 -1.000000 -1.000000 -1.000000 4 0.7354259E-02 -1.000000 -1.000000 -1.000000 5 0.3668928E-01 -1.000000 -1.000000 -1.000000 6 -0.3684092E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 1nonlinear least squares estimation subroutine test number 23 --nprt = 0 test of nlsws input - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76886213 2 no 3.8604076 residual sum of squares 0.4317308E-02 residual standard deviation 0.3285311E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.85537 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741185 nc * -0.36118507E-01 nc * 0.100E+01 2 1.4710000 3.4210000 3.4111567 nc * 0.98433495E-02 nc * 0.100E+01 3 1.4900000 3.5969999 3.5844131 nc * 0.12586832E-01 nc * 0.100E+01 4 1.5650001 4.3400002 4.3326459 nc * 0.73542595E-02 nc * 0.100E+01 5 1.6109999 4.8820000 4.8453107 nc * 0.36689281E-01 nc * 0.100E+01 6 1.6799999 5.6599998 5.6968408 nc * -0.36840916E-01 nc * 0.100E+01 * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = 0.34526698E-03, mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688621 -0.3611851E-01 2.174119 0.3402823E+39 0.3402823E+39 2 3.860408 0.9843349E-02 3.411157 0.3402823E+39 0.3402823E+39 3 0.1258683E-01 3.584413 0.3402823E+39 0.3402823E+39 4 0.7354259E-02 4.332646 0.3402823E+39 0.3402823E+39 5 0.3668928E-01 4.845311 0.3402823E+39 0.3402823E+39 6 -0.3684092E-01 5.696841 0.3402823E+39 0.3402823E+39 variance covariance matrix column 1 2 3 4 5 6 1 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285311E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 24 --nprt = 0 test of nlsdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 -1.000000 -1.000000 -1.000000 2 3.860417 0.9845734E-02 -1.000000 -1.000000 -1.000000 3 0.1258850E-01 -1.000000 -1.000000 -1.000000 4 0.7354736E-02 -1.000000 -1.000000 -1.000000 5 0.3668833E-01 -1.000000 -1.000000 -1.000000 6 -0.3684425E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 1nonlinear least squares estimation subroutine test number 25 --nprt = 0 test of nlsds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = -1 npare = 2 1nonlinear least squares estimation subroutine test number 26 --nprt = 0 test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 -1.000000 -1.000000 -1.000000 2 3.860417 0.9845734E-02 -1.000000 -1.000000 -1.000000 3 0.1258850E-01 -1.000000 -1.000000 -1.000000 4 0.7354736E-02 -1.000000 -1.000000 -1.000000 5 0.3668833E-01 -1.000000 -1.000000 -1.000000 6 -0.3684425E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 1nonlinear least squares estimation subroutine test number 27 --nprt = 0 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 1nonlinear least squares estimation subroutine test number 28 --nprt = -1 test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = -1 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .72500002 1.0000000 2 no 4.0000000 1.0000000 number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) .5000 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.2188E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 6 0.3285E-01 0.4317E-02 -0.3883E-05 0.2270E-07 y 0.1637E-05 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1741149 0.22429520E-01 -0.36114931E-01 -1.50 0.100E+01 2 1.4710000 3.4210000 3.4111543 0.16685290E-01 0.98457336E-02 0.35 0.100E+01 3 1.4900000 3.5969999 3.5844114 0.15797626E-01 0.12588501E-01 0.44 0.100E+01 4 1.5650001 4.3400002 4.3326454 0.14086458E-01 0.73547363E-02 0.25 0.100E+01 5 1.6109999 4.8820000 4.8453116 0.16482538E-01 0.36688328E-01 1.29 0.100E+01 6 1.6799999 5.6599998 5.6968441 0.26233174E-01 -0.36844254E-01 -1.86 0.100E+01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that conditions necessary for asymptotic maximum likelihood theory might be violated - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 0.3445187E-03 -0.9649554E-03 2 -.9910501 0.2751759E-02 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18561216E-01 41.42 .71732473 .82039320 2 no 3.8604167 0.52457210E-01 73.59 3.7147720 4.0060611 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = 1.0000000 , delta = .50000000 , ivaprx = 3, nprt = -1 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688590 -0.3611493E-01 2.174115 0.2242952E-01 -1.504466 2 3.860417 0.9845734E-02 3.411154 0.1668529E-01 .3478968 3 0.1258850E-01 3.584411 0.1579763E-01 .4370150 4 0.7354736E-02 4.332645 0.1408646E-01 .2478011 5 0.3668833E-01 4.845312 0.1648254E-01 1.290966 6 -0.3684425E-01 5.696844 0.2623317E-01 -1.862934 variance covariance matrix column 1 2 3 4 5 6 1 0.34451875E-03 -.96495537E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.96495537E-03 0.27517590E-02 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.3285316E-01 nnzw = 6 npare = 2 test parameter handling 1nonlinear least squares estimation subroutine test number 29 all parameters zero test of nlsc input - ifixed(1) = -1 , stp(1) = -1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 11000 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for observations failing step size selection criteria approximating * parameter starting value scale derivative count notes row number index fixed (par) (scale) (stp) f c 1 no .00000000 default 0.99999997E-04 0 2 no .00000000 default 0.99999997E-04 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 1 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 103.9 residual standard deviation for input parameter values (rsd) 5.097 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 1.380 7.614 .9267 .9267 n 1.000 n current parameter values index 1 2 value 4.005899 .0000000 iteration number 10 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 17 0.3285E-01 0.4317E-02 -0.4530E-05 0.1020E-05 y 0.7842E-05 y current parameter values index 1 2 value .7688122 3.860534 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued WARNING WARNING ** error summary ** the variance-covariance matrix could not be computed at the solution. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 no .76881224 2 no 3.8605342 residual sum of squares 0.4317339E-02 residual standard deviation 0.3285323E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.89561 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1740515 nc * -0.36051512E-01 nc * 2 1.4710000 3.4210000 3.4111021 nc * 0.98979473E-02 nc * 3 1.4900000 3.5969999 3.5843616 nc * 0.12638330E-01 nc * 4 1.5650001 4.3400002 4.3326101 nc * 0.73900223E-02 nc * 5 1.6109999 4.8820000 4.8452888 nc * 0.36711216E-01 nc * 6 1.6799999 5.6599998 5.6968455 nc * -0.36845684E-01 nc * * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = -1 , stp(1) = -1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 7 par res pv sdpv sdres 1 .7688122 -0.3605151E-01 -1.000000 -1.000000 -1.000000 2 3.860534 0.9897947E-02 -1.000000 -1.000000 -1.000000 3 0.1263833E-01 -1.000000 -1.000000 -1.000000 4 0.7390022E-02 -1.000000 -1.000000 -1.000000 5 0.3671122E-01 -1.000000 -1.000000 -1.000000 6 -0.3684568E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 1nonlinear least squares estimation subroutine test number 30 all parameters zero test of nlswdc input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 11000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no .00000000 default 2 no .00000000 default number of observations (n) 6 number of non zero weighted observations (nnzw) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 103.9 residual standard deviation for input parameter values (rsd) 5.097 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 1.380 7.614 .9267 .9267 n 1.000 n current parameter values index 1 2 value 4.005899 .0000000 iteration number 10 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 14 0.3285E-01 0.4317E-02 0.1812E-04 0.2004E-04 y 0.2197E-04 y current parameter values index 1 2 value .7688624 3.860405 ***** parameter and residual sum of squares convergence ***** output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7688624 -0.3611779E-01 -1.000000 -1.000000 -1.000000 2 3.860405 0.9845257E-02 -1.000000 -1.000000 -1.000000 3 0.1258874E-01 -1.000000 -1.000000 -1.000000 4 0.7357597E-02 -1.000000 -1.000000 -1.000000 5 0.3669310E-01 -1.000000 -1.000000 -1.000000 6 -0.3683615E-01 -1.000000 -1.000000 -1.000000 variance covariance matrix column 1 2 3 4 5 6 1 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = -1.000000 nnzw = -1 npare = -1 1nonlinear least squares estimation subroutine test number 31 test with constant zero y test of nlsws input - ifixed(1) = -1 , stp(1) = -1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 21222 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for observations failing step size selection criteria approximating * parameter starting value scale derivative count notes row number index fixed (par) (scale) (stp) f c 1 no 1.0000000 default 0.99999905E-02 0 2 no 2.0000000 default 0.99999905E-02 0 3 no 3.0000000 default 0.99999905E-02 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 1 number of observations (n) 10 number of non zero weighted observations (nnzw) 10 number of independent variables (m) 3 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.2152E+08 residual standard deviation for input parameter values (rsd) 1753. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 4 1696. 0.2014E+08 0.6417E-01 1.000 y 0.6157E-02 n current parameter values index 1 2 3 value -1.634831 -.5812211 2.963287 iteration number 10 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 28 .0000 .0000 1.000 1.000 y 1.000 y current parameter values index 1 2 3 value 0.3573420E-21 -0.3705769E-21 0.3505824E-24 the residual sum of squares is exactly zero 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued estimates from least squares fit --------------------------------- index fixed parameter 1 no 0.35734202E-21 2 no -0.37057691E-21 3 no 0.35058241E-24 residual sum of squares .0000000 residual standard deviation .0000000 based on degrees of freedom 10 - 3 = 7 approximate condition number 12675.04 the least squares fit of the data to the model is exact to within machine precision. statistical analysis of the results is not possible. output - ifixed(1) = -1 , stp(1) = -1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 21222 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 0.3573420E-21 0.5887227E-22 -0.5887227E-22 .0000000 .0000000 2-0.3705769E-21 0.6879854E-22 -0.6879854E-22 .0000000 .0000000 3 0.3505824E-24 0.7399195E-22 -0.7399195E-22 .0000000 .0000000 4 0.7234900E-22 -0.7234900E-22 .0000000 .0000000 5 0.6176619E-22 -0.6176619E-22 .0000000 .0000000 6 0.4014004E-22 -0.4014004E-22 .0000000 .0000000 7 0.5367056E-23 -0.5367056E-23 .0000000 .0000000 8 -0.4465627E-22 0.4465627E-22 .0000000 .0000000 9 -0.1120334E-21 0.1120334E-21 .0000000 .0000000 10 -0.1988679E-21 0.1988679E-21 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 -.00000000 -.00000000 -1.0000000 -1.0000000 -1.0000000 2 -.00000000 .00000000 .00000000 -1.0000000 -1.0000000 -1.0000000 3 -.00000000 .00000000 .00000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = .0000000 nnzw = 10 npare = 3 1nonlinear least squares estimation subroutine test number 32 test with constant zero y test of nlswds input - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 21222 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 no 1.0000000 default 2 no 2.0000000 default 3 no 3.0000000 default number of observations (n) 10 number of non zero weighted observations (nnzw) 10 number of independent variables (m) 3 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.2152E+08 residual standard deviation for input parameter values (rsd) 1753. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 4 1696. 0.2014E+08 0.6406E-01 1.000 y 0.6145E-02 n current parameter values index 1 2 3 value -1.630051 -.5764010 2.963355 iteration number 12 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 24 .0000 .0000 1.000 1.003 y 1.000 y current parameter values index 1 2 3 value 0.1164373E-18 -0.1125660E-18 -0.2585828E-22 the residual sum of squares is exactly zero 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued estimates from least squares fit --------------------------------- index fixed parameter 1 no 0.11643725E-18 2 no -0.11256605E-18 3 no -0.25858280E-22 residual sum of squares .0000000 residual standard deviation .0000000 based on degrees of freedom 10 - 3 = 7 approximate condition number 12675.08 the least squares fit of the data to the model is exact to within machine precision. statistical analysis of the results is not possible. output - ifixed(1) = -1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 21222 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 0.1164373E-18 0.1025005E-19 -0.1025005E-19 .0000000 .0000000 2-0.1125660E-18 0.6622887E-20 -0.6622887E-20 .0000000 .0000000 3-0.2585828E-22 0.3344806E-20 -0.3344806E-20 .0000000 .0000000 4 0.5709618E-21 -0.5709618E-21 .0000000 .0000000 5 -0.1543497E-20 0.1543497E-20 .0000000 .0000000 6 -0.2843419E-20 0.2843419E-20 .0000000 .0000000 7 -0.3173656E-20 0.3173656E-20 .0000000 .0000000 8 -0.2379059E-20 0.2379059E-20 .0000000 .0000000 9 -0.3044752E-21 0.3044752E-21 .0000000 .0000000 10 0.3205241E-20 -0.3205241E-20 .0000000 .0000000 variance covariance matrix column 1 2 3 4 5 6 1 .00000000 -.00000000 -.00000000 -1.0000000 -1.0000000 -1.0000000 2 -.00000000 .00000000 .00000000 -1.0000000 -1.0000000 -1.0000000 3 -.00000000 .00000000 .00000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = .0000000 nnzw = 10 npare = 3 1nonlinear least squares estimation subroutine test number 33 test linear model test of nlsc input - ifixed(1) = 1 , stp(1) = -1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11212 starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for observations failing step size selection criteria approximating * parameter starting value scale derivative count notes row number index fixed (par) (scale) (stp) f c 1 yes 6.0000000 --- --- - 2 no 5.0000000 default 0.83544157E-01 6 + + 39 40 45 46 47 49 3 no 4.0000000 default 0.99999905E-02 0 4 no 3.0000000 default 0.99999905E-02 0 5 no 2.0000000 default 0.99999905E-02 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 5 number of observations (n) 50 number of independent variables (m) 5 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.2594E+15 residual standard deviation for input parameter values (rsd) 0.2375E+07 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.2375E+07 0.2594E+15 0.2173E-04 -0.2265E-04 n 0.7749E-06 n current parameter values (only unfixed parameters are listed) index 2 3 4 5 value 4.800783 3.994372 2.999864 1.999997 ***** singular convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued WARNING WARNING ** error summary ** this model and data are computationally singular. check your input for errors. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 yes 6.0000000 2 no 4.8007827 3 no 3.9943717 4 no 2.9998636 5 no 1.9999969 residual sum of squares 0.2594438E+15 residual standard deviation 2374886. based on degrees of freedom 50 - 4 = 46 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.0000000 1.0000000 1.0000000 5.0000000 19.795013 nc * -14.795013 nc * 2 2.0000000 4.0000000 8.0000000 31.000000 87.577911 nc * -56.577911 nc * 3 3.0000000 9.0000000 27.000000 121.00000 299.34778 nc * -178.34778 nc * 4 4.0000000 16.000000 64.000000 341.00000 793.10352 nc * -452.10352 nc * 5 5.0000000 25.000000 125.00000 781.00000 1754.8442 nc * -973.84424 nc * 6 6.0000000 36.000000 216.00000 1555.0000 3418.5688 nc * -1863.5688 nc * 7 7.0000000 49.000000 343.00000 2801.0000 6066.2754 nc * -3265.2754 nc * 8 8.0000000 64.000000 512.00000 4681.0000 10027.964 nc * -5346.9639 nc * 9 9.0000000 81.000000 729.00000 7381.0000 15681.631 nc * -8300.6309 nc * 10 10.000000 100.00000 1000.0000 11111.000 23453.277 nc * -12342.277 nc * 11 11.000000 121.00000 1331.0000 16105.000 33816.902 nc * -17711.902 nc * 12 12.000000 144.00000 1728.0000 22621.000 47294.500 nc * -24673.500 nc * 13 13.000000 169.00000 2197.0000 30941.000 64456.070 nc * -33515.070 nc * 14 14.000000 196.00000 2744.0000 41371.000 85919.617 nc * -44548.617 nc * 15 15.000000 225.00000 3375.0000 54241.000 112351.12 nc * -58110.125 nc * 16 16.000000 256.00000 4096.0000 69905.000 144464.61 nc * -74559.609 nc * 17 17.000000 289.00000 4913.0000 88741.000 183022.05 nc * -94281.047 nc * 18 18.000000 324.00000 5832.0000 111151.00 228833.47 nc * -117682.47 nc * 19 19.000000 361.00000 6859.0000 137561.00 282756.84 nc * -145195.84 nc * 20 20.000000 400.00000 8000.0000 168421.00 345698.19 nc * -177277.19 nc * 21 21.000000 441.00000 9261.0000 204205.00 418611.47 nc * -214406.47 nc * 22 22.000000 484.00000 10648.000 245411.00 502498.72 nc * -257087.72 nc * 23 23.000000 529.00000 12167.000 292561.00 598409.88 nc * -305848.88 nc * 24 24.000000 576.00000 13824.000 346201.00 707443.06 nc * -361242.06 nc * 25 25.000000 625.00000 15625.000 406901.00 830744.19 nc * -423843.19 nc * 26 26.000000 676.00000 17576.000 475255.00 969507.19 nc * -494252.19 nc * 27 27.000000 729.00000 19683.000 551881.00 1124974.2 nc * -573093.25 nc * 28 28.000000 784.00000 21952.000 637421.00 1298435.1 nc * -661014.12 nc * 29 29.000000 841.00000 24389.000 732541.00 1491227.9 nc * -758686.88 nc * 30 30.000000 900.00000 27000.000 837931.00 1704738.8 nc * -866807.75 nc * 31 31.000000 961.00000 29791.000 954305.00 1940401.5 nc * -986096.50 nc * 32 32.000000 1024.0000 32768.000 1082401.0 2199698.2 nc * -1117297.2 nc * 33 33.000000 1089.0000 35937.000 1222981.0 2484158.8 nc * -1261177.8 nc * 34 34.000000 1156.0000 39304.000 1376831.0 2795361.0 nc * -1418530.0 nc * 35 35.000000 1225.0000 42875.000 1544761.0 3134931.5 nc * -1590170.5 nc * 36 36.000000 1296.0000 46656.000 1727605.0 3504544.0 nc * -1776939.0 nc * 37 37.000000 1369.0000 50653.000 1926221.0 3905920.2 nc * -1979699.2 nc * 38 38.000000 1444.0000 54872.000 2141491.0 4340830.5 nc * -2199339.5 nc * 39 39.000000 1521.0000 59319.000 2374321.0 4811092.5 nc * -2436771.5 nc * 40 40.000000 1600.0000 64000.000 2625641.0 5318572.5 nc * -2692931.5 nc * 41 41.000000 1681.0000 68921.000 2896405.0 5865184.0 nc * -2968779.0 nc * 42 42.000000 1764.0000 74088.000 3187591.0 6452890.0 nc * -3265299.0 nc * 43 43.000000 1849.0000 79507.000 3500201.0 7083699.5 nc * -3583498.5 nc * 44 44.000000 1936.0000 85184.000 3835261.0 7759671.0 nc * -3924410.0 nc * 45 45.000000 2025.0000 91125.000 4193821.0 8482911.0 nc * -4289090.0 nc * 46 46.000000 2116.0000 97336.000 4576955.0 9255572.0 nc * -4678617.0 nc * 47 47.000000 2209.0000 103823.00 4985761.0 10079857. nc * -5094096.0 nc * 48 48.000000 2304.0000 110592.00 5421361.0 10958016. nc * -5536655.0 nc * 49 49.000000 2401.0000 117649.00 5884901.0 11892347. nc * -6007446.0 nc * 50 50.000000 2500.0000 125000.00 6377551.0 12885196. nc * -6507645.0 nc * * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = 1 , stp(1) = -1.0000000 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11212 returned results (-1 indicates value not changed by called subroutine) ierr is 3 1nonlinear least squares estimation subroutine test number 34 test linear model test of nlsdc input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11111 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 yes 6.0000000 --- 2 no 5.0000000 default 3 no 4.0000000 default 4 no 3.0000000 default 5 no 2.0000000 default number of observations (n) 50 number of independent variables (m) 5 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.2594E+15 residual standard deviation for input parameter values (rsd) 0.2375E+07 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.2375E+07 0.2594E+15 0.2173E-04 -0.2266E-04 n 0.7749E-06 n current parameter values (only unfixed parameters are listed) index 2 3 4 5 value 4.800792 3.994372 2.999864 1.999997 ***** singular convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued WARNING WARNING ** error summary ** this model and data are computationally singular. check your input for errors. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 yes 6.0000000 2 no 4.8007917 3 no 3.9943724 4 no 2.9998636 5 no 1.9999969 residual sum of squares 0.2594438E+15 residual standard deviation 2374886. based on degrees of freedom 50 - 4 = 46 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.0000000 1.0000000 1.0000000 5.0000000 19.795025 nc * -14.795025 nc * 2 2.0000000 4.0000000 8.0000000 31.000000 87.577927 nc * -56.577927 nc * 3 3.0000000 9.0000000 27.000000 121.00000 299.34778 nc * -178.34778 nc * 4 4.0000000 16.000000 64.000000 341.00000 793.10364 nc * -452.10364 nc * 5 5.0000000 25.000000 125.00000 781.00000 1754.8442 nc * -973.84424 nc * 6 6.0000000 36.000000 216.00000 1555.0000 3418.5688 nc * -1863.5688 nc * 7 7.0000000 49.000000 343.00000 2801.0000 6066.2759 nc * -3265.2759 nc * 8 8.0000000 64.000000 512.00000 4681.0000 10027.964 nc * -5346.9639 nc * 9 9.0000000 81.000000 729.00000 7381.0000 15681.631 nc * -8300.6309 nc * 10 10.000000 100.00000 1000.0000 11111.000 23453.277 nc * -12342.277 nc * 11 11.000000 121.00000 1331.0000 16105.000 33816.902 nc * -17711.902 nc * 12 12.000000 144.00000 1728.0000 22621.000 47294.500 nc * -24673.500 nc * 13 13.000000 169.00000 2197.0000 30941.000 64456.070 nc * -33515.070 nc * 14 14.000000 196.00000 2744.0000 41371.000 85919.617 nc * -44548.617 nc * 15 15.000000 225.00000 3375.0000 54241.000 112351.13 nc * -58110.133 nc * 16 16.000000 256.00000 4096.0000 69905.000 144464.61 nc * -74559.609 nc * 17 17.000000 289.00000 4913.0000 88741.000 183022.05 nc * -94281.047 nc * 18 18.000000 324.00000 5832.0000 111151.00 228833.47 nc * -117682.47 nc * 19 19.000000 361.00000 6859.0000 137561.00 282756.84 nc * -145195.84 nc * 20 20.000000 400.00000 8000.0000 168421.00 345698.19 nc * -177277.19 nc * 21 21.000000 441.00000 9261.0000 204205.00 418611.47 nc * -214406.47 nc * 22 22.000000 484.00000 10648.000 245411.00 502498.72 nc * -257087.72 nc * 23 23.000000 529.00000 12167.000 292561.00 598409.88 nc * -305848.88 nc * 24 24.000000 576.00000 13824.000 346201.00 707443.06 nc * -361242.06 nc * 25 25.000000 625.00000 15625.000 406901.00 830744.19 nc * -423843.19 nc * 26 26.000000 676.00000 17576.000 475255.00 969507.19 nc * -494252.19 nc * 27 27.000000 729.00000 19683.000 551881.00 1124974.2 nc * -573093.25 nc * 28 28.000000 784.00000 21952.000 637421.00 1298435.1 nc * -661014.12 nc * 29 29.000000 841.00000 24389.000 732541.00 1491227.9 nc * -758686.88 nc * 30 30.000000 900.00000 27000.000 837931.00 1704738.8 nc * -866807.75 nc * 31 31.000000 961.00000 29791.000 954305.00 1940401.5 nc * -986096.50 nc * 32 32.000000 1024.0000 32768.000 1082401.0 2199698.2 nc * -1117297.2 nc * 33 33.000000 1089.0000 35937.000 1222981.0 2484158.8 nc * -1261177.8 nc * 34 34.000000 1156.0000 39304.000 1376831.0 2795361.0 nc * -1418530.0 nc * 35 35.000000 1225.0000 42875.000 1544761.0 3134931.5 nc * -1590170.5 nc * 36 36.000000 1296.0000 46656.000 1727605.0 3504544.0 nc * -1776939.0 nc * 37 37.000000 1369.0000 50653.000 1926221.0 3905920.2 nc * -1979699.2 nc * 38 38.000000 1444.0000 54872.000 2141491.0 4340830.5 nc * -2199339.5 nc * 39 39.000000 1521.0000 59319.000 2374321.0 4811092.5 nc * -2436771.5 nc * 40 40.000000 1600.0000 64000.000 2625641.0 5318572.5 nc * -2692931.5 nc * . . . . + . . . . . . . . . + . . . . . . . . . + . . . . . 50 50.000000 2500.0000 125000.00 6377551.0 12885196. nc * -6507645.0 nc * * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11111 returned results (-1 indicates value not changed by called subroutine) ierr is 3 1nonlinear least squares estimation subroutine test number 35 **1 column zero** test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 yes 1.0000000 --- 2 no 2.0000000 default 3 no 3.0000000 default number of observations (n) 10 number of non zero weighted observations (nnzw) 10 number of independent variables (m) 3 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.2131E+08 residual standard deviation for input parameter values (rsd) 1632. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 4 1588. 0.2018E+08 0.5288E-01 1.000 y 0.7363E-02 n current parameter values (only unfixed parameters are listed) index 2 3 value -1.084577 2.956144 iteration number 7 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 16 .6819 3.720 0.4487E-06 0.2594E-06 y 0.1176E-03 y current parameter values (only unfixed parameters are listed) index 2 3 value .5337483 -0.3574737E-02 ***** residual sum of squares convergence ***** output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 1.000000 1.406515 .5934849 0.9626347E-01 2.083555 2 .5337483 .9065033 1.093497 .1737991 1.374815 3-0.3574737E-02 .4547507 1.545249 .2374882 .7114464 4 0.7270527E-01 1.927295 .2818461 .1170947 5 -.2181842 2.218184 .3026730 -.3570770 6 -.3964696 2.396470 .2990789 -.6469849 7 -.4407022 2.440702 .2795893 -.7086045 8 -.3294339 2.329434 .2776611 -.5289622 9 -0.4121614E-01 2.041216 .3549416 -0.7079102E-01 10 .4453998 1.554600 .5406615 1.071925 variance covariance matrix column 1 2 3 4 5 6 1 0.76369611E-02 -.91898721E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -.91898721E-04 0.13050394E-05 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = .6818847 nnzw = 10 npare = 2 1nonlinear least squares estimation subroutine test number 36 **2 columns zero** test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11000 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 yes 1.0000000 --- 2 no 2.0000000 default 3 no 3.0000000 default number of observations (n) 10 number of non zero weighted observations (nnzw) 10 number of independent variables (m) 3 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.2097E+08 residual standard deviation for input parameter values (rsd) 1619. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 5 2.291 41.99 1.000 1.000 n .9961 n current parameter values (only unfixed parameters are listed) index 2 3 value 2.000000 0.5842209E-02 iteration number 3 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 7 1.623 21.06 .0000 -0.2713E-14 n 0.4088E-07 n current parameter values (only unfixed parameters are listed) index 2 3 value 2.000000 0.2848077E-02 ***** singular convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued WARNING WARNING ** error summary ** this model and data are computationally singular. check your input for errors. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued the following summary should be used to analyze the above mentioned problems. estimates from least squares fit --------------------------------- index fixed parameter 1 yes 1.0000000 2 no 2.0000000 3 no 0.28480766E-02 residual sum of squares 21.06474 residual standard deviation 1.622681 based on degrees of freedom 10 - 2 = 8 results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 .00000000 .00000000 1.2500000 2.0000000 0.55626496E-02 nc * 1.9944373 nc * 0.100E+01 2 .00000000 .00000000 2.2500000 2.0000000 0.32441374E-01 nc * 1.9675586 nc * 0.100E+01 3 .00000000 .00000000 3.2500000 2.0000000 0.97769134E-01 nc * 1.9022309 nc * 0.100E+01 4 .00000000 .00000000 4.2500000 2.0000000 .21863438 nc * 1.7813656 nc * 0.100E+01 5 .00000000 .00000000 5.2500000 2.0000000 .41212559 nc * 1.5878744 nc * 0.100E+01 6 .00000000 .00000000 6.2500000 2.0000000 .69533122 nc * 1.3046688 nc * 0.100E+01 7 .00000000 .00000000 7.2500000 2.0000000 1.0853397 nc * .91466033 nc * 0.100E+01 8 .00000000 .00000000 8.2500000 2.0000000 1.5992396 nc * .40076041 nc * 0.100E+01 9 .00000000 .00000000 9.2500000 2.0000000 2.2541192 nc * -.25411916 nc * 0.100E+01 10 .00000000 .00000000 10.250000 2.0000000 3.0670669 nc * -1.0670669 nc * 0.100E+01 * nc - value not computed because convergence problems prevented the covariance matrix from being computed. output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 11000 returned results (-1 indicates value not changed by called subroutine) ierr is 3 par res pv sdpv sdres 1 1.000000 1.994437 0.5562650E-02 0.3402823E+39 0.3402823E+39 2 2.000000 1.967559 0.3244137E-01 0.3402823E+39 0.3402823E+39 3 0.2848077E-02 1.902231 0.9776913E-01 0.3402823E+39 0.3402823E+39 4 1.781366 .2186344 0.3402823E+39 0.3402823E+39 5 1.587874 .4121256 0.3402823E+39 0.3402823E+39 6 1.304669 .6953312 0.3402823E+39 0.3402823E+39 7 .9146603 1.085340 0.3402823E+39 0.3402823E+39 8 .4007604 1.599240 0.3402823E+39 0.3402823E+39 9 -.2541192 2.254119 0.3402823E+39 0.3402823E+39 10 -1.067067 3.067067 0.3402823E+39 0.3402823E+39 variance covariance matrix column 1 2 3 4 5 6 1 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 0.34028235E+39 0.34028235E+39 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 1.622681 nnzw = 10 npare = 2 1nonlinear least squares estimation subroutine test number 37 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = -1, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9890214E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99449549E-02 400.9 3.9612620 4.0123906 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = -1, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5679844E-02 .3655967 2 3.986826 0.4361486E-01 3.377385 0.1296301E-01 .9734890 3 0.4231071E-01 3.554689 0.1409722E-01 .9516841 4 0.1652479E-01 4.323475 0.1925765E-01 .3890117 5 0.2920485E-01 4.852795 0.2301342E-01 .7199129 6 -0.7597160E-01 5.735971 0.2959406E-01 -2.107473 variance covariance matrix column 1 2 3 4 5 6 1 0.98902136E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 38 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 0, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9890214E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99449549E-02 400.9 3.9612620 4.0123906 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 0, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5679844E-02 .3655967 2 3.986826 0.4361486E-01 3.377385 0.1296301E-01 .9734890 3 0.4231071E-01 3.554689 0.1409722E-01 .9516841 4 0.1652479E-01 4.323475 0.1925765E-01 .3890117 5 0.2920485E-01 4.852795 0.2301342E-01 .7199129 6 -0.7597160E-01 5.735971 0.2959406E-01 -2.107473 variance covariance matrix column 1 2 3 4 5 6 1 0.98902136E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 39 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9890214E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99449549E-02 400.9 3.9612620 4.0123906 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5679844E-02 .3655967 2 3.986826 0.4361486E-01 3.377385 0.1296301E-01 .9734890 3 0.4231071E-01 3.554689 0.1409722E-01 .9516841 4 0.1652479E-01 4.323475 0.1925765E-01 .3890117 5 0.2920485E-01 4.852795 0.2301342E-01 .7199129 6 -0.7597160E-01 5.735971 0.2959406E-01 -2.107473 variance covariance matrix column 1 2 3 4 5 6 1 0.98902136E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 40 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 2, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on asymptotic maximum likelihood theory - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9869669E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99346209E-02 401.3 3.9612887 4.0123644 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 2, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5673942E-02 .3655911 2 3.986826 0.4361486E-01 3.377385 0.1294954E-01 .9734045 3 0.4231071E-01 3.554689 0.1408258E-01 .9515848 4 0.1652479E-01 4.323475 0.1923763E-01 .3889286 5 0.2920485E-01 4.852795 0.2298951E-01 .7196723 6 -0.7597160E-01 5.735971 0.2956331E-01 -2.106000 variance covariance matrix column 1 2 3 4 5 6 1 0.98696692E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 41 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that conditions necessary for asymptotic maximum likelihood theory might be violated - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9849168E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99242972E-02 401.7 3.9613152 4.0123377 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 3, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5668046E-02 .3655853 2 3.986826 0.4361486E-01 3.377385 0.1293608E-01 .9733200 3 0.4231071E-01 3.554689 0.1406794E-01 .9514857 4 0.1652479E-01 4.323475 0.1921764E-01 .3888459 5 0.2920485E-01 4.852795 0.2296562E-01 .7194326 6 -0.7597160E-01 5.735971 0.2953259E-01 -2.104532 variance covariance matrix column 1 2 3 4 5 6 1 0.98491677E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 42 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 4, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9890214E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99449549E-02 400.9 3.9612620 4.0123906 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 4, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5679844E-02 .3655967 2 3.986826 0.4361486E-01 3.377385 0.1296301E-01 .9734890 3 0.4231071E-01 3.554689 0.1409722E-01 .9516841 4 0.1652479E-01 4.323475 0.1925765E-01 .3890117 5 0.2920485E-01 4.852795 0.2301342E-01 .7199129 6 -0.7597160E-01 5.735971 0.2959406E-01 -2.107473 variance covariance matrix column 1 2 3 4 5 6 1 0.98902136E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 43 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 5, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on asymptotic maximum likelihood theory - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9879821E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99397283E-02 401.1 3.9612756 4.0123773 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 5, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5676859E-02 .3655939 2 3.986826 0.4361486E-01 3.377385 0.1295620E-01 .9734462 3 0.4231071E-01 3.554689 0.1408982E-01 .9516339 4 0.1652479E-01 4.323475 0.1924752E-01 .3889697 5 0.2920485E-01 4.852795 0.2300133E-01 .7197912 6 -0.7597160E-01 5.735971 0.2957851E-01 -2.106728 variance covariance matrix column 1 2 3 4 5 6 1 0.98798206E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 44 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 6, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that conditions necessary for asymptotic maximum likelihood theory might be violated - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9869441E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99345054E-02 401.3 3.9612889 4.0123639 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 6, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5673876E-02 .3655910 2 3.986826 0.4361486E-01 3.377385 0.1294939E-01 .9734035 3 0.4231071E-01 3.554689 0.1408241E-01 .9515836 4 0.1652479E-01 4.323475 0.1923741E-01 .3889277 5 0.2920485E-01 4.852795 0.2298924E-01 .7196697 6 -0.7597160E-01 5.735971 0.2956297E-01 -2.105983 variance covariance matrix column 1 2 3 4 5 6 1 0.98694407E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 45 test handling of variance-covariance computation codes test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 7, nprt = 2 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.9890214E-04 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9868264 0.99449549E-02 400.9 3.9612620 4.0123906 residual sum of squares 0.1087657E-01 residual standard deviation 0.4664027E-01 based on degrees of freedom 6 - 1 = 5 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 7, nprt = 2 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.1692462E-01 2.121075 0.5679844E-02 .3655967 2 3.986826 0.4361486E-01 3.377385 0.1296301E-01 .9734890 3 0.4231071E-01 3.554689 0.1409722E-01 .9516841 4 0.1652479E-01 4.323475 0.1925765E-01 .3890117 5 0.2920485E-01 4.852795 0.2301342E-01 .7199129 6 -0.7597160E-01 5.735971 0.2959406E-01 -2.107473 variance covariance matrix column 1 2 3 4 5 6 1 0.98902136E-04 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4664027E-01 nnzw = 6 npare = 1 1nonlinear least squares estimation subroutine test number 46 **test with 2 zero weights** test of nlswds input - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 22222 starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ parameter starting values scale index fixed (par) (scale) 1 yes .72500002 --- 2 no 4.0000000 default number of observations (n) 6 number of non zero weighted observations (nnzw) 4 number of independent variables (m) 1 maximum number of iterations allowed (mit) 500 maximum number of model subroutine calls allowed 1000 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.1000E-03 maximum scaled relative change in the parameters (stopp) 0.1000E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.1416E-01 residual standard deviation for input parameter values (rsd) 0.6871E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.4873E-01 0.7123E-02 .4971 .4977 y 0.2734E-02 y current parameter values (only unfixed parameters are listed) index 2 value 3.978189 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.4873E-01 0.7123E-02 0.2014E-04 0.1626E-04 y 0.1124E-04 y current parameter values (only unfixed parameters are listed) index 2 value 3.978099 ***** parameter and residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res weight 1 1.3090000 2.1380000 2.1160972 0.72888853E-02 0.21902800E-01 0.45 0.100E+01 2 1.4710000 3.4210000 3.3660290 0.16618369E-01 0.54970980E-01 1.20 0.100E+01 3 1.4900000 3.5969999 3.5423398 0.18070385E-01 0.54660082E-01 nc * 0.000E+00 4 1.5650001 4.3400002 4.3066092 0.24674641E-01 0.33390999E-01 0.79 0.100E+01 5 1.6109999 4.8820000 4.8326416 0.29479425E-01 0.49358368E-01 nc * 0.000E+00 6 1.6799999 5.6599998 5.7100601 0.37895132E-01 -0.50060272E-01 -1.63 0.100E+01 * nc - value not computed because the weight is zero. 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ * + 0.75+ * + -* - - * - - - - - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - - *- - * - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.058 4.812 8.565 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----**********************---++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - ******** - - - - ******************** - - - - **** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ * + - - - * - - - - - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 2 2 0.1636425E-03 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 yes .72500002 --- --- --- --- 2 no 3.9780993 0.12792282E-01 311.0 3.9373887 4.0188103 residual sum of squares 0.7122531E-02 residual standard deviation 0.4872553E-01 based on degrees of freedom 4 - 1 = 3 approximate condition number 1.000000 output - ifixed(1) = 1 , idrvck = 0 , mit = 500, stopss = 0.99999997E-04, stopp = 0.99999997E-04 scale(1) = -1.0000000 , delta = -1.0000000 , ivaprx = 1, nprt = 22222 returned results (-1 indicates value not changed by called subroutine) ierr is 0 par res pv sdpv sdres 1 .7250000 0.2190280E-01 2.116097 0.7288885E-02 .4546293 2 3.978099 0.5497098E-01 3.366029 0.1661837E-01 1.200135 3 0.5466008E-01 3.542340 0.1807038E-01 0.3402823E+39 4 0.3339100E-01 4.306609 0.2467464E-01 .7947221 5 0.4935837E-01 4.832642 0.2947943E-01 0.3402823E+39 6 -0.5006027E-01 5.710060 0.3789513E-01 -1.634407 variance covariance matrix column 1 2 3 4 5 6 1 0.16364247E-03 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 2 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 3 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 4 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 5 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 6 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 -1.0000000 rsd = 0.4872553E-01 nnzw = 4 npare = 1 1test runs for the nrand family of routines. test 1. generate each of the possible error messages. starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nrand ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call nrand(y, n, iseed) the value of ierr is 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nrandc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of sigma is -1.0000000 . the value of the argument sigma must be greater than or equal to zero . the correct form of the call statement is call nrandc (y, n, iseed, ymean, sigma) the value of ierr is 1 1generate 10 standard normal numbers using iseed = 334 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine nrand ------------------------------------- the value of iseed must be between 0 and 2**31 - 1, inclusive, and, if iseed is not 0, iseed must be odd. the seed actually used by the random number generator has been set to 333. -0.45889217E+00 0.71778372E-01 0.10052732E+01-0.32403651E+00 0.16735224E+00 0.20620432E+01-0.39634407E+00-0.45402986E+00-0.10576700E+01 0.77653337E+00 1generate 10 normally distributed numbers with ymean = 0.00 and sigma = 1.00 using iseed = 333 -0.45889217E+00 0.71778372E-01 0.10052732E+01-0.32403651E+00 0.16735224E+00 0.20620432E+01-0.39634407E+00-0.45402986E+00-0.10576700E+01 0.77653337E+00 1generate 1000 standard normal numbers using iseed = 13531 starpac 2.08s (03/15/90) histogram number of observations = 1000 minimum observation = -2.86867571E+00 maximum observation = 3.49294615E+00 histogram lower bound = -2.86867571E+00 histogram upper bound = 3.49294615E+00 number of cells = 10 observations used = 1000 25 pct trimmed mean = 5.53617440E-02 min. observation used = -2.86867571E+00 standard deviation = 9.77697790E-01 max. observation used = 3.49294615E+00 mean dev./std. dev. = 8.00623715E-01 mean value = 4.42687720E-02 sqrt(beta one) = 2.52594855E-02 median value = 4.92259786E-02 beta two = 2.91934347E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. cell fraction hmid point fract. fract. fract. obs. + 0.00 0.10 0.20 0.30 0.40 0.50 ------------------------------------------ +---------+---------+---------+---------+---------+ -2.550595E+00 0.009 1.000 0.009 9 + -1.914433E+00 0.047 0.991 0.038 38 ++++ -1.278270E+00 0.159 0.953 0.112 112 +++++++++++ -6.421083E-01 0.346 0.841 0.187 187 +++++++++++++++++++ -5.946159E-03 0.598 0.654 0.252 252 +++++++++++++++++++++++++ 6.302160E-01 0.826 0.402 0.228 228 +++++++++++++++++++++++ 1.266378E+00 0.944 0.174 0.118 118 ++++++++++++ 1.902540E+00 0.985 0.056 0.041 41 ++++ 2.538702E+00 0.998 0.015 0.013 13 + 3.174865E+00 1.000 0.002 0.002 2 + 1generate 1000 normally distributed numbers with ymean = 0.00 and sigma = 1.00 using iseed = 13531 starpac 2.08s (03/15/90) histogram number of observations = 1000 minimum observation = -2.86867571E+00 maximum observation = 3.49294615E+00 histogram lower bound = -2.86867571E+00 histogram upper bound = 3.49294615E+00 number of cells = 10 observations used = 1000 25 pct trimmed mean = 5.53617440E-02 min. observation used = -2.86867571E+00 standard deviation = 9.77697790E-01 max. observation used = 3.49294615E+00 mean dev./std. dev. = 8.00623715E-01 mean value = 4.42687720E-02 sqrt(beta one) = 2.52594855E-02 median value = 4.92259786E-02 beta two = 2.91934347E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. cell fraction hmid point fract. fract. fract. obs. + 0.00 0.10 0.20 0.30 0.40 0.50 ------------------------------------------ +---------+---------+---------+---------+---------+ -2.550595E+00 0.009 1.000 0.009 9 + -1.914433E+00 0.047 0.991 0.038 38 ++++ -1.278270E+00 0.159 0.953 0.112 112 +++++++++++ -6.421083E-01 0.346 0.841 0.187 187 +++++++++++++++++++ -5.946159E-03 0.598 0.654 0.252 252 +++++++++++++++++++++++++ 6.302160E-01 0.826 0.402 0.228 228 +++++++++++++++++++++++ 1.266378E+00 0.944 0.174 0.118 118 ++++++++++++ 1.902540E+00 0.985 0.056 0.041 41 ++++ 2.538702E+00 0.998 0.015 0.013 13 + 3.174865E+00 1.000 0.002 0.002 2 + 1generate 1000 standard normal numbers using iseed = 99999 starpac 2.08s (03/15/90) histogram number of observations = 1000 minimum observation = -2.94250894E+00 maximum observation = 3.24497890E+00 histogram lower bound = -2.94250894E+00 histogram upper bound = 3.24497890E+00 number of cells = 10 observations used = 1000 25 pct trimmed mean = 9.12925601E-03 min. observation used = -2.94250894E+00 standard deviation = 9.93340671E-01 max. observation used = 3.24497890E+00 mean dev./std. dev. = 7.84965873E-01 mean value = -5.78684732E-03 sqrt(beta one) = 4.73502465E-02 median value = 3.20420414E-03 beta two = 3.14881325E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. cell fraction hmid point fract. fract. fract. obs. + 0.00 0.05 0.10 0.15 0.20 0.25 ------------------------------------------ +---------+---------+---------+---------+---------+ -2.633134E+00 0.014 1.000 0.014 14 +++ -2.014386E+00 0.051 0.986 0.037 37 +++++++ -1.395637E+00 0.129 0.949 0.078 78 ++++++++++++++++ -7.768881E-01 0.315 0.871 0.186 186 +++++++++++++++++++++++++++++++++++++ -1.581393E-01 0.562 0.685 0.247 247 +++++++++++++++++++++++++++++++++++++++++++++++++ 4.606094E-01 0.797 0.438 0.235 235 +++++++++++++++++++++++++++++++++++++++++++++++ 1.079358E+00 0.926 0.203 0.129 129 ++++++++++++++++++++++++++ 1.698107E+00 0.978 0.074 0.052 52 ++++++++++ 2.316856E+00 0.994 0.022 0.016 16 +++ 2.935605E+00 1.000 0.006 0.006 6 + 1generate 1000 normally distributed numbers with ymean = 4.00 and sigma = 4.00 using iseed = 99999 starpac 2.08s (03/15/90) histogram number of observations = 1000 minimum observation = -7.77003574E+00 maximum observation = 1.69799156E+01 histogram lower bound = -7.77003574E+00 histogram upper bound = 1.69799156E+01 number of cells = 10 observations used = 1000 25 pct trimmed mean = 4.03651762E+00 min. observation used = -7.77003574E+00 standard deviation = 3.97336292E+00 max. observation used = 1.69799156E+01 mean dev./std. dev. = 7.84965575E-01 mean value = 3.97685337E+00 sqrt(beta one) = 4.73506264E-02 median value = 4.01281691E+00 beta two = 3.14881253E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. cell fraction hmid point fract. fract. fract. obs. + 0.00 0.05 0.10 0.15 0.20 0.25 ------------------------------------------ +---------+---------+---------+---------+---------+ -6.532538E+00 0.014 1.000 0.014 14 +++ -4.057543E+00 0.051 0.986 0.037 37 +++++++ -1.582548E+00 0.129 0.949 0.078 78 ++++++++++++++++ 8.924475E-01 0.315 0.871 0.186 186 +++++++++++++++++++++++++++++++++++++ 3.367443E+00 0.562 0.685 0.247 247 +++++++++++++++++++++++++++++++++++++++++++++++++ 5.842438E+00 0.797 0.438 0.235 235 +++++++++++++++++++++++++++++++++++++++++++++++ 8.317432E+00 0.926 0.203 0.129 129 ++++++++++++++++++++++++++ 1.079243E+01 0.978 0.074 0.052 52 ++++++++++ 1.326742E+01 0.994 0.022 0.016 16 +++ 1.574242E+01 1.000 0.006 0.006 6 + test of center ierr is 0 -0.41943287E+02 -0.35943287E+02 -0.30943287E+02 -0.23943287E+02 -0.10943287E+02 0.11056713E+02 -0.17943287E+02 -0.26943287E+02 -0.36943287E+02 -0.38943287E+02 -0.43943287E+02 -0.46943287E+02 -0.46943287E+02 -0.44943287E+02 -0.35943287E+02 -0.19943287E+02 0.56713104E-01 0.16056713E+02 0.13056713E+02 -0.79432869E+01 -0.18943287E+02 -0.20943287E+02 -0.24943287E+02 -0.35943287E+02 -0.25943287E+02 -0.69432869E+01 0.31056713E+02 0.75056717E+02 0.56056713E+02 0.26056713E+02 0.56713104E-01 -0.11943287E+02 -0.35943287E+02 -0.41943287E+02 -0.30943287E+02 -0.12943287E+02 0.23056713E+02 0.34056713E+02 0.64056717E+02 0.54056713E+02 0.26056713E+02 -0.69432869E+01 -0.26943287E+02 -0.30943287E+02 -0.41943287E+02 -0.35943287E+02 -0.24943287E+02 -0.69432869E+01 0.13056713E+02 0.33956715E+02 0.36456715E+02 0.75671387E+00 0.85671234E+00 -0.16243286E+02 -0.34743286E+02 -0.37343285E+02 -0.36743286E+02 -0.14543285E+02 0.65671158E+00 0.70567131E+01 0.15956715E+02 0.38956715E+02 0.14256714E+02 -0.18432884E+01 -0.10543285E+02 -0.26043287E+02 -0.35543289E+02 -0.91432877E+01 0.22856716E+02 0.59156712E+02 0.53856716E+02 0.34656712E+02 0.19556713E+02 -0.12143288E+02 -0.16343287E+02 -0.39943287E+02 -0.27143288E+02 0.45556713E+02 0.10745671E+03 0.78956711E+02 0.37856716E+02 0.21156712E+02 -0.84432869E+01 -0.24143288E+02 -0.36743286E+02 -0.22843287E+02 0.35956715E+02 0.85056717E+02 0.83956711E+02 0.71156708E+02 0.42956715E+02 0.19656712E+02 0.13056713E+02 -0.43285370E-01 -0.59432869E+01 -0.25643288E+02 -0.30943287E+02 -0.40543285E+02 -0.42843288E+02 -0.40143288E+02 -0.32443287E+02 -0.12943287E+02 -0.19432869E+01 -0.38432884E+01 0.55671310E+00 -0.47432861E+01 -0.18843287E+02 -0.36843285E+02 -0.38843285E+02 -0.44443287E+02 -0.46943287E+02 -0.45543285E+02 -0.41943287E+02 -0.34743286E+02 -0.33043289E+02 -0.11543285E+02 -0.11432877E+01 -0.58432884E+01 -0.16843287E+02 -0.23043287E+02 -0.31343287E+02 -0.40343288E+02 -0.42943287E+02 -0.45143288E+02 -0.38443287E+02 -0.30343287E+02 -0.10643288E+02 0.26567116E+01 0.17256710E+02 0.20056713E+02 0.23956715E+02 0.85671234E+00 -0.19443287E+02 -0.38443287E+02 -0.33743286E+02 0.99567146E+01 0.74556717E+02 0.91356720E+02 0.56256710E+02 0.38756710E+02 0.17656712E+02 -0.10243286E+02 -0.22743286E+02 -0.36243286E+02 -0.31943287E+02 -0.68432884E+01 0.14556713E+02 0.51556713E+02 0.77756714E+02 0.49356716E+02 0.19656712E+02 0.17556713E+02 0.71567116E+01 -0.79432869E+01 -0.26343287E+02 -0.40243286E+02 -0.42643288E+02 -0.24243286E+02 0.78567123E+01 0.46856716E+02 0.48856716E+02 0.30256710E+02 0.12156712E+02 -0.29432869E+01 0.56713104E-01 -0.16443287E+02 -0.30643288E+02 -0.39643288E+02 -0.93432884E+01 0.27056713E+02 0.92056717E+02 0.64256714E+02 0.54656712E+02 0.19256710E+02 -0.22432861E+01 -0.29943287E+02 -0.35643288E+02 -0.34543289E+02 -0.43543285E+02 -0.40943287E+02 -0.14643288E+02 0.73567123E+01 0.12756714E+02 0.16756714E+02 0.16556713E+02 0.52567139E+01 -0.21543287E+02 -0.33843285E+02 -0.40143288E+02 -0.40643288E+02 -0.39843288E+02 -0.11343288E+02 0.26056713E+02 0.38156712E+02 0.31056713E+02 0.17056713E+02 -0.51432877E+01 -0.20743286E+02 -0.20243286E+02 -0.34843285E+02 -0.37443287E+02 -0.44243286E+02 -0.41943287E+02 -0.22543287E+02 -0.49432869E+01 0.16556713E+02 0.68567123E+01 0.15056713E+02 0.15567131E+01 -0.30432854E+01 -0.28343287E+02 -0.41243286E+02 -0.43343288E+02 -0.45543285E+02 -0.37343285E+02 0.45671463E+00 0.10156712E+02 0.56956715E+02 0.33656712E+02 0.16656712E+02 -0.93432884E+01 -0.20843287E+02 -0.32743286E+02 -0.41143288E+02 -0.30243286E+02 -0.26432877E+01 0.16956715E+02 0.22056713E+02 0.30856716E+02 0.17956715E+02 -0.11243286E+02 -0.25743286E+02 -0.35843285E+02 -0.41243286E+02 -0.38243286E+02 -0.10843288E+02 0.32756710E+02 0.67456711E+02 0.62656712E+02 0.41856716E+02 0.20856716E+02 0.55671310E+00 -0.16343287E+02 -0.30643288E+02 -0.37343285E+02 -0.13743286E+02 0.45656712E+02 0.10465672E+03 0.89356720E+02 0.87756714E+02 0.36956715E+02 0.22456715E+02 -0.15443287E+02 -0.33043289E+02 -0.42543285E+02 -0.89432869E+01 0.94756714E+02 0.14325671E+03 0.13785672E+03 0.11205672E+03 0.65356720E+02 test of taper ierr is 0 -0.15290715E+00 -0.11678643E+01 -0.27387307E+01 -0.40329762E+01 -0.29288437E+01 0.42053347E+01 -0.89716434E+01 -0.16695620E+02 -0.27055847E+02 -0.32383732E+02 -0.40053955E+02 -0.45418011E+02 -0.46772152E+02 -0.44943287E+02 -0.35943287E+02 -0.19943287E+02 0.56713104E-01 0.16056713E+02 0.13056713E+02 -0.79432869E+01 -0.18943287E+02 -0.20943287E+02 -0.24943287E+02 -0.35943287E+02 -0.25943287E+02 -0.69432869E+01 0.31056713E+02 0.75056717E+02 0.56056713E+02 0.26056713E+02 0.56713104E-01 -0.11943287E+02 -0.35943287E+02 -0.41943287E+02 -0.30943287E+02 -0.12943287E+02 0.23056713E+02 0.34056713E+02 0.64056717E+02 0.54056713E+02 0.26056713E+02 -0.69432869E+01 -0.26943287E+02 -0.30943287E+02 -0.41943287E+02 -0.35943287E+02 -0.24943287E+02 -0.69432869E+01 0.13056713E+02 0.33956715E+02 0.36456715E+02 0.75671387E+00 0.85671234E+00 -0.16243286E+02 -0.34743286E+02 -0.37343285E+02 -0.36743286E+02 -0.14543285E+02 0.65671158E+00 0.70567131E+01 0.15956715E+02 0.38956715E+02 0.14256714E+02 -0.18432884E+01 -0.10543285E+02 -0.26043287E+02 -0.35543289E+02 -0.91432877E+01 0.22856716E+02 0.59156712E+02 0.53856716E+02 0.34656712E+02 0.19556713E+02 -0.12143288E+02 -0.16343287E+02 -0.39943287E+02 -0.27143288E+02 0.45556713E+02 0.10745671E+03 0.78956711E+02 0.37856716E+02 0.21156712E+02 -0.84432869E+01 -0.24143288E+02 -0.36743286E+02 -0.22843287E+02 0.35956715E+02 0.85056717E+02 0.83956711E+02 0.71156708E+02 0.42956715E+02 0.19656712E+02 0.13056713E+02 -0.43285370E-01 -0.59432869E+01 -0.25643288E+02 -0.30943287E+02 -0.40543285E+02 -0.42843288E+02 -0.40143288E+02 -0.32443287E+02 -0.12943287E+02 -0.19432869E+01 -0.38432884E+01 0.55671310E+00 -0.47432861E+01 -0.18843287E+02 -0.36843285E+02 -0.38843285E+02 -0.44443287E+02 -0.46943287E+02 -0.45543285E+02 -0.41943287E+02 -0.34743286E+02 -0.33043289E+02 -0.11543285E+02 -0.11432877E+01 -0.58432884E+01 -0.16843287E+02 -0.23043287E+02 -0.31343287E+02 -0.40343288E+02 -0.42943287E+02 -0.45143288E+02 -0.38443287E+02 -0.30343287E+02 -0.10643288E+02 0.26567116E+01 0.17256710E+02 0.20056713E+02 0.23956715E+02 0.85671234E+00 -0.19443287E+02 -0.38443287E+02 -0.33743286E+02 0.99567146E+01 0.74556717E+02 0.91356720E+02 0.56256710E+02 0.38756710E+02 0.17656712E+02 -0.10243286E+02 -0.22743286E+02 -0.36243286E+02 -0.31943287E+02 -0.68432884E+01 0.14556713E+02 0.51556713E+02 0.77756714E+02 0.49356716E+02 0.19656712E+02 0.17556713E+02 0.71567116E+01 -0.79432869E+01 -0.26343287E+02 -0.40243286E+02 -0.42643288E+02 -0.24243286E+02 0.78567123E+01 0.46856716E+02 0.48856716E+02 0.30256710E+02 0.12156712E+02 -0.29432869E+01 0.56713104E-01 -0.16443287E+02 -0.30643288E+02 -0.39643288E+02 -0.93432884E+01 0.27056713E+02 0.92056717E+02 0.64256714E+02 0.54656712E+02 0.19256710E+02 -0.22432861E+01 -0.29943287E+02 -0.35643288E+02 -0.34543289E+02 -0.43543285E+02 -0.40943287E+02 -0.14643288E+02 0.73567123E+01 0.12756714E+02 0.16756714E+02 0.16556713E+02 0.52567139E+01 -0.21543287E+02 -0.33843285E+02 -0.40143288E+02 -0.40643288E+02 -0.39843288E+02 -0.11343288E+02 0.26056713E+02 0.38156712E+02 0.31056713E+02 0.17056713E+02 -0.51432877E+01 -0.20743286E+02 -0.20243286E+02 -0.34843285E+02 -0.37443287E+02 -0.44243286E+02 -0.41943287E+02 -0.22543287E+02 -0.49432869E+01 0.16556713E+02 0.68567123E+01 0.15056713E+02 0.15567131E+01 -0.30432854E+01 -0.28343287E+02 -0.41243286E+02 -0.43343288E+02 -0.45543285E+02 -0.37343285E+02 0.45671463E+00 0.10156712E+02 0.56956715E+02 0.33656712E+02 0.16656712E+02 -0.93432884E+01 -0.20843287E+02 -0.32743286E+02 -0.41143288E+02 -0.30243286E+02 -0.26432877E+01 0.16956715E+02 0.22056713E+02 0.30856716E+02 0.17956715E+02 -0.11243286E+02 -0.25743286E+02 -0.35843285E+02 -0.41243286E+02 -0.38243286E+02 -0.10843288E+02 0.32756710E+02 0.67456711E+02 0.62656712E+02 0.41856716E+02 0.20856716E+02 0.55671310E+00 -0.16343287E+02 -0.30643288E+02 -0.37343285E+02 -0.13743286E+02 0.45656712E+02 0.10465672E+03 0.89030968E+02 0.84905327E+02 0.33685749E+02 0.18674135E+02 -0.11310071E+02 -0.20475534E+02 -0.21271643E+02 -0.34015093E+01 0.25360535E+02 0.24129974E+02 0.12201432E+02 0.36409314E+01 0.23826244E+00 test of pgm starpac 2.08s (03/15/90) sample periodogram (in decibels) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 48.7516 - + - i i i i i i i + i 43.9721 - + - i + + i i 2 i i + i i + + i 39.1927 - + - i i i + + + i i + i i + + i 34.4133 - + + - i + + i i ++ + + ++ i i + + i i + + + i 29.6339 - + + + - i ++ + + i i + + + i i i i + + + + i 24.8544 - + + + - i + + ++ + + + + + i i + +++ + + + + + + i i + + + + i i + + + + i 20.0750 - + + + + - i + + + + ++ + i i + + + + + + i i + + + ++ + i i + + + i 15.2956 - + + + + + + + - i + + + + i i + ++ + i i + + + + + + i i + + i 10.5162 - + + - i + i i + + i i + i i i 5.7367 - - i i i i i i i i 0.9573 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of fftlen ierr is 0 nfft is 514 test of pgms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine pgms ------------------------------------- the input value of the parameter nfft ( 513) does not meet the requirements of singletons fft code. the next larger value which does is 514. the value 514 will be used for the extended series length. starpac 2.08s (03/15/90) sample periodogram -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 47729.7500 - + - i i i i i 2 + i i + i i 2 + i 10000.0000 - + - i + + i 5000.0000 - ++ + - i + + 2 + i i + ++ + i i ++ + + ++ i i + + + 2 i i + + ++ + i i + + + + + + i 1000.0000 - +2+ + + - i + + + 2 + ++ i 500.0000 - + + +++ ++ 2 ++ + - i + + + 2 + i i + + + ++ + + i i + + + + ++ + i i ++ + 2 + + + + i i + + 2 2 + + i 100.0000 - +++ + ++ ++++ 2 + ++ ++ + - i + + + + ++ 2 ++ + + i i + + + + + + + + + ++ + ++ i 50.0000 - + + + ++ + + + + + - i + + 2 + + ++ + 2++ + ++ i i + ++ + + + 2 + + ++ + i i + + + + ++ + + i i + +++ +2 + + + ++ 3+ + + + + i i + + + + ++ i 10.0000 - + + + + + 2 + + - i + + + + + i 5.0000 - + + + + + - i i i + ++ i i + + + i i + + i i i i + + i 1.0000 - - i + i 0.5000 - - i i i i i i i i i i 0.1000 - - 0.0799 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. nfft is 513 ierr is 0 0.00000000E+00 0.19531250E-02 0.39062500E-02 0.58593750E-02 0.78125000E-02 0.97656250E-02 0.11718750E-01 0.13671875E-01 0.15625000E-01 0.17578125E-01 0.19531250E-01 0.21484375E-01 0.23437500E-01 0.25390625E-01 0.27343750E-01 0.29296875E-01 0.31250000E-01 0.33203125E-01 0.35156250E-01 0.37109375E-01 0.39062500E-01 0.41015625E-01 0.42968750E-01 0.44921875E-01 0.46875000E-01 0.48828125E-01 0.50781250E-01 0.52734375E-01 0.54687500E-01 0.56640625E-01 0.58593750E-01 0.60546875E-01 0.62500000E-01 0.64453125E-01 0.66406250E-01 0.68359375E-01 0.70312500E-01 0.72265625E-01 0.74218750E-01 0.76171875E-01 0.78125000E-01 0.80078125E-01 0.82031250E-01 0.83984375E-01 0.85937500E-01 0.87890625E-01 0.89843750E-01 0.91796875E-01 0.93750000E-01 0.95703125E-01 0.97656250E-01 0.99609375E-01 0.10156250E+00 0.10351562E+00 0.10546875E+00 0.10742188E+00 0.10937500E+00 0.11132812E+00 0.11328125E+00 0.11523438E+00 0.11718750E+00 0.11914062E+00 0.12109375E+00 0.12304688E+00 0.12500000E+00 0.12695312E+00 0.12890625E+00 0.13085938E+00 0.13281250E+00 0.13476562E+00 0.13671875E+00 0.13867188E+00 0.14062500E+00 0.14257812E+00 0.14453125E+00 0.14648438E+00 0.14843750E+00 0.15039062E+00 0.15234375E+00 0.15429688E+00 0.15625000E+00 0.15820312E+00 0.16015625E+00 0.16210938E+00 0.16406250E+00 0.16601562E+00 0.16796875E+00 0.16992188E+00 0.17187500E+00 0.17382812E+00 0.17578125E+00 0.17773438E+00 0.17968750E+00 0.18164062E+00 0.18359375E+00 0.18554688E+00 0.18750000E+00 0.18945312E+00 0.19140625E+00 0.19335938E+00 0.19531250E+00 0.19726562E+00 0.19921875E+00 0.20117188E+00 0.20312500E+00 0.20507812E+00 0.20703125E+00 0.20898438E+00 0.21093750E+00 0.21289062E+00 0.21484375E+00 0.21679688E+00 0.21875000E+00 0.22070312E+00 0.22265625E+00 0.22460938E+00 0.22656250E+00 0.22851562E+00 0.23046875E+00 0.23242188E+00 0.23437500E+00 0.23632812E+00 0.23828125E+00 0.24023438E+00 0.24218750E+00 0.24414062E+00 0.24609375E+00 0.24804688E+00 0.25000000E+00 0.25195312E+00 0.25390625E+00 0.25585938E+00 0.25781250E+00 0.25976562E+00 0.26171875E+00 0.26367188E+00 0.26562500E+00 0.26757812E+00 0.26953125E+00 0.27148438E+00 0.27343750E+00 0.27539062E+00 0.27734375E+00 0.27929688E+00 0.28125000E+00 0.28320312E+00 0.28515625E+00 0.28710938E+00 0.28906250E+00 0.29101562E+00 0.29296875E+00 0.29492188E+00 0.29687500E+00 0.29882812E+00 0.30078125E+00 0.30273438E+00 0.30468750E+00 0.30664062E+00 0.30859375E+00 0.31054688E+00 0.31250000E+00 0.31445312E+00 0.31640625E+00 0.31835938E+00 0.32031250E+00 0.32226562E+00 0.32421875E+00 0.32617188E+00 0.32812500E+00 0.33007812E+00 0.33203125E+00 0.33398438E+00 0.33593750E+00 0.33789062E+00 0.33984375E+00 0.34179688E+00 0.34375000E+00 0.34570312E+00 0.34765625E+00 0.34960938E+00 0.35156250E+00 0.35351562E+00 0.35546875E+00 0.35742188E+00 0.35937500E+00 0.36132812E+00 0.36328125E+00 0.36523438E+00 0.36718750E+00 0.36914062E+00 0.37109375E+00 0.37304688E+00 0.37500000E+00 0.37695312E+00 0.37890625E+00 0.38085938E+00 0.38281250E+00 0.38476562E+00 0.38671875E+00 0.38867188E+00 0.39062500E+00 0.39257812E+00 0.39453125E+00 0.39648438E+00 0.39843750E+00 0.40039062E+00 0.40234375E+00 0.40429688E+00 0.40625000E+00 0.40820312E+00 0.41015625E+00 0.41210938E+00 0.41406250E+00 0.41601562E+00 0.41796875E+00 0.41992188E+00 0.42187500E+00 0.42382812E+00 0.42578125E+00 0.42773438E+00 0.42968750E+00 0.43164062E+00 0.43359375E+00 0.43554688E+00 0.43750000E+00 0.43945312E+00 0.44140625E+00 0.44335938E+00 0.44531250E+00 0.44726562E+00 0.44921875E+00 0.45117188E+00 0.45312500E+00 0.45507812E+00 0.45703125E+00 0.45898438E+00 0.46093750E+00 0.46289062E+00 0.46484375E+00 0.46679688E+00 0.46875000E+00 0.47070312E+00 0.47265625E+00 0.47460938E+00 0.47656250E+00 0.47851562E+00 0.48046875E+00 0.48242188E+00 0.48437500E+00 0.48632812E+00 0.48828125E+00 0.49023438E+00 0.49218750E+00 0.49414062E+00 0.49609375E+00 0.49804688E+00 0.50000000E+00 0.32249594E+03 0.74935437E+03 0.27311295E+03 0.17461674E+04 0.36836389E+03 0.11774479E+05 0.13822347E+05 0.38262358E+04 0.12312119E+04 0.70322739E+04 0.86727998E+04 0.24384414E+04 0.28954727E+04 0.22068422E+03 0.17036268E+04 0.56703864E+03 0.33378186E+03 0.61941811E+02 0.22555522E+04 0.30025403E+04 0.84041760E+03 0.57587531E+03 0.39563980E+02 0.80676282E+03 0.99133325E+03 0.97913110E+03 0.45832825E+03 0.57321484E+03 0.75660278E+02 0.12513515E+03 0.55137518E+03 0.17306559E+04 0.12046140E+04 0.18764528E+03 0.64501758E+03 0.26368677E+04 0.20334792E+04 0.16553278E+03 0.13139119E+04 0.49524229E+04 0.21266807E+04 0.19009897E+04 0.61683560E+04 0.67504502E+04 0.61181069E+04 0.19205248E+05 0.47729773E+05 0.19125926E+05 0.31576279E+04 0.13765041E+05 0.29837219E+04 0.23286225E+05 0.17122324E+05 0.76070824E+02 0.44846064E+04 0.44208716E+04 0.16069729E+04 0.68028693E+03 0.31423142E+04 0.12947598E+04 0.44674604E+04 0.57152969E+04 0.22455596E+04 0.23381255E+04 0.14102432E+04 0.25861154E+03 0.10269471E+04 0.13532404E+04 0.41029565E+03 0.75239270E+03 0.76759241E+03 0.17715581E+03 0.45639523E+03 0.53508478E+03 0.33109000E+03 0.11831969E+03 0.59480930E+02 0.12726088E+03 0.10457677E+03 0.52840870E+02 0.10275404E+03 0.13605817E+03 0.12765086E+03 0.74387756E+02 0.29419481E+02 0.35078992E+03 0.74880377E+03 0.47527924E+03 0.52290723E+03 0.10481721E+04 0.89434875E+03 0.23341949E+03 0.62042114E+03 0.15125369E+04 0.73218665E+03 0.48618618E+02 0.31520039E+02 0.36441357E+03 0.36403409E+03 0.24393888E+02 0.48715637E+03 0.94742332E+02 0.26764886E+03 0.50996738E+03 0.33360783E+02 0.29041760E+03 0.84482056E+03 0.59213580E+03 0.24557861E+02 0.26414777E+03 0.18771924E+03 0.15849654E+03 0.32166168E+03 0.61642544E+02 0.17042706E+02 0.10990285E+03 0.16509569E+03 0.99162262E+02 0.40790854E+03 0.25993091E+03 0.62926579E+02 0.25719153E+03 0.15073882E+03 0.92711388E+02 0.14517593E+03 0.16900772E+03 0.11891722E+03 0.25546083E+02 0.97782486E+02 0.21049844E+03 0.10254627E+03 0.46260365E+02 0.13254927E+03 0.90822929E+02 0.91636391E+01 0.49552951E+01 0.55185303E+02 0.10633472E+03 0.17493702E+02 0.11540630E+03 0.33625531E+03 0.20184560E+03 0.14390571E+02 0.60528631E+01 0.16259485E+02 0.62402355E+02 0.46421829E+02 0.79914123E-01 0.51378586E+02 0.10375204E+03 0.67257896E+02 0.87841053E+01 0.15041517E+02 0.14681117E+02 0.16016052E+02 0.10525831E+02 0.76185784E+01 0.23327188E+02 0.16681475E+02 0.37440097E+01 0.60302910E+02 0.11305019E+03 0.13454569E+02 0.46800850E+02 0.89223343E+02 0.95315750E+02 0.90039124E+02 0.30610104E+02 0.14632752E+02 0.67889333E+00 0.76939678E+01 0.17552778E+01 0.10619121E+02 0.28531839E+02 0.17473305E+02 0.29758554E+02 0.78754265E+02 0.90874290E+02 0.33638664E+02 0.18321495E+02 0.41353176E+02 0.15353157E+02 0.90163094E+02 0.18702693E+03 0.91748283E+02 0.12961302E+01 0.26266399E+02 0.39779831E+02 0.19848553E+02 0.77225533E+01 0.11844740E+02 0.17335804E+02 0.18089441E+02 0.18094700E+02 0.11058237E+02 0.14784035E+02 0.10911881E+02 0.28260741E+01 0.11785717E+01 0.74564877E+01 0.20504830E+01 0.51768031E+01 0.10781134E+02 0.95498953E+01 0.21852585E+02 0.35477013E+02 0.16648813E+02 0.49016434E+02 0.68009392E+02 0.21132767E+02 0.62195382E+01 0.39563438E+02 0.39480362E+02 0.37198138E+01 0.41500095E+02 0.18319679E+02 0.31376176E+01 0.21278976E+02 0.92424728E+02 0.44974449E+02 0.20743340E+02 0.10896548E+03 0.37308495E+02 0.15692808E+02 0.19281042E+02 0.99956017E+01 0.55499393E+02 0.27350712E+01 0.67081169E+02 0.87543625E+02 0.17539169E+02 0.12943848E+02 0.24637201E+01 0.37275215E+02 0.36630066E+02 0.27543497E+02 0.33659763E+02 0.32689953E+02 0.61458459E+01 0.10449180E+03 0.13427411E+03 0.60915833E+02 0.37749840E+02 0.12383274E+02 0.66855683E+01 0.47558815E+02 0.31299791E+02 0.13700484E+02 0.33239449E+02 0.29246193E+02 0.21336878E+02 0.36956783E+02 0.55482327E+02 0.68331398E+02 0.75391670E+02 0.26829247E+02 0.88789253E+01 test of mdflt ierr is 0 0.37626309E+04 0.37540137E+04 0.37280693E+04 0.36862402E+04 0.36320010E+04 0.35676873E+04 0.34933621E+04 0.34084448E+04 0.33126665E+04 0.32061758E+04 0.30890569E+04 0.29608064E+04 0.28206414E+04 0.26689709E+04 0.25094500E+04 0.23482024E+04 0.21899834E+04 0.20371130E+04 0.18919563E+04 0.17576221E+04 0.16366470E+04 0.15314445E+04 0.14465111E+04 0.13890205E+04 0.13655458E+04 0.13794088E+04 0.14324722E+04 0.15272808E+04 0.16673855E+04 0.18559756E+04 0.20935879E+04 0.23781003E+04 0.27068120E+04 0.30776812E+04 0.34885422E+04 0.39357935E+04 0.44147754E+04 0.49198999E+04 0.54400107E+04 0.59554189E+04 0.64468745E+04 0.69037856E+04 0.73202114E+04 0.76895996E+04 0.80027427E+04 0.82493945E+04 0.84232051E+04 0.85220439E+04 0.85452412E+04 0.84928096E+04 0.83654385E+04 0.81647598E+04 0.78932979E+04 0.75544717E+04 0.71570732E+04 0.67188955E+04 0.62582090E+04 0.57852373E+04 0.53057124E+04 0.48258838E+04 0.43547378E+04 0.39025759E+04 0.34758894E+04 0.30771990E+04 0.27082300E+04 0.23702175E+04 0.20636406E+04 0.17887162E+04 0.15456825E+04 0.13350415E+04 0.11560863E+04 0.10048799E+04 0.87658459E+03 0.76851843E+03 0.67919073E+03 0.60684668E+03 0.54884467E+03 0.50202737E+03 0.46436884E+03 0.43498062E+03 0.41302502E+03 0.39771417E+03 0.38844058E+03 0.38457660E+03 0.38541531E+03 0.38988525E+03 0.39639368E+03 0.40344727E+03 0.41016687E+03 0.41612692E+03 0.42104791E+03 0.42463171E+03 0.42654453E+03 0.42648898E+03 0.42432727E+03 0.42011917E+03 0.41399805E+03 0.40615460E+03 0.39686816E+03 0.38631775E+03 0.37451761E+03 0.36165302E+03 0.34825885E+03 0.33487897E+03 0.32172031E+03 0.30875040E+03 0.29599911E+03 0.28361407E+03 0.27172488E+03 0.26042596E+03 0.24974612E+03 0.23959531E+03 0.22986757E+03 0.22047646E+03 0.21133925E+03 0.20251434E+03 0.19419305E+03 0.18644809E+03 0.17915294E+03 0.17215811E+03 0.16543814E+03 0.15904727E+03 0.15297041E+03 0.14711162E+03 0.14140034E+03 0.13580450E+03 0.13033163E+03 0.12507381E+03 0.12014754E+03 0.11561317E+03 0.11148232E+03 0.10771222E+03 0.10419148E+03 0.10078313E+03 0.97386612E+02 0.93962967E+02 0.90505531E+02 0.87023460E+02 0.83562675E+02 0.80185440E+02 0.76933990E+02 0.73834312E+02 0.70885117E+02 0.68035240E+02 0.65218445E+02 0.62404858E+02 0.59591179E+02 0.56775440E+02 0.53991974E+02 0.51347031E+02 0.48958237E+02 0.46878181E+02 0.45105408E+02 0.43615494E+02 0.42371010E+02 0.41340076E+02 0.40495529E+02 0.39802612E+02 0.39233242E+02 0.38781311E+02 0.38458256E+02 0.38276688E+02 0.38238876E+02 0.38341785E+02 0.38572136E+02 0.38890259E+02 0.39245495E+02 0.39606228E+02 0.39957859E+02 0.40289902E+02 0.40594536E+02 0.40866428E+02 0.41109791E+02 0.41344555E+02 0.41593910E+02 0.41853271E+02 0.42070282E+02 0.42177090E+02 0.42134075E+02 0.41923653E+02 0.41528946E+02 0.40938614E+02 0.40155251E+02 0.39189323E+02 0.38058022E+02 0.36790627E+02 0.35420444E+02 0.33970490E+02 0.32451729E+02 0.30868484E+02 0.29226379E+02 0.27557806E+02 0.25937162E+02 0.24443171E+02 0.23120304E+02 0.21993334E+02 0.21086935E+02 0.20417662E+02 0.19989882E+02 0.19800629E+02 0.19833939E+02 0.20058081E+02 0.20443445E+02 0.20975998E+02 0.21646250E+02 0.22443716E+02 0.23360615E+02 0.24377451E+02 0.25456873E+02 0.26565201E+02 0.27679371E+02 0.28770342E+02 0.29805927E+02 0.30763729E+02 0.31621733E+02 0.32362076E+02 0.32997734E+02 0.33570126E+02 0.34112263E+02 0.34631340E+02 0.35125832E+02 0.35587654E+02 0.35996330E+02 0.36340290E+02 0.36625633E+02 0.36859829E+02 0.37049831E+02 0.37213207E+02 0.37372635E+02 0.37548378E+02 0.37770988E+02 0.38069412E+02 0.38426811E+02 0.38779919E+02 0.39073212E+02 0.39292095E+02 0.39449291E+02 0.39571243E+02 0.39688152E+02 0.39814705E+02 0.39948830E+02 0.40084259E+02 0.40216606E+02 0.40343311E+02 0.40458702E+02 0.40546471E+02 0.40573593E+02 0.40508526E+02 0.40366550E+02 0.40217770E+02 0.40128704E+02 0.40113308E+02 0.40144604E+02 0.40187687E+02 0.40220818E+02 0.40239716E+02 0.40245972E+02 display of periodogram smoothed with modified daniel filter starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 8545.2412 - 322 - 8000.0000 - +2 +2 - i 2 + i 6000.0000 - + 2 - i + + i i 2 + i 4000.0000 - + ++ - i 223 + + i i 22 + + i i ++ + + i i 2 i i 2 + + i 2000.0000 - 2 + + - i + 2 + i i 2 + + i i 32 + i i + i i i 1000.0000 - + - i + i 800.0000 - + - i + i 600.0000 - + - i 2 i i + i 400.0000 - 22+32323 - i ++ 3+ i i +2 i i +2 i i 3 i i 2+ i 200.0000 - 2 - i 3 i i 22 i i +2 i i 3 i i 2+ i 100.0000 - 22 - 80.0000 - 3 - i 3 i i 2 i 60.0000 - 3 - i 2 i i 3+ i 40.0000 - +32 +2323233 2232332322 - i 32 2+ 32332+ i i 2+ +32 i i ++ 2+ i i 2 2+ i i 2+ 3 i 20.0000 - 223 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 test of ipgmp starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - +232323323232323323232323323232332322 - i +23233232323233232323232 . i i 323322 . i i +3323232 . i i 223+ . . i 0.9000 - 2+ . . - i + . . i i 2 . . i i +2 . . i i +2 . . i 0.8000 - . . - i . . i i + . . i i . . i i . . i 0.7000 - . . - i ++ . . i i . . i i . . i i ++ . . i 0.6000 - . . - i . . i i + . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - + . . - i . . i i . . i i + . . i i + . . i 0.3000 - + . . - i 2 . . i i 2 . . i i 33 . . i i +232 . . i 0.2000 - 222 . . - i 32+ . . i i 2 . . i i + . . i i . . i 0.1000 - 22 . - i . . i i . i i + . i i + . i 0.0000 - 22 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of ipgmps starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - +232323323232323323232323323232332322 - i +23233232323233232323232 . i i 323322 . i i +3323232 . i i 223+ . . i 0.9000 - 2+ . . - i + . . i i 2 . . i i +2 . . i i +2 . . i 0.8000 - . . - i . . i i + . . i i . . i i . . i 0.7000 - . . - i ++ . . i i . . i i . . i i ++ . . i 0.6000 - . . - i . . i i + . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - + . . - i . . i i . . i i + . . i i + . . i 0.3000 - + . . - i 2 . . i i 2 . . i i 33 . . i i +232 . . i 0.2000 - 222 . . - i 32+ . . i i 2 . . i i + . . i i . . i 0.1000 - 22 . - i . . i i . i i + . i i + . i 0.0000 - 22 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.19531250E-02 0.39062500E-02 0.58593750E-02 0.78125000E-02 0.97656250E-02 0.11718750E-01 0.13671875E-01 0.15625000E-01 0.17578125E-01 0.19531250E-01 0.21484375E-01 0.23437500E-01 0.25390625E-01 0.27343750E-01 0.29296875E-01 0.31250000E-01 0.33203125E-01 0.35156250E-01 0.37109375E-01 0.39062500E-01 0.41015625E-01 0.42968750E-01 0.44921875E-01 0.46875000E-01 0.48828125E-01 0.50781250E-01 0.52734375E-01 0.54687500E-01 0.56640625E-01 0.58593750E-01 0.60546875E-01 0.62500000E-01 0.64453125E-01 0.66406250E-01 0.68359375E-01 0.70312500E-01 0.72265625E-01 0.74218750E-01 0.76171875E-01 0.78125000E-01 0.80078125E-01 0.82031250E-01 0.83984375E-01 0.85937500E-01 0.87890625E-01 0.89843750E-01 0.91796875E-01 0.93750000E-01 0.95703125E-01 0.97656250E-01 0.99609375E-01 0.10156250E+00 0.10351562E+00 0.10546875E+00 0.10742188E+00 0.10937500E+00 0.11132812E+00 0.11328125E+00 0.11523438E+00 0.11718750E+00 0.11914062E+00 0.12109375E+00 0.12304688E+00 0.12500000E+00 0.12695312E+00 0.12890625E+00 0.13085938E+00 0.13281250E+00 0.13476562E+00 0.13671875E+00 0.13867188E+00 0.14062500E+00 0.14257812E+00 0.14453125E+00 0.14648438E+00 0.14843750E+00 0.15039062E+00 0.15234375E+00 0.15429688E+00 0.15625000E+00 0.15820312E+00 0.16015625E+00 0.16210938E+00 0.16406250E+00 0.16601562E+00 0.16796875E+00 0.16992188E+00 0.17187500E+00 0.17382812E+00 0.17578125E+00 0.17773438E+00 0.17968750E+00 0.18164062E+00 0.18359375E+00 0.18554688E+00 0.18750000E+00 0.18945312E+00 0.19140625E+00 0.19335938E+00 0.19531250E+00 0.19726562E+00 0.19921875E+00 0.20117188E+00 0.20312500E+00 0.20507812E+00 0.20703125E+00 0.20898438E+00 0.21093750E+00 0.21289062E+00 0.21484375E+00 0.21679688E+00 0.21875000E+00 0.22070312E+00 0.22265625E+00 0.22460938E+00 0.22656250E+00 0.22851562E+00 0.23046875E+00 0.23242188E+00 0.23437500E+00 0.23632812E+00 0.23828125E+00 0.24023438E+00 0.24218750E+00 0.24414062E+00 0.24609375E+00 0.24804688E+00 0.25000000E+00 0.25195312E+00 0.25390625E+00 0.25585938E+00 0.25781250E+00 0.25976562E+00 0.26171875E+00 0.26367188E+00 0.26562500E+00 0.26757812E+00 0.26953125E+00 0.27148438E+00 0.27343750E+00 0.27539062E+00 0.27734375E+00 0.27929688E+00 0.28125000E+00 0.28320312E+00 0.28515625E+00 0.28710938E+00 0.28906250E+00 0.29101562E+00 0.29296875E+00 0.29492188E+00 0.29687500E+00 0.29882812E+00 0.30078125E+00 0.30273438E+00 0.30468750E+00 0.30664062E+00 0.30859375E+00 0.31054688E+00 0.31250000E+00 0.31445312E+00 0.31640625E+00 0.31835938E+00 0.32031250E+00 0.32226562E+00 0.32421875E+00 0.32617188E+00 0.32812500E+00 0.33007812E+00 0.33203125E+00 0.33398438E+00 0.33593750E+00 0.33789062E+00 0.33984375E+00 0.34179688E+00 0.34375000E+00 0.34570312E+00 0.34765625E+00 0.34960938E+00 0.35156250E+00 0.35351562E+00 0.35546875E+00 0.35742188E+00 0.35937500E+00 0.36132812E+00 0.36328125E+00 0.36523438E+00 0.36718750E+00 0.36914062E+00 0.37109375E+00 0.37304688E+00 0.37500000E+00 0.37695312E+00 0.37890625E+00 0.38085938E+00 0.38281250E+00 0.38476562E+00 0.38671875E+00 0.38867188E+00 0.39062500E+00 0.39257812E+00 0.39453125E+00 0.39648438E+00 0.39843750E+00 0.40039062E+00 0.40234375E+00 0.40429688E+00 0.40625000E+00 0.40820312E+00 0.41015625E+00 0.41210938E+00 0.41406250E+00 0.41601562E+00 0.41796875E+00 0.41992188E+00 0.42187500E+00 0.42382812E+00 0.42578125E+00 0.42773438E+00 0.42968750E+00 0.43164062E+00 0.43359375E+00 0.43554688E+00 0.43750000E+00 0.43945312E+00 0.44140625E+00 0.44335938E+00 0.44531250E+00 0.44726562E+00 0.44921875E+00 0.45117188E+00 0.45312500E+00 0.45507812E+00 0.45703125E+00 0.45898438E+00 0.46093750E+00 0.46289062E+00 0.46484375E+00 0.46679688E+00 0.46875000E+00 0.47070312E+00 0.47265625E+00 0.47460938E+00 0.47656250E+00 0.47851562E+00 0.48046875E+00 0.48242188E+00 0.48437500E+00 0.48632812E+00 0.48828125E+00 0.49023438E+00 0.49218750E+00 0.49414062E+00 0.49609375E+00 0.49804688E+00 0.50000000E+00 0.10329618E-02 0.34331609E-02 0.43079481E-02 0.99009611E-02 0.11080840E-01 0.48794765E-01 0.93068056E-01 0.10532357E+00 0.10926717E+00 0.13179170E+00 0.15957087E+00 0.16738126E+00 0.17665552E+00 0.17736238E+00 0.18281913E+00 0.18463537E+00 0.18570447E+00 0.18590288E+00 0.19312745E+00 0.20274465E+00 0.20543653E+00 0.20728107E+00 0.20740779E+00 0.20999187E+00 0.21316715E+00 0.21630332E+00 0.21777137E+00 0.21960740E+00 0.21984975E+00 0.22025055E+00 0.22201662E+00 0.22755995E+00 0.23141837E+00 0.23201941E+00 0.23408541E+00 0.24253136E+00 0.24904463E+00 0.24957483E+00 0.25378332E+00 0.26964605E+00 0.27645785E+00 0.28254676E+00 0.30230415E+00 0.32392600E+00 0.34352246E+00 0.40503731E+00 0.55791688E+00 0.61917764E+00 0.62929153E+00 0.67338133E+00 0.68293822E+00 0.75752455E+00 0.81236774E+00 0.81261140E+00 0.82697570E+00 0.84113586E+00 0.84628302E+00 0.84846199E+00 0.85852689E+00 0.86267400E+00 0.87698340E+00 0.89528966E+00 0.90248227E+00 0.90997136E+00 0.91448838E+00 0.91531676E+00 0.91860610E+00 0.92294055E+00 0.92425472E+00 0.92666471E+00 0.92912334E+00 0.92969078E+00 0.93115264E+00 0.93286657E+00 0.93392706E+00 0.93430603E+00 0.93449646E+00 0.93490410E+00 0.93523896E+00 0.93540823E+00 0.93573737E+00 0.93617314E+00 0.93658203E+00 0.93682027E+00 0.93691444E+00 0.93803805E+00 0.94043648E+00 0.94195884E+00 0.94363374E+00 0.94699109E+00 0.94985569E+00 0.95060331E+00 0.95259047E+00 0.95743513E+00 0.95978034E+00 0.95993608E+00 0.96003711E+00 0.96120429E+00 0.96237028E+00 0.96244848E+00 0.96400881E+00 0.96431231E+00 0.96516961E+00 0.96680307E+00 0.96690995E+00 0.96784014E+00 0.97054613E+00 0.97244269E+00 0.97252136E+00 0.97336745E+00 0.97396874E+00 0.97447640E+00 0.97550666E+00 0.97570419E+00 0.97575873E+00 0.97611076E+00 0.97663957E+00 0.97695714E+00 0.97826368E+00 0.97909629E+00 0.97929788E+00 0.98012161E+00 0.98060447E+00 0.98090148E+00 0.98136652E+00 0.98190784E+00 0.98228866E+00 0.98237044E+00 0.98268366E+00 0.98335791E+00 0.98368633E+00 0.98383445E+00 0.98425907E+00 0.98454994E+00 0.98457927E+00 0.98459518E+00 0.98477191E+00 0.98511255E+00 0.98516858E+00 0.98553824E+00 0.98661524E+00 0.98726177E+00 0.98730785E+00 0.98732722E+00 0.98737931E+00 0.98757917E+00 0.98772782E+00 0.98772812E+00 0.98789269E+00 0.98822498E+00 0.98844039E+00 0.98846853E+00 0.98851669E+00 0.98856372E+00 0.98861504E+00 0.98864877E+00 0.98867321E+00 0.98874789E+00 0.98880136E+00 0.98881334E+00 0.98900652E+00 0.98936868E+00 0.98941183E+00 0.98956174E+00 0.98984754E+00 0.99015284E+00 0.99044120E+00 0.99053925E+00 0.99058610E+00 0.99058831E+00 0.99061292E+00 0.99061853E+00 0.99065256E+00 0.99074399E+00 0.99079990E+00 0.99089521E+00 0.99114746E+00 0.99143851E+00 0.99154621E+00 0.99160486E+00 0.99173731E+00 0.99178642E+00 0.99207520E+00 0.99267429E+00 0.99296814E+00 0.99297225E+00 0.99305648E+00 0.99318385E+00 0.99324745E+00 0.99327213E+00 0.99331009E+00 0.99336565E+00 0.99342358E+00 0.99348158E+00 0.99351698E+00 0.99356431E+00 0.99359924E+00 0.99360830E+00 0.99361205E+00 0.99363601E+00 0.99364263E+00 0.99365920E+00 0.99369377E+00 0.99372441E+00 0.99379432E+00 0.99390793E+00 0.99396127E+00 0.99411833E+00 0.99433613E+00 0.99440384E+00 0.99442375E+00 0.99455047E+00 0.99467689E+00 0.99468881E+00 0.99482173E+00 0.99488038E+00 0.99489039E+00 0.99495852E+00 0.99525464E+00 0.99539864E+00 0.99546510E+00 0.99581414E+00 0.99593365E+00 0.99598390E+00 0.99604565E+00 0.99607772E+00 0.99625546E+00 0.99626428E+00 0.99647915E+00 0.99675953E+00 0.99681568E+00 0.99685711E+00 0.99686503E+00 0.99698448E+00 0.99710178E+00 0.99718994E+00 0.99729776E+00 0.99740243E+00 0.99742216E+00 0.99775690E+00 0.99818701E+00 0.99838209E+00 0.99850297E+00 0.99854261E+00 0.99856406E+00 0.99871641E+00 0.99881667E+00 0.99886054E+00 0.99896705E+00 0.99906069E+00 0.99912906E+00 0.99924749E+00 0.99942517E+00 0.99964404E+00 0.99988562E+00 0.99997157E+00 0.10000000E+01 test of ipgm starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - +++2++2++2+++2++2++2++2++2++2++2++2++ - i 2++2++2+++2++2++2++2+++ . i i 2++2++ . i i ++2++2++ . i i ++2 . . i 0.9000 - ++ . . - i . . i i + . . i i ++ . . i i . . i 0.8000 - + . . - i . . i i + . . i i . . i i . . i 0.7000 - + . . - i . . i i . . i i . . i i + . . i 0.6000 - . . - i . . i i . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - . . - i + . . i i . . i i . . i i + . . i 0.3000 - . . - i ++ . . i i . . i i ++2 . . i i +2 . . i 0.2000 - +2+ . . - i 2+ . . i i + . . i i . . i i . . i 0.1000 - 2+ . - i . . i i . i i . i i . i 0.0000 - +++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of ipgms starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - +++2++2++2+++2++2++2++2++2++2++2++2++ - i 2++2++2+++2++2++2++2+++ . i i 2++2++ . i i ++2++2++ . i i ++2 . . i 0.9000 - ++ . . - i . . i i + . . i i ++ . . i i . . i 0.8000 - + . . - i . . i i + . . i i . . i i . . i 0.7000 - + . . - i . . i i . . i i . . i i + . . i 0.6000 - . . - i . . i i . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - . . - i + . . i i . . i i . . i i + . . i 0.3000 - . . - i ++ . . i i . . i i ++2 . . i i +2 . . i 0.2000 - +2+ . . - i 2+ . . i i + . . i i . . i i . . i 0.1000 - 2+ . - i . . i i . i i . i i . i 0.0000 - +++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.37878789E-02 0.75757578E-02 0.11363637E-01 0.15151516E-01 0.18939395E-01 0.22727273E-01 0.26515152E-01 0.30303031E-01 0.34090910E-01 0.37878789E-01 0.41666668E-01 0.45454547E-01 0.49242426E-01 0.53030305E-01 0.56818184E-01 0.60606062E-01 0.64393938E-01 0.68181813E-01 0.71969688E-01 0.75757563E-01 0.79545438E-01 0.83333313E-01 0.87121189E-01 0.90909064E-01 0.94696939E-01 0.98484814E-01 0.10227269E+00 0.10606056E+00 0.10984844E+00 0.11363631E+00 0.11742419E+00 0.12121207E+00 0.12499994E+00 0.12878782E+00 0.13257569E+00 0.13636357E+00 0.14015144E+00 0.14393932E+00 0.14772719E+00 0.15151507E+00 0.15530294E+00 0.15909082E+00 0.16287869E+00 0.16666657E+00 0.17045444E+00 0.17424232E+00 0.17803019E+00 0.18181807E+00 0.18560594E+00 0.18939382E+00 0.19318169E+00 0.19696957E+00 0.20075744E+00 0.20454532E+00 0.20833319E+00 0.21212107E+00 0.21590894E+00 0.21969682E+00 0.22348469E+00 0.22727257E+00 0.23106045E+00 0.23484832E+00 0.23863620E+00 0.24242407E+00 0.24621195E+00 0.24999982E+00 0.25378770E+00 0.25757557E+00 0.26136345E+00 0.26515132E+00 0.26893920E+00 0.27272707E+00 0.27651495E+00 0.28030282E+00 0.28409070E+00 0.28787857E+00 0.29166645E+00 0.29545432E+00 0.29924220E+00 0.30303007E+00 0.30681795E+00 0.31060582E+00 0.31439370E+00 0.31818157E+00 0.32196945E+00 0.32575732E+00 0.32954520E+00 0.33333308E+00 0.33712095E+00 0.34090883E+00 0.34469670E+00 0.34848458E+00 0.35227245E+00 0.35606033E+00 0.35984820E+00 0.36363608E+00 0.36742395E+00 0.37121183E+00 0.37499970E+00 0.37878758E+00 0.38257545E+00 0.38636333E+00 0.39015120E+00 0.39393908E+00 0.39772695E+00 0.40151483E+00 0.40530270E+00 0.40909058E+00 0.41287845E+00 0.41666633E+00 0.42045420E+00 0.42424208E+00 0.42802995E+00 0.43181783E+00 0.43560570E+00 0.43939358E+00 0.44318146E+00 0.44696933E+00 0.45075721E+00 0.45454508E+00 0.45833296E+00 0.46212083E+00 0.46590871E+00 0.46969658E+00 0.47348446E+00 0.47727233E+00 0.48106021E+00 0.48484808E+00 0.48863596E+00 0.49242383E+00 0.49621171E+00 0.50000000E+00 0.27725673E-15 0.12581620E-02 0.17357259E-02 0.96258134E-01 0.10349320E+00 0.16509883E+00 0.18235604E+00 0.18703204E+00 0.18725154E+00 0.19034387E+00 0.20241880E+00 0.20421772E+00 0.20978610E+00 0.21518086E+00 0.21845847E+00 0.21934082E+00 0.23030247E+00 0.23153083E+00 0.24704571E+00 0.24904753E+00 0.27659076E+00 0.28210518E+00 0.32293397E+00 0.38389149E+00 0.62459141E+00 0.69718283E+00 0.75400370E+00 0.80436248E+00 0.83680767E+00 0.84235895E+00 0.86111391E+00 0.89264297E+00 0.90601718E+00 0.91478372E+00 0.92074400E+00 0.92377484E+00 0.92920274E+00 0.93149745E+00 0.93396693E+00 0.93435448E+00 0.93517309E+00 0.93562794E+00 0.93648291E+00 0.93677336E+00 0.94008076E+00 0.94262141E+00 0.94939327E+00 0.95058614E+00 0.96002978E+00 0.96030533E+00 0.96249461E+00 0.96254867E+00 0.96362078E+00 0.96706134E+00 0.96791846E+00 0.97282892E+00 0.97360092E+00 0.97415990E+00 0.97545129E+00 0.97573370E+00 0.97649133E+00 0.97928375E+00 0.97996479E+00 0.98072737E+00 0.98166960E+00 0.98236573E+00 0.98297554E+00 0.98367012E+00 0.98445684E+00 0.98455989E+00 0.98478639E+00 0.98506808E+00 0.98681706E+00 0.98724300E+00 0.98730105E+00 0.98773223E+00 0.98779440E+00 0.98842645E+00 0.98845828E+00 0.98853731E+00 0.98858553E+00 0.98873198E+00 0.98875546E+00 0.98946601E+00 0.98970580E+00 0.99027687E+00 0.99052697E+00 0.99054736E+00 0.99058115E+00 0.99073690E+00 0.99084878E+00 0.99143702E+00 0.99153537E+00 0.99174267E+00 0.99256611E+00 0.99293011E+00 0.99313158E+00 0.99324089E+00 0.99331665E+00 0.99342936E+00 0.99350041E+00 0.99357629E+00 0.99358058E+00 0.99360538E+00 0.99367070E+00 0.99376506E+00 0.99392122E+00 0.99436343E+00 0.99441433E+00 0.99473041E+00 0.99481624E+00 0.99487740E+00 0.99509370E+00 0.99527508E+00 0.99594831E+00 0.99605060E+00 0.99610239E+00 0.99614143E+00 0.99675441E+00 0.99684775E+00 0.99702537E+00 0.99721175E+00 0.99746358E+00 0.99776500E+00 0.99837101E+00 0.99855173E+00 0.99868399E+00 0.99880183E+00 0.99901056E+00 0.99914849E+00 0.99949718E+00 0.99994725E+00 0.10000000E+01 test of fftr ierr is 0 -0.23841858E-05 0.00000000E+00 -0.28360734E+01 0.71108060E+01 -0.10268552E+02 0.10796180E+02 -0.19436886E+02 0.88392735E+01 -0.26673634E+02 0.10348785E+01 -0.28768360E+02 -0.10684374E+02 -0.24095848E+02 -0.22845695E+02 -0.13230585E+02 -0.31564686E+02 0.11626766E+01 -0.33834724E+02 0.15134077E+02 -0.28552046E+02 0.24692400E+02 -0.16935810E+02 0.27084768E+02 -0.21991692E+01 0.21696014E+02 0.11428744E+02 0.10273703E+02 0.20018112E+02 -0.35972753E+01 0.21169415E+02 -0.15604272E+02 0.14765267E+02 -0.22046631E+02 0.30032697E+01 -0.20978977E+02 -0.10298685E+02 -0.12796869E+02 -0.20900259E+02 -0.84322929E-01 -0.25456795E+02 0.13220213E+02 -0.22540447E+02 0.23067289E+02 -0.13047018E+02 0.26551399E+02 0.12921882E+00 0.22783085E+02 0.13036447E+02 0.13112955E+02 0.21913305E+02 0.65321112E+00 0.24345398E+02 -0.10734646E+02 0.19970715E+02 -0.17653648E+02 0.10526581E+02 -0.18200968E+02 -0.75534916E+00 -0.12498453E+02 -0.10194404E+02 -0.25777068E+01 -0.14803955E+02 0.83179379E+01 -0.13172137E+02 0.16771057E+02 -0.58179049E+01 0.20228176E+02 0.50627537E+01 0.17715216E+02 0.16349396E+02 0.10026051E+02 0.24985615E+02 -0.64206600E+00 0.28880682E+02 -0.11455477E+02 0.27448118E+02 -0.19843901E+02 0.21637995E+02 -0.24238895E+02 0.13491165E+02 -0.24428823E+02 0.53956738E+01 -0.21448740E+02 -0.69074726E+00 -0.17081478E+02 -0.37965817E+01 -0.13168873E+02 -0.41076984E+01 -0.10980202E+02 -0.27312543E+01 -0.10846958E+02 -0.11687150E+01 -0.12166708E+02 -0.70468140E+00 -0.13744469E+02 -0.19322824E+01 -0.14324875E+02 -0.45729814E+01 -0.13111353E+02 -0.76351199E+01 -0.10085679E+02 -0.98333588E+01 -0.60229206E+01 -0.10103121E+02 -0.22108331E+01 -0.80194511E+01 0.12558460E-01 -0.39736619E+01 -0.26963472E+00 0.94545937E+00 -0.32401896E+01 0.53113394E+01 -0.82960663E+01 0.78228660E+01 -0.14262574E+02 0.77042437E+01 -0.19783375E+02 0.49062538E+01 -0.23738792E+02 0.52449703E-01 -0.25543962E+02 -0.58310542E+01 -0.25231693E+02 -0.11687601E+02 -0.23310780E+02 -0.16759472E+02 -0.20475231E+02 -0.20756479E+02 -0.17292355E+02 -0.23814787E+02 -0.13998054E+02 -0.26282856E+02 -0.10478386E+02 -0.28439564E+02 -0.64357805E+01 -0.30274620E+02 -0.16599114E+01 -0.31433151E+02 0.37234650E+01 -0.31356531E+02 0.91476250E+01 -0.29566940E+02 0.13710706E+02 -0.25976772E+02 0.16473444E+02 -0.21083538E+02 0.16846571E+02 -0.15945438E+02 0.14917258E+02 -0.11912485E+02 0.11571121E+02 -0.10183887E+02 0.83301048E+01 -0.11339235E+02 0.69263968E+01 -0.15017970E+02 0.87332592E+01 -0.19885714E+02 0.14238978E+02 -0.23935818E+02 0.22753031E+02 -0.25059776E+02 0.32469738E+02 -0.21719540E+02 0.40902428E+02 -0.13505968E+02 0.45576523E+02 -0.13892641E+01 0.44776054E+02 0.12443153E+02 0.38107979E+02 0.25136353E+02 0.26695538E+02 0.33952709E+02 0.12923233E+02 0.37056499E+02 -0.20343637E+00 0.34054382E+02 -0.98957157E+01 0.26118328E+02 -0.14362531E+02 0.15645855E+02 -0.13294220E+02 0.55578041E+01 -0.79002438E+01 -0.15509405E+01 -0.49222648E+00 -0.41456671E+01 0.62530994E+01 -0.21457796E+01 0.10116334E+02 0.31373057E+01 0.99844036E+01 0.94882078E+01 0.61267319E+01 0.14536270E+02 0.22279382E-01 0.16519531E+02 -0.61927366E+01 0.14818938E+02 -0.10465500E+02 0.10097801E+02 -0.11507974E+02 0.40141559E+01 -0.91968794E+01 -0.13857276E+01 -0.45682268E+01 -0.43819742E+01 0.58039069E+00 -0.41621032E+01 0.43361483E+01 -0.10766327E+01 0.53422365E+01 0.35099168E+01 0.32659225E+01 0.77145395E+01 -0.10926161E+01 0.98040676E+01 -0.60833850E+01 0.88069067E+01 -0.97820845E+01 0.48605056E+01 -0.10660712E+02 -0.82185459E+00 -0.81260071E+01 -0.63524594E+01 -0.27442927E+01 -0.98026943E+01 0.39225116E+01 -0.98561258E+01 0.98149509E+01 -0.62632494E+01 0.13043036E+02 0.52570820E-01 0.12502426E+02 0.72723203E+01 0.82389374E+01 0.13249825E+02 0.14445257E+01 0.16185549E+02 -0.59104328E+01 0.15187923E+02 -0.11686470E+02 0.10544245E+02 -0.14228299E+02 0.36200714E+01 -0.12854749E+02 -0.35729539E+01 -0.80358057E+01 -0.89961777E+01 -0.12114515E+01 -0.11189975E+02 0.56775799E+01 -0.96780453E+01 0.10788211E+02 -0.50527129E+01 0.12909599E+02 test of center ierr is 0 -0.13000000E+02 -0.70000000E+01 -0.20000000E+01 0.50000000E+01 0.18000000E+02 0.40000000E+02 0.11000000E+02 0.20000000E+01 -0.80000000E+01 -0.10000000E+02 -0.15000000E+02 -0.18000000E+02 -0.18000000E+02 -0.16000000E+02 -0.70000000E+01 0.90000000E+01 0.29000000E+02 test of taper ierr is 0 -0.13000000E+02 -0.70000000E+01 -0.20000000E+01 0.50000000E+01 0.18000000E+02 0.40000000E+02 0.11000000E+02 0.20000000E+01 -0.80000000E+01 -0.10000000E+02 -0.15000000E+02 -0.18000000E+02 -0.18000000E+02 -0.16000000E+02 -0.70000000E+01 0.90000000E+01 0.29000000E+02 test of pgm starpac 2.08s (03/15/90) sample periodogram (in decibels) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 32.5413 - + - i i i i i + i i i 30.3330 - - i i i i i + i i i 28.1246 - - i i i i i i i i 25.9162 - - i + i i i i i i i 23.7078 - - i i i i i i i i 21.4995 - - i + i i i i i i i 19.2911 - - i i i + i i i i + i 17.0827 - - i i i i i i i i 14.8743 - - i + i i i i i i i 12.6660 - - i i i i i i i i 10.4576 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of fftlen ierr is 0 nfft is 514 test of pgms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine pgms ------------------------------------- the input value of the parameter nfft ( 513) does not meet the requirements of singletons fft code. the next larger value which does is 514. the value 514 will be used for the extended series length. starpac 2.08s (03/15/90) sample periodogram -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 118.3231 - 3232 - 100.0000 - 22 3+ - i 2+ 2+ i i ++ + i 50.0000 - ++ 2 - i 2 + i i + + 22+ i i ++ + +2+ 22 i i + + 2 3 i i + + + 2 i i + + ++ ++ 23 i i + ++ +3 2+ i 10.0000 - + + + + +2 2+ - i + + ++ + + 332+ 2232 i i + + + + 2 + 2 2+ 2+ 3 i 5.0000 - + + + + 2 ++ 2 2 - i + + + + + + + + ++ + i i + ++ + + + ++ 2 + i i + + + + + + + + + i i + + + + + ++ + i i + ++ + + + + + + i i + + + + + i 1.0000 - + + + + 2 + - i + + + + + + i i + + 2 i i + + + + + + + i 0.5000 - 22 + + ++ + - i + + + i i + ++++ i i + + 2+ i i i i + + i i + i 0.1000 - + - i i i i i + i 0.0500 - - i i i + i i i i i i i i + i 0.0100 - - i i i i 0.0050 - - i i i i 0.0029 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. nfft is 513 ierr is 0 0.00000000E+00 0.19531250E-02 0.39062500E-02 0.58593750E-02 0.78125000E-02 0.97656250E-02 0.11718750E-01 0.13671875E-01 0.15625000E-01 0.17578125E-01 0.19531250E-01 0.21484375E-01 0.23437500E-01 0.25390625E-01 0.27343750E-01 0.29296875E-01 0.31250000E-01 0.33203125E-01 0.35156250E-01 0.37109375E-01 0.39062500E-01 0.41015625E-01 0.42968750E-01 0.44921875E-01 0.46875000E-01 0.48828125E-01 0.50781250E-01 0.52734375E-01 0.54687500E-01 0.56640625E-01 0.58593750E-01 0.60546875E-01 0.62500000E-01 0.64453125E-01 0.66406250E-01 0.68359375E-01 0.70312500E-01 0.72265625E-01 0.74218750E-01 0.76171875E-01 0.78125000E-01 0.80078125E-01 0.82031250E-01 0.83984375E-01 0.85937500E-01 0.87890625E-01 0.89843750E-01 0.91796875E-01 0.93750000E-01 0.95703125E-01 0.97656250E-01 0.99609375E-01 0.10156250E+00 0.10351562E+00 0.10546875E+00 0.10742188E+00 0.10937500E+00 0.11132812E+00 0.11328125E+00 0.11523438E+00 0.11718750E+00 0.11914062E+00 0.12109375E+00 0.12304688E+00 0.12500000E+00 0.12695312E+00 0.12890625E+00 0.13085938E+00 0.13281250E+00 0.13476562E+00 0.13671875E+00 0.13867188E+00 0.14062500E+00 0.14257812E+00 0.14453125E+00 0.14648438E+00 0.14843750E+00 0.15039062E+00 0.15234375E+00 0.15429688E+00 0.15625000E+00 0.15820312E+00 0.16015625E+00 0.16210938E+00 0.16406250E+00 0.16601562E+00 0.16796875E+00 0.16992188E+00 0.17187500E+00 0.17382812E+00 0.17578125E+00 0.17773438E+00 0.17968750E+00 0.18164062E+00 0.18359375E+00 0.18554688E+00 0.18750000E+00 0.18945312E+00 0.19140625E+00 0.19335938E+00 0.19531250E+00 0.19726562E+00 0.19921875E+00 0.20117188E+00 0.20312500E+00 0.20507812E+00 0.20703125E+00 0.20898438E+00 0.21093750E+00 0.21289062E+00 0.21484375E+00 0.21679688E+00 0.21875000E+00 0.22070312E+00 0.22265625E+00 0.22460938E+00 0.22656250E+00 0.22851562E+00 0.23046875E+00 0.23242188E+00 0.23437500E+00 0.23632812E+00 0.23828125E+00 0.24023438E+00 0.24218750E+00 0.24414062E+00 0.24609375E+00 0.24804688E+00 0.25000000E+00 0.25195312E+00 0.25390625E+00 0.25585938E+00 0.25781250E+00 0.25976562E+00 0.26171875E+00 0.26367188E+00 0.26562500E+00 0.26757812E+00 0.26953125E+00 0.27148438E+00 0.27343750E+00 0.27539062E+00 0.27734375E+00 0.27929688E+00 0.28125000E+00 0.28320312E+00 0.28515625E+00 0.28710938E+00 0.28906250E+00 0.29101562E+00 0.29296875E+00 0.29492188E+00 0.29687500E+00 0.29882812E+00 0.30078125E+00 0.30273438E+00 0.30468750E+00 0.30664062E+00 0.30859375E+00 0.31054688E+00 0.31250000E+00 0.31445312E+00 0.31640625E+00 0.31835938E+00 0.32031250E+00 0.32226562E+00 0.32421875E+00 0.32617188E+00 0.32812500E+00 0.33007812E+00 0.33203125E+00 0.33398438E+00 0.33593750E+00 0.33789062E+00 0.33984375E+00 0.34179688E+00 0.34375000E+00 0.34570312E+00 0.34765625E+00 0.34960938E+00 0.35156250E+00 0.35351562E+00 0.35546875E+00 0.35742188E+00 0.35937500E+00 0.36132812E+00 0.36328125E+00 0.36523438E+00 0.36718750E+00 0.36914062E+00 0.37109375E+00 0.37304688E+00 0.37500000E+00 0.37695312E+00 0.37890625E+00 0.38085938E+00 0.38281250E+00 0.38476562E+00 0.38671875E+00 0.38867188E+00 0.39062500E+00 0.39257812E+00 0.39453125E+00 0.39648438E+00 0.39843750E+00 0.40039062E+00 0.40234375E+00 0.40429688E+00 0.40625000E+00 0.40820312E+00 0.41015625E+00 0.41210938E+00 0.41406250E+00 0.41601562E+00 0.41796875E+00 0.41992188E+00 0.42187500E+00 0.42382812E+00 0.42578125E+00 0.42773438E+00 0.42968750E+00 0.43164062E+00 0.43359375E+00 0.43554688E+00 0.43750000E+00 0.43945312E+00 0.44140625E+00 0.44335938E+00 0.44531250E+00 0.44726562E+00 0.44921875E+00 0.45117188E+00 0.45312500E+00 0.45507812E+00 0.45703125E+00 0.45898438E+00 0.46093750E+00 0.46289062E+00 0.46484375E+00 0.46679688E+00 0.46875000E+00 0.47070312E+00 0.47265625E+00 0.47460938E+00 0.47656250E+00 0.47851562E+00 0.48046875E+00 0.48242188E+00 0.48437500E+00 0.48632812E+00 0.48828125E+00 0.49023438E+00 0.49218750E+00 0.49414062E+00 0.49609375E+00 0.49804688E+00 0.50000000E+00 0.00000000E+00 0.28507500E-02 0.12007113E-01 0.29313829E-01 0.57952851E-01 0.10259269E+00 0.16958161E+00 0.26716533E+00 0.40570873E+00 0.59789699E+00 0.85889941E+00 0.12064593E+01 0.16609023E+01 0.22450256E+01 0.29838722E+01 0.39043601E+01 0.50347719E+01 0.64040980E+01 0.80412579E+01 0.99741840E+01 0.12228821E+02 0.14828029E+02 0.17790495E+02 0.21129591E+02 0.24852301E+02 0.28958199E+02 0.33438599E+02 0.38275829E+02 0.43442719E+02 0.48902267E+02 0.54607758E+02 0.60502850E+02 0.66522293E+02 0.72592667E+02 0.78633583E+02 0.84559097E+02 0.90279266E+02 0.95702240E+02 0.10073590E+03 0.10529043E+03 0.10928012E+03 0.11262579E+03 0.11525682E+03 0.11711299E+03 0.11814645E+03 0.11832311E+03 0.11762386E+03 0.11604546E+03 0.11360113E+03 0.11032037E+03 0.10624893E+03 0.10144781E+03 0.95992287E+02 0.89970291E+02 0.83480690E+02 0.76630943E+02 0.69534882E+02 0.62310028E+02 0.55075104E+02 0.47947151E+02 0.41039059E+02 0.34456909E+02 0.28297695E+02 0.22647236E+02 0.17578394E+02 0.13149561E+02 0.94037199E+01 0.63677340E+01 0.40521727E+01 0.24514885E+01 0.15446819E+01 0.12962836E+01 0.16577318E+01 0.25690355E+01 0.39606910E+01 0.57557893E+01 0.78722634E+01 0.10225159E+02 0.12728948E+02 0.15299696E+02 0.17857187E+02 0.20326756E+02 0.22640976E+02 0.24740967E+02 0.26577511E+02 0.28111725E+02 0.29315495E+02 0.30171499E+02 0.30672981E+02 0.30823187E+02 0.30634537E+02 0.30127596E+02 0.29329874E+02 0.28274426E+02 0.26998476E+02 0.25541920E+02 0.23945938E+02 0.22251579E+02 0.20498541E+02 0.18724051E+02 0.16961977E+02 0.15242003E+02 0.13589252E+02 0.12023832E+02 0.10560886E+02 0.92106123E+01 0.79785981E+01 0.68662729E+01 0.58714962E+01 0.49892049E+01 0.42121925E+01 0.35318432E+01 0.29388981E+01 0.24241352E+01 0.19790285E+01 0.15962586E+01 0.12701257E+01 0.99682051E+00 0.77456254E+00 0.60358852E+00 0.48600242E+00 0.42550346E+00 0.42700785E+00 0.49618000E+00 0.63889933E+00 0.86070418E+00 0.11662152E+01 0.15585966E+01 0.20390620E+01 0.26064506E+01 0.32569165E+01 0.39837232E+01 0.47771668E+01 0.56246343E+01 0.65108027E+01 0.74179654E+01 0.83264627E+01 0.92152557E+01 0.10062549E+02 0.10846479E+02 0.11545861E+02 0.12140914E+02 0.12613958E+02 0.12950100E+02 0.13137803E+02 0.13169350E+02 0.13041233E+02 0.12754349E+02 0.12314070E+02 0.11730172E+02 0.11016622E+02 0.10191159E+02 0.92748117E+01 0.82912722E+01 0.72661610E+01 0.62262702E+01 0.51986942E+01 0.42100148E+01 0.32854669E+01 0.24481528E+01 0.17183408E+01 0.11128588E+01 0.64459199E+00 0.32212821E+00 0.14954963E+00 0.12636422E+00 0.24759990E+00 0.50404084E+00 0.88260162E+00 0.13668244E+01 0.19374820E+01 0.25732415E+01 0.32514157E+01 0.39487200E+01 0.46420298E+01 0.53091226E+01 0.59293647E+01 0.64843187E+01 0.69582748E+01 0.73386354E+01 0.76162305E+01 0.77854481E+01 0.78443031E+01 0.77942934E+01 0.76402211E+01 0.73898511E+01 0.70535135E+01 0.66435976E+01 0.61740317E+01 0.56597080E+01 0.51159143E+01 0.45577755E+01 0.39997327E+01 0.34550784E+01 0.29355736E+01 0.24511275E+01 0.20095863E+01 0.16166192E+01 0.12757022E+01 0.98818016E+00 0.75344837E+00 0.56917429E+00 0.43161210E+00 0.33594054E+00 0.27664346E+00 0.24788980E+00 0.24391574E+00 0.25936967E+00 0.28962132E+00 0.33101144E+00 0.38103813E+00 0.43846500E+00 0.50334996E+00 0.57699269E+00 0.66181529E+00 0.76116675E+00 0.87905836E+00 0.10198783E+01 0.11880462E+01 0.13876699E+01 0.16222099E+01 0.18941277E+01 0.22046261E+01 0.25533850E+01 0.29384089E+01 0.33558969E+01 0.38002563E+01 0.42641339E+01 0.47386012E+01 0.52133589E+01 0.56770453E+01 0.61176100E+01 0.65227385E+01 0.68802695E+01 0.71786985E+01 0.74076195E+01 0.75581646E+01 0.76234016E+01 0.75986776E+01 0.74818964E+01 0.72736917E+01 0.69774952E+01 0.65995693E+01 0.61488514E+01 0.56367579E+01 0.50768700E+01 0.44845424E+01 0.38764286E+01 0.32699809E+01 0.26828718E+01 0.21324286E+01 0.16350691E+01 0.12057465E+01 0.85745335E+00 0.60078323E+00 0.44356993E+00 0.39062500E+00 test of mdflt ierr is 0 0.87709588E+00 0.90655965E+00 0.99578261E+00 0.11472485E+01 0.13650585E+01 0.16548735E+01 0.20238295E+01 0.24804287E+01 0.30344000E+01 0.36965306E+01 0.44784646E+01 0.53924704E+01 0.64511728E+01 0.76672564E+01 0.90531359E+01 0.10620596E+02 0.12380415E+02 0.14341970E+02 0.16512829E+02 0.18898333E+02 0.21501217E+02 0.24321213E+02 0.27354713E+02 0.30594461E+02 0.34029297E+02 0.37643982E+02 0.41419090E+02 0.45330986E+02 0.49351894E+02 0.53450073E+02 0.57590103E+02 0.61733246E+02 0.65837952E+02 0.69860405E+02 0.73755203E+02 0.77476089E+02 0.80976776E+02 0.84211769E+02 0.87137245E+02 0.89711937E+02 0.91898033E+02 0.93661964E+02 0.94975189E+02 0.95814926E+02 0.96164734E+02 0.96014977E+02 0.95363220E+02 0.94214371E+02 0.92580826E+02 0.90482361E+02 0.87945755E+02 0.85004532E+02 0.81698303E+02 0.78072136E+02 0.74175667E+02 0.70062248E+02 0.65787895E+02 0.61410248E+02 0.56987446E+02 0.52577049E+02 0.48234951E+02 0.44014324E+02 0.39964622E+02 0.36130749E+02 0.32552246E+02 0.29262669E+02 0.26289085E+02 0.23651733E+02 0.21363821E+02 0.19431524E+02 0.17854095E+02 0.16624166E+02 0.15728156E+02 0.15146826E+02 0.14855938E+02 0.14827004E+02 0.15028100E+02 0.15424725E+02 0.15980684E+02 0.16658976E+02 0.17422636E+02 0.18235573E+02 0.19063299E+02 0.19873617E+02 0.20637186E+02 0.21328007E+02 0.21923786E+02 0.22406183E+02 0.22760958E+02 0.22977987E+02 0.23051197E+02 0.22978382E+02 0.22760967E+02 0.22403652E+02 0.21914047E+02 0.21302240E+02 0.20580339E+02 0.19762011E+02 0.18862019E+02 0.17895775E+02 0.16878931E+02 0.15826982E+02 0.14754953E+02 0.13677102E+02 0.12606705E+02 0.11555878E+02 0.10535463E+02 0.95549765E+01 0.86225739E+01 0.77450972E+01 0.69281263E+01 0.61760702E+01 0.54922709E+01 0.48791261E+01 0.43382068E+01 0.38703732E+01 0.34758816E+01 0.31544750E+01 0.29054525E+01 0.27277248E+01 0.26198382E+01 0.25799861E+01 0.26059952E+01 0.26953032E+01 0.28449168E+01 0.30513697E+01 0.33106756E+01 0.36182890E+01 0.39690709E+01 0.43572745E+01 0.47765465E+01 0.52199526E+01 0.56800270E+01 0.61488523E+01 0.66181574E+01 0.70794487E+01 0.75241594E+01 0.79438162E+01 0.83302279E+01 0.86756668E+01 0.89730682E+01 0.92162094E+01 0.93998928E+01 0.95200930E+01 0.95740957E+01 0.95605869E+01 0.94797325E+01 0.93331852E+01 0.91240892E+01 0.88570042E+01 0.85378237E+01 0.81736250E+01 0.77724996E+01 0.73433518E+01 0.68956628E+01 0.64392495E+01 0.59839993E+01 0.55396099E+01 0.51153355E+01 0.47197390E+01 0.43604722E+01 0.40440764E+01 0.37758222E+01 0.35595872E+01 0.33977702E+01 0.32912550E+01 0.32394161E+01 0.32401693E+01 0.32900629E+01 0.33844082E+01 0.35174432E+01 0.36825223E+01 0.38723302E+01 0.40791049E+01 0.42948742E+01 0.45116835E+01 0.47218199E+01 0.49180241E+01 0.50936728E+01 0.52429433E+01 0.53609419E+01 0.54438019E+01 0.54887514E+01 0.54941325E+01 0.54593983E+01 0.53850698E+01 0.52726703E+01 0.51246247E+01 0.49441442E+01 0.47350931E+01 0.45018435E+01 0.42491288E+01 0.39818935E+01 0.37051482E+01 0.34238410E+01 0.31427348E+01 0.28663080E+01 0.25986714E+01 0.23435066E+01 0.21040261E+01 0.18829551E+01 0.16825290E+01 0.15045114E+01 0.13502258E+01 0.12205958E+01 0.11161977E+01 0.10373124E+01 0.98397952E+00 0.95604944E+00 0.95322710E+00 0.97510982E+00 0.10212120E+01 0.10909810E+01 0.11837982E+01 0.12989725E+01 0.14357177E+01 0.15931275E+01 0.17701386E+01 0.19654924E+01 0.21776972E+01 0.24049907E+01 0.26453092E+01 0.28962662E+01 0.31551428E+01 0.34188888E+01 0.36841450E+01 0.39472766E+01 0.42044268E+01 0.44515839E+01 0.46846662E+01 0.48996153E+01 0.50925045E+01 0.52596493E+01 0.53977184E+01 0.55038514E+01 0.55757618E+01 0.56118326E+01 0.56111979E+01 0.55738034E+01 0.55004492E+01 0.53928046E+01 0.52533970E+01 0.50855813E+01 0.48934712E+01 0.46818528E+01 0.44560747E+01 0.42219119E+01 0.39854200E+01 0.37527742E+01 0.35300984E+01 0.33232970E+01 0.31378868E+01 0.29788404E+01 0.28504405E+01 0.27561569E+01 0.26985435E+01 0.26791635E+01 display of periodogram smoothed with modified daniel filter starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 96.1647 - 323+ - i +2 +2 i 80.0000 - 2 ++ - i 2 2 i i + + i 60.0000 - 2 + - i + 2 i i + + i i + + i 40.0000 - + + - i + + i i + i i + + i i + + i i + i i + + +22 i i + + 22 +2+ i 20.0000 - + + 2 2 - i + ++ + i i + 2 2+ 2 i i + 3+ + i i + i i + + i i + + i 10.0000 - + - i + + 3232 i 8.0000 - + 2 2 - i + + ++ ++ i i + ++ + i i + + + + i 6.0000 - + + 2 2 +23 - i + + + 3+22 +2 2+ i i + + + 2 ++ ++ 2 i i + + + + 2 ++ 2 2 i 4.0000 - + + 2 ++ + + + - i + + + + ++ + + 2 i i + + 222 2 ++ 2 i i + + ++ + + ++ i i ++2 + + +2 i i + + + i i + + i 2.0000 - + + + - i + i i + + + i i + + i i + + + i i + + i i + + + i i + ++ i 1.0000 - + 22 - 0.8771 - 2 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 test of ipgmp starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - . +332322 - i . 22323+ i i . +3232323323232323+ i i . 23233+ i i 233323323 i 0.9000 - 223. - i +2323+. i i 23232332322 . i i 23 . i i +2+ . i 0.8000 - +2 . - i 2+ . i i 2+ . i i +3 . i i 23233+ . i 0.7000 - 23 . - i 2 . . i i ++ . . i i + . . i i + . . i 0.6000 - + . . - i + . . i i + . . i i + . . i i 2 . i 0.5000 - .+ . - i . + . i i . + . i i . . i i . + . i 0.4000 - . + . - i . + . i i . . i i . + . i i . + . i 0.3000 - + . - i + . i i . i i + . i i + . i 0.2000 - + . - i + . i i + . i i + . i i 2 . i 0.1000 - + . - i ++ . i i 2 . i i 2 . i i 23 . i 0.0000 - 22323323 . - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of ipgmps starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - . +332322 - i . 22323+ i i . +3232323323232323+ i i . 23233+ i i 233323323 i 0.9000 - 223. - i +2323+. i i 23232332322 . i i 23 . i i +2+ . i 0.8000 - +2 . - i 2+ . i i 2+ . i i +3 . i i 23233+ . i 0.7000 - 23 . - i 2 . . i i ++ . . i i + . . i i + . . i 0.6000 - + . . - i + . . i i + . . i i + . . i i 2 . i 0.5000 - .+ . - i . + . i i . + . i i . . i i . + . i 0.4000 - . + . - i . + . i i . . i i . + . i i . + . i 0.3000 - + . - i + . i i . i i + . i i + . i 0.2000 - + . - i + . i i + . i i + . i i 2 . i 0.1000 - + . - i ++ . i i 2 . i i 2 . i i 23 . i 0.0000 - 22323323 . - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.19531250E-02 0.39062500E-02 0.58593750E-02 0.78125000E-02 0.97656250E-02 0.11718750E-01 0.13671875E-01 0.15625000E-01 0.17578125E-01 0.19531250E-01 0.21484375E-01 0.23437500E-01 0.25390625E-01 0.27343750E-01 0.29296875E-01 0.31250000E-01 0.33203125E-01 0.35156250E-01 0.37109375E-01 0.39062500E-01 0.41015625E-01 0.42968750E-01 0.44921875E-01 0.46875000E-01 0.48828125E-01 0.50781250E-01 0.52734375E-01 0.54687500E-01 0.56640625E-01 0.58593750E-01 0.60546875E-01 0.62500000E-01 0.64453125E-01 0.66406250E-01 0.68359375E-01 0.70312500E-01 0.72265625E-01 0.74218750E-01 0.76171875E-01 0.78125000E-01 0.80078125E-01 0.82031250E-01 0.83984375E-01 0.85937500E-01 0.87890625E-01 0.89843750E-01 0.91796875E-01 0.93750000E-01 0.95703125E-01 0.97656250E-01 0.99609375E-01 0.10156250E+00 0.10351562E+00 0.10546875E+00 0.10742188E+00 0.10937500E+00 0.11132812E+00 0.11328125E+00 0.11523438E+00 0.11718750E+00 0.11914062E+00 0.12109375E+00 0.12304688E+00 0.12500000E+00 0.12695312E+00 0.12890625E+00 0.13085938E+00 0.13281250E+00 0.13476562E+00 0.13671875E+00 0.13867188E+00 0.14062500E+00 0.14257812E+00 0.14453125E+00 0.14648438E+00 0.14843750E+00 0.15039062E+00 0.15234375E+00 0.15429688E+00 0.15625000E+00 0.15820312E+00 0.16015625E+00 0.16210938E+00 0.16406250E+00 0.16601562E+00 0.16796875E+00 0.16992188E+00 0.17187500E+00 0.17382812E+00 0.17578125E+00 0.17773438E+00 0.17968750E+00 0.18164062E+00 0.18359375E+00 0.18554688E+00 0.18750000E+00 0.18945312E+00 0.19140625E+00 0.19335938E+00 0.19531250E+00 0.19726562E+00 0.19921875E+00 0.20117188E+00 0.20312500E+00 0.20507812E+00 0.20703125E+00 0.20898438E+00 0.21093750E+00 0.21289062E+00 0.21484375E+00 0.21679688E+00 0.21875000E+00 0.22070312E+00 0.22265625E+00 0.22460938E+00 0.22656250E+00 0.22851562E+00 0.23046875E+00 0.23242188E+00 0.23437500E+00 0.23632812E+00 0.23828125E+00 0.24023438E+00 0.24218750E+00 0.24414062E+00 0.24609375E+00 0.24804688E+00 0.25000000E+00 0.25195312E+00 0.25390625E+00 0.25585938E+00 0.25781250E+00 0.25976562E+00 0.26171875E+00 0.26367188E+00 0.26562500E+00 0.26757812E+00 0.26953125E+00 0.27148438E+00 0.27343750E+00 0.27539062E+00 0.27734375E+00 0.27929688E+00 0.28125000E+00 0.28320312E+00 0.28515625E+00 0.28710938E+00 0.28906250E+00 0.29101562E+00 0.29296875E+00 0.29492188E+00 0.29687500E+00 0.29882812E+00 0.30078125E+00 0.30273438E+00 0.30468750E+00 0.30664062E+00 0.30859375E+00 0.31054688E+00 0.31250000E+00 0.31445312E+00 0.31640625E+00 0.31835938E+00 0.32031250E+00 0.32226562E+00 0.32421875E+00 0.32617188E+00 0.32812500E+00 0.33007812E+00 0.33203125E+00 0.33398438E+00 0.33593750E+00 0.33789062E+00 0.33984375E+00 0.34179688E+00 0.34375000E+00 0.34570312E+00 0.34765625E+00 0.34960938E+00 0.35156250E+00 0.35351562E+00 0.35546875E+00 0.35742188E+00 0.35937500E+00 0.36132812E+00 0.36328125E+00 0.36523438E+00 0.36718750E+00 0.36914062E+00 0.37109375E+00 0.37304688E+00 0.37500000E+00 0.37695312E+00 0.37890625E+00 0.38085938E+00 0.38281250E+00 0.38476562E+00 0.38671875E+00 0.38867188E+00 0.39062500E+00 0.39257812E+00 0.39453125E+00 0.39648438E+00 0.39843750E+00 0.40039062E+00 0.40234375E+00 0.40429688E+00 0.40625000E+00 0.40820312E+00 0.41015625E+00 0.41210938E+00 0.41406250E+00 0.41601562E+00 0.41796875E+00 0.41992188E+00 0.42187500E+00 0.42382812E+00 0.42578125E+00 0.42773438E+00 0.42968750E+00 0.43164062E+00 0.43359375E+00 0.43554688E+00 0.43750000E+00 0.43945312E+00 0.44140625E+00 0.44335938E+00 0.44531250E+00 0.44726562E+00 0.44921875E+00 0.45117188E+00 0.45312500E+00 0.45507812E+00 0.45703125E+00 0.45898438E+00 0.46093750E+00 0.46289062E+00 0.46484375E+00 0.46679688E+00 0.46875000E+00 0.47070312E+00 0.47265625E+00 0.47460938E+00 0.47656250E+00 0.47851562E+00 0.48046875E+00 0.48242188E+00 0.48437500E+00 0.48632812E+00 0.48828125E+00 0.49023438E+00 0.49218750E+00 0.49414062E+00 0.49609375E+00 0.49804688E+00 0.50000000E+00 0.00000000E+00 0.62513777E-06 0.32581638E-05 0.96863605E-05 0.22394775E-04 0.44892211E-04 0.82079576E-04 0.14066596E-03 0.22963337E-03 0.36074553E-03 0.54909260E-03 0.81365567E-03 0.11778731E-02 0.16701822E-02 0.23245122E-02 0.31806948E-02 0.42847642E-02 0.56891120E-02 0.74524707E-02 0.96396981E-02 0.12321343E-01 0.15572963E-01 0.19474221E-01 0.24107706E-01 0.29557537E-01 0.35907749E-01 0.43240461E-01 0.51633928E-01 0.61160438E-01 0.71884163E-01 0.83859034E-01 0.97126633E-01 0.11171423E+00 0.12763299E+00 0.14487647E+00 0.16341934E+00 0.18321657E+00 0.20420302E+00 0.22629327E+00 0.24938229E+00 0.27334622E+00 0.29804379E+00 0.32331833E+00 0.34899992E+00 0.37490812E+00 0.40085506E+00 0.42664868E+00 0.45209613E+00 0.47700760E+00 0.50119960E+00 0.52449882E+00 0.54674518E+00 0.56779522E+00 0.58752471E+00 0.60583109E+00 0.62263536E+00 0.63788360E+00 0.65154749E+00 0.66362488E+00 0.67413920E+00 0.68313861E+00 0.69069463E+00 0.69690001E+00 0.70186627E+00 0.70572102E+00 0.70860457E+00 0.71066672E+00 0.71206313E+00 0.71295172E+00 0.71348929E+00 0.71382803E+00 0.71411228E+00 0.71447581E+00 0.71503919E+00 0.71590775E+00 0.71716994E+00 0.71889621E+00 0.72113848E+00 0.72392982E+00 0.72728491E+00 0.73120075E+00 0.73565817E+00 0.74062306E+00 0.74604851E+00 0.75187659E+00 0.75804120E+00 0.76446980E+00 0.77108604E+00 0.77781230E+00 0.78457147E+00 0.79128927E+00 0.79789597E+00 0.80432767E+00 0.81052792E+00 0.81644839E+00 0.82204950E+00 0.82730061E+00 0.83218008E+00 0.83667517E+00 0.84078115E+00 0.84450072E+00 0.84784311E+00 0.85082310E+00 0.85345984E+00 0.85577571E+00 0.85779548E+00 0.85954511E+00 0.86105078E+00 0.86233836E+00 0.86343247E+00 0.86435610E+00 0.86513060E+00 0.86577505E+00 0.86630666E+00 0.86674064E+00 0.86709064E+00 0.86736917E+00 0.86758775E+00 0.86775762E+00 0.86788994E+00 0.86799657E+00 0.86808985E+00 0.86818349E+00 0.86829227E+00 0.86843240E+00 0.86862111E+00 0.86887687E+00 0.86921865E+00 0.86966580E+00 0.87023735E+00 0.87095153E+00 0.87182510E+00 0.87287271E+00 0.87410611E+00 0.87553388E+00 0.87716055E+00 0.87898642E+00 0.88100725E+00 0.88321382E+00 0.88559234E+00 0.88812423E+00 0.89078659E+00 0.89355272E+00 0.89639252E+00 0.89927351E+00 0.90216142E+00 0.90502113E+00 0.90781802E+00 0.91051835E+00 0.91309059E+00 0.91550642E+00 0.91774118E+00 0.91977507E+00 0.92159331E+00 0.92318666E+00 0.92455196E+00 0.92569202E+00 0.92661524E+00 0.92733574E+00 0.92787260E+00 0.92824936E+00 0.92849338E+00 0.92863476E+00 0.92870539E+00 0.92873818E+00 0.92876589E+00 0.92882019E+00 0.92893070E+00 0.92912430E+00 0.92942399E+00 0.92984885E+00 0.93041313E+00 0.93112618E+00 0.93199205E+00 0.93301004E+00 0.93417424E+00 0.93547446E+00 0.93689638E+00 0.93842232E+00 0.94003165E+00 0.94170183E+00 0.94340914E+00 0.94512928E+00 0.94683850E+00 0.94851393E+00 0.95013440E+00 0.95168120E+00 0.95313805E+00 0.95449185E+00 0.95573300E+00 0.95685482E+00 0.95785427E+00 0.95873129E+00 0.95948893E+00 0.96013266E+00 0.96067017E+00 0.96111089E+00 0.96146542E+00 0.96174520E+00 0.96196193E+00 0.96212715E+00 0.96225202E+00 0.96234667E+00 0.96242034E+00 0.96248102E+00 0.96253544E+00 0.96258897E+00 0.96264583E+00 0.96270931E+00 0.96278191E+00 0.96286541E+00 0.96296161E+00 0.96307200E+00 0.96319854E+00 0.96334362E+00 0.96351057E+00 0.96370327E+00 0.96392697E+00 0.96418750E+00 0.96449178E+00 0.96484751E+00 0.96526283E+00 0.96574628E+00 0.96630615E+00 0.96695054E+00 0.96768647E+00 0.96851987E+00 0.96945494E+00 0.97049409E+00 0.97163731E+00 0.97288227E+00 0.97422379E+00 0.97565424E+00 0.97716302E+00 0.97873724E+00 0.98036164E+00 0.98201907E+00 0.98369080E+00 0.98535711E+00 0.98699784E+00 0.98859292E+00 0.99012303E+00 0.99157023E+00 0.99291861E+00 0.99415469E+00 0.99526793E+00 0.99625134E+00 0.99710137E+00 0.99781847E+00 0.99840683E+00 0.99887443E+00 0.99923301E+00 0.99949741E+00 0.99968541E+00 0.99981713E+00 0.99991435E+00 0.10000000E+01 test of ipgm starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - . + + - i . i i . + + i i + . i i . i 0.9000 - . - i . i i + + . i i . i i . i 0.8000 - . - i . i i . i i . i i . i 0.7000 - . - i + . . i i . . i i . . i i . . i 0.6000 - . . - i . . i i . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - . . - i . . i i . . i i . . i i . . i 0.3000 - . - i + . i i . i i . i i . i 0.2000 - . - i . i i . i i . i i . i 0.1000 - . - i . i i . i i . i i . i 0.0000 - + . - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of ipgms starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - . + + - i . i i . + + i i + . i i . i 0.9000 - . - i . i i + + . i i . i i . i 0.8000 - . - i . i i . i i . i i . i 0.7000 - . - i + . . i i . . i i . . i i . . i 0.6000 - . . - i . . i i . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - . . - i . . i i . . i i . . i i . . i 0.3000 - . - i + . i i . i i . i i . i 0.2000 - . - i . i i . i i . i i . i 0.1000 - . - i . i i . i i . i i . i 0.0000 - + . - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.55555556E-01 0.11111111E+00 0.16666667E+00 0.22222222E+00 0.27777779E+00 0.33333334E+00 0.38888890E+00 0.44444445E+00 0.50000000E+00 0.00000000E+00 0.28556630E+00 0.67878997E+00 0.85666776E+00 0.86957872E+00 0.94870859E+00 0.96338367E+00 0.96936172E+00 0.99756628E+00 0.10000000E+01 test of fftr ierr is 0 0.19073486E-05 0.00000000E+00 0.39383560E-01 -0.30869943E+00 0.15614659E+00 -0.60240304E+00 0.34618747E+00 -0.86663878E+00 0.60282683E+00 -0.10879457E+01 0.91705984E+00 -0.12543375E+01 0.12778735E+01 -0.13557104E+01 0.16726494E+01 -0.13841625E+01 0.20876012E+01 -0.13342621E+01 0.25082750E+01 -0.12031852E+01 0.29200459E+01 -0.99078834E+00 0.33086329E+01 -0.69955540E+00 0.36605697E+01 -0.33447722E+00 0.39636641E+01 0.97195134E-01 0.42073731E+01 0.58624554E+00 0.43831315E+01 0.11218756E+01 0.44845800E+01 0.16921252E+01 0.45077090E+01 0.22843418E+01 0.44509096E+01 0.28856552E+01 0.43149190E+01 0.34834471E+01 0.41026855E+01 0.40657978E+01 0.38191285E+01 0.46218991E+01 0.34708474E+01 0.51423898E+01 0.30657411E+01 0.56196375E+01 0.26126106E+01 0.60479269E+01 0.21207025E+01 0.64235497E+01 0.15992821E+01 0.67448130E+01 0.10571795E+01 0.70119448E+01 0.50240386E+00 0.72269087E+01 -0.58226466E-01 0.73931413E+01 -0.61935246E+00 0.75152102E+01 -0.11772959E+01 0.75984249E+01 -0.17301624E+01 0.76484127E+01 -0.22778749E+01 0.76706619E+01 -0.28220773E+01 0.76700821E+01 -0.33659673E+01 0.76505852E+01 -0.39140177E+01 0.76147051E+01 -0.44716339E+01 0.75632830E+01 -0.50447235E+01 0.74952307E+01 -0.56392279E+01 0.74073896E+01 -0.62606230E+01 0.72944946E+01 -0.69134030E+01 0.71492429E+01 -0.76005769E+01 0.69624677E+01 -0.83232079E+01 0.67234392E+01 -0.90800047E+01 0.64202509E+01 -0.98669949E+01 0.60402956E+01 -0.10677284E+02 0.55708237E+01 -0.11500939E+02 0.49995470E+01 -0.12324996E+02 0.43152857E+01 -0.13133570E+02 0.35085974E+01 -0.13908130E+02 0.25724251E+01 -0.14627880E+02 0.15026793E+01 -0.15270267E+02 0.29875159E+00 -0.15811589E+02 -0.10360625E+01 -0.16227699E+02 -0.24942408E+01 -0.16494770E+02 -0.40638466E+01 -0.16590117E+02 -0.57285070E+01 -0.16493011E+02 -0.74674997E+01 -0.16185509E+02 -0.92560301E+01 -0.15653212E+02 -0.11065641E+02 -0.14885994E+02 -0.12864786E+02 -0.13878580E+02 -0.14619514E+02 -0.12631057E+02 -0.16294277E+02 -0.11149206E+02 -0.17852842E+02 -0.94447031E+01 -0.19259251E+02 -0.75350890E+01 -0.20478821E+02 -0.54436674E+01 -0.21479168E+02 -0.31991239E+01 -0.22231174E+02 -0.83502388E+00 -0.22709940E+02 0.16108770E+01 -0.22895586E+02 0.40974355E+01 -0.22774006E+02 0.65810843E+01 -0.22337429E+02 0.90168695E+01 -0.21584827E+02 0.11359568E+02 -0.20522167E+02 0.13564825E+02 -0.19162413E+02 0.15590322E+02 -0.17525417E+02 0.17396891E+02 -0.15637537E+02 0.18949547E+02 -0.13531088E+02 0.20218472E+02 -0.11243652E+02 0.21179815E+02 -0.88171940E+01 0.21816383E+02 -0.62970552E+01 0.22118099E+02 -0.37308407E+01 0.22082348E+02 -0.11672544E+01 0.21714043E+02 0.13451335E+01 0.21025551E+02 0.37590940E+01 0.20036343E+02 0.60299406E+01 0.18772522E+02 0.81166401E+01 0.17266121E+02 0.99828300E+01 0.15554271E+02 0.11597719E+02 0.13678198E+02 0.12936809E+02 0.11682156E+02 0.13982454E+02 0.96122532E+01 0.14724236E+02 0.75152569E+01 0.15159119E+02 0.54373751E+01 0.15291412E+02 0.34230866E+01 0.15132534E+02 0.15140262E+01 0.14700583E+02 -0.25203383E+00 0.14019725E+02 -0.18420819E+01 0.13119419E+02 -0.32286124E+01 0.12033538E+02 -0.43902063E+01 0.10799362E+02 -0.53119259E+01 0.94564991E+01 -0.59855347E+01 0.80457668E+01 -0.64095068E+01 0.66080861E+01 -0.65888681E+01 0.51833720E+01 -0.65348406E+01 0.38095036E+01 -0.62643309E+01 0.25213649E+01 -0.57992849E+01 0.13500116E+01 -0.51659007E+01 0.32196164E+00 -0.43937612E+01 -0.54135883E+00 -0.35148892E+01 -0.12240072E+01 -0.25627789E+01 -0.17157550E+01 -0.15714099E+01 -0.20121329E+01 -0.57428789E+00 -0.21143217E+01 0.39646673E+00 -0.20288811E+01 0.13109508E+01 -0.17673428E+01 0.21422176E+01 -0.13456630E+01 0.28668878E+01 -0.78358614E+00 0.34656541E+01 -0.10391569E+00 0.39236212E+01 0.66825819E+00 0.42305160E+01 0.15063751E+01 0.43807569E+01 0.23832126E+01 0.43733668E+01 0.32716837E+01 0.42117834E+01 0.41455827E+01 0.39035258E+01 0.49802666E+01 0.34597793E+01 0.57532616E+01 0.28948772E+01 0.64447670E+01 0.22257471E+01 0.70380507E+01 0.14712925E+01 0.75197430E+01 0.65176260E+00 test of mdflt starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mdflt ------------------------------------- the values in the vector kmd must all be even. the next larger integer will be used in place of odd values. ierr is 0 0.87709588E+00 0.90655965E+00 0.99578261E+00 0.11472485E+01 0.13650585E+01 0.16548735E+01 0.20238295E+01 0.24804287E+01 0.30344000E+01 0.36965306E+01 0.44784646E+01 0.53924704E+01 0.64511728E+01 0.76672564E+01 0.90531359E+01 0.10620596E+02 0.12380415E+02 0.14341970E+02 0.16512829E+02 0.18898333E+02 0.21501217E+02 0.24321213E+02 0.27354713E+02 0.30594461E+02 0.34029297E+02 0.37643982E+02 0.41419090E+02 0.45330986E+02 0.49351894E+02 0.53450073E+02 0.57590103E+02 0.61733246E+02 0.65837952E+02 0.69860405E+02 0.73755203E+02 0.77476089E+02 0.80976776E+02 0.84211769E+02 0.87137245E+02 0.89711937E+02 0.91898033E+02 0.93661964E+02 0.94975189E+02 0.95814926E+02 0.96164734E+02 0.96014977E+02 0.95363220E+02 0.94214371E+02 0.92580826E+02 0.90482361E+02 0.87945755E+02 0.85004532E+02 0.81698303E+02 0.78072136E+02 0.74175667E+02 0.70062248E+02 0.65787895E+02 0.61410248E+02 0.56987446E+02 0.52577049E+02 0.48234951E+02 0.44014324E+02 0.39964622E+02 0.36130749E+02 0.32552246E+02 0.29262669E+02 0.26289085E+02 0.23651733E+02 0.21363821E+02 0.19431524E+02 0.17854095E+02 0.16624166E+02 0.15728156E+02 0.15146826E+02 0.14855938E+02 0.14827004E+02 0.15028100E+02 0.15424725E+02 0.15980684E+02 0.16658976E+02 0.17422636E+02 0.18235573E+02 0.19063299E+02 0.19873617E+02 0.20637186E+02 0.21328007E+02 0.21923786E+02 0.22406183E+02 0.22760958E+02 0.22977987E+02 0.23051197E+02 0.22978382E+02 0.22760967E+02 0.22403652E+02 0.21914047E+02 0.21302240E+02 0.20580339E+02 0.19762011E+02 0.18862019E+02 0.17895775E+02 0.16878931E+02 0.15826982E+02 0.14754953E+02 0.13677102E+02 0.12606705E+02 0.11555878E+02 0.10535463E+02 0.95549765E+01 0.86225739E+01 0.77450972E+01 0.69281263E+01 0.61760702E+01 0.54922709E+01 0.48791261E+01 0.43382068E+01 0.38703732E+01 0.34758816E+01 0.31544750E+01 0.29054525E+01 0.27277248E+01 0.26198382E+01 0.25799861E+01 0.26059952E+01 0.26953032E+01 0.28449168E+01 0.30513697E+01 0.33106756E+01 0.36182890E+01 0.39690709E+01 0.43572745E+01 0.47765465E+01 0.52199526E+01 0.56800270E+01 0.61488523E+01 0.66181574E+01 0.70794487E+01 0.75241594E+01 0.79438162E+01 0.83302279E+01 0.86756668E+01 0.89730682E+01 0.92162094E+01 0.93998928E+01 0.95200930E+01 0.95740957E+01 0.95605869E+01 0.94797325E+01 0.93331852E+01 0.91240892E+01 0.88570042E+01 0.85378237E+01 0.81736250E+01 0.77724996E+01 0.73433518E+01 0.68956628E+01 0.64392495E+01 0.59839993E+01 0.55396099E+01 0.51153355E+01 0.47197390E+01 0.43604722E+01 0.40440764E+01 0.37758222E+01 0.35595872E+01 0.33977702E+01 0.32912550E+01 0.32394161E+01 0.32401693E+01 0.32900629E+01 0.33844082E+01 0.35174432E+01 0.36825223E+01 0.38723302E+01 0.40791049E+01 0.42948742E+01 0.45116835E+01 0.47218199E+01 0.49180241E+01 0.50936728E+01 0.52429433E+01 0.53609419E+01 0.54438019E+01 0.54887514E+01 0.54941325E+01 0.54593983E+01 0.53850698E+01 0.52726703E+01 0.51246247E+01 0.49441442E+01 0.47350931E+01 0.45018435E+01 0.42491288E+01 0.39818935E+01 0.37051482E+01 0.34238410E+01 0.31427348E+01 0.28663080E+01 0.25986714E+01 0.23435066E+01 0.21040261E+01 0.18829551E+01 0.16825290E+01 0.15045114E+01 0.13502258E+01 0.12205958E+01 0.11161977E+01 0.10373124E+01 0.98397952E+00 0.95604944E+00 0.95322710E+00 0.97510982E+00 0.10212120E+01 0.10909810E+01 0.11837982E+01 0.12989725E+01 0.14357177E+01 0.15931275E+01 0.17701386E+01 0.19654924E+01 0.21776972E+01 0.24049907E+01 0.26453092E+01 0.28962662E+01 0.31551428E+01 0.34188888E+01 0.36841450E+01 0.39472766E+01 0.42044268E+01 0.44515839E+01 0.46846662E+01 0.48996153E+01 0.50925045E+01 0.52596493E+01 0.53977184E+01 0.55038514E+01 0.55757618E+01 0.56118326E+01 0.56111979E+01 0.55738034E+01 0.55004492E+01 0.53928046E+01 0.52533970E+01 0.50855813E+01 0.48934712E+01 0.46818528E+01 0.44560747E+01 0.42219119E+01 0.39854200E+01 0.37527742E+01 0.35300984E+01 0.33232970E+01 0.31378868E+01 0.29788404E+01 0.28504405E+01 0.27561569E+01 0.26985435E+01 0.26791635E+01 test of pgms starpac 2.08s (03/15/90) sample periodogram (in decibels) -25.4504 -20.8323 -16.2142 -11.5961 -6.9780 -2.3599 2.2583 6.8764 11.4945 16.1126 20.7307 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.19531E-02 i+ i -25.450 0.39062E-02 i + i -19.206 0.58594E-02 i + i -15.329 0.78125E-02 i + i -12.369 0.97656E-02 i + i -9.8888 0.11719E-01 i + i -7.7062 0.13672E-01 i + i -5.7322 0.15625E-01 i + i -3.9179 0.17578E-01 i + i -2.2337 0.19531E-01 i + i -.66058 0.21484E-01 i + i .81513 0.23438E-01 i + i 2.2034 0.25391E-01 i + i 3.5122 0.27344E-01 i + i 4.7478 0.29297E-01 i + i 5.9155 0.31250E-01 i + i 7.0198 0.33203E-01 i + i 8.0646 0.35156E-01 i + i 9.0532 0.37109E-01 i + i 9.9888 0.39062E-01 i + i 10.874 0.41016E-01 i + i 11.711 0.42969E-01 i + i 12.502 0.44922E-01 i + i 13.249 0.46875E-01 i + i 13.954 0.48828E-01 i + i 14.618 0.50781E-01 i + i 15.242 0.52734E-01 i + i 15.829 0.54688E-01 i + i 16.379 0.56641E-01 i + i 16.893 0.58594E-01 i + i 17.373 0.60547E-01 i + i 17.818 0.62500E-01 i + i 18.230 0.64453E-01 i + i 18.609 0.66406E-01 i + i 18.956 0.68359E-01 i + i 19.272 0.70312E-01 i + i 19.556 0.72266E-01 i + i 19.809 0.74219E-01 i + i 20.032 0.76172E-01 i + i 20.224 0.78125E-01 i + i 20.385 0.80078E-01 i +i 20.516 0.82031E-01 i +i 20.617 0.83984E-01 i +i 20.686 0.85938E-01 i +i 20.724 0.87891E-01 i +i 20.731 0.89844E-01 i +i 20.705 0.91797E-01 i +i 20.646 0.93750E-01 i +i 20.554 0.95703E-01 i + i 20.427 0.97656E-01 i + i 20.263 0.99609E-01 i + i 20.062 .10156 i + i 19.822 .10352 i + i 19.541 .10547 i + i 19.216 .10742 i + i 18.844 .10938 i + i 18.422 .11133 i + i 17.946 .11328 i + i 17.410 .11523 i + i 16.808 .11719 i + i 16.132 .11914 i + i 15.373 .12109 i + i 14.518 .12305 i + i 13.550 .12500 i + i 12.450 .12695 i + i 11.189 .12891 i + i 9.7330 .13086 i + i 8.0398 .13281 i + i 6.0769 .13477 i + i 3.8943 .13672 i + i 1.8884 .13867 i + i 1.1270 .14062 i + i 2.1951 .14258 i + i 4.0977 .14453 i + i 5.9777 .14648 i + i 7.6010 .14844 i + i 8.9610 .15039 i + i 10.097 .15234 i + i 11.048 .15430 i + i 11.847 .15625 i + i 12.518 .15820 i + i 13.081 .16016 i + i 13.549 .16211 i + i 13.934 .16406 i + i 14.245 .16602 i + i 14.489 .16797 i + i 14.671 .16992 i + i 14.796 .17188 i + i 14.868 .17383 i + i 14.889 .17578 i + i 14.862 .17773 i + i 14.790 .17969 i + i 14.673 .18164 i + i 14.514 .18359 i + i 14.313 .18555 i + i 14.073 .18750 i + i 13.792 .18945 i + i 13.474 .19141 i + i 13.117 .19336 i + i 12.724 .19531 i + i 12.295 .19727 i + i 11.830 .19922 i + i 11.332 .20117 i + i 10.800 .20312 i + i 10.237 .20508 i + i 9.6429 .20703 i + i 9.0193 .20898 i + i 8.3672 .21094 i + i 7.6875 .21289 i + i 6.9803 .21484 i + i 6.2451 .21680 i + i 5.4800 .21875 i + i 4.6818 .22070 i + i 3.8456 .22266 i + i 2.9645 .22461 i + i 2.0310 .22656 i + i 1.0385 .22852 i + i -0.13830E-01 .23047 i + i -1.1094 .23242 i + i -2.1926 .23438 i + i -3.1336 .23633 i + i -3.7110 .23828 i + i -3.6956 .24023 i + i -3.0436 .24219 i + i -1.9457 .24414 i + i -.65146 .24609 i + i .66779 .24805 i + i 1.9273 .25000 i + i 3.0943 .25195 i + i 4.1605 .25391 i + i 5.1281 .25586 i + i 6.0029 .25781 i + i 6.7917 .25977 i + i 7.5009 .26172 i + i 8.1363 .26367 i + i 8.7028 .26562 i + i 9.2046 .26758 i + i 9.6451 .26953 i + i 10.027 .27148 i + i 10.353 .27344 i + i 10.624 .27539 i + i 10.843 .27734 i + i 11.009 .27930 i + i 11.123 .28125 i + i 11.185 .28320 i + i 11.196 .28516 i + i 11.153 .28711 i + i 11.057 .28906 i + i 10.904 .29102 i + i 10.693 .29297 i + i 10.420 .29492 i + i 10.082 .29688 i + i 9.6731 .29883 i + i 9.1862 .30078 i + i 8.6131 .30273 i + i 7.9423 .30469 i + i 7.1589 .30664 i + i 6.2428 .30859 i + i 5.1660 .31055 i + i 3.8884 .31250 i + i 2.3511 .31445 i + i .46440 .31641 i + i -1.9072 .31836 i + i -4.9197 .32031 i + i -8.2521 .32227 i + i -8.9838 .32422 i + i -6.0625 .32617 i + i -2.9753 .32812 i + i -.54235 .33008 i + i 1.3571 .33203 i + i 2.8724 .33398 i + i 4.1048 .33594 i + i 5.1207 .33789 i + i 5.9646 .33984 i + i 6.6671 .34180 i + i 7.2502 .34375 i + i 7.7301 .34570 i + i 8.1186 .34766 i + i 8.4250 .34961 i + i 8.6562 .35156 i + i 8.8174 .35352 i + i 8.9128 .35547 i + i 8.9455 .35742 i + i 8.9178 .35938 i + i 8.8311 .36133 i + i 8.6864 .36328 i + i 8.4841 .36523 i + i 8.2240 .36719 i + i 7.9057 .36914 i + i 7.5279 .37109 i + i 7.0892 .37305 i + i 6.5875 .37500 i + i 6.0203 .37695 i + i 5.3846 .37891 i + i 4.6769 .38086 i + i 3.8937 .38281 i + i 3.0311 .38477 i + i 2.0861 .38672 i + i 1.0575 .38867 i + i -0.51639E-01 .39062 i + i -1.2295 .39258 i + i -2.4475 .39453 i + i -3.6491 .39648 i + i -4.7374 .39844 i + i -5.5808 .40039 i + i -6.0574 .40234 i + i -6.1276 .40430 i + i -5.8608 .40625 i + i -5.3817 .40820 i + i -4.8016 .41016 i + i -4.1903 .41211 i + i -3.5807 .41406 i + i -2.9813 .41602 i + i -2.3883 .41797 i + i -1.7926 .41992 i + i -1.1852 .42188 i + i -.55982 .42383 i + i 0.85483E-01 .42578 i + i .74833 .42773 i + i 1.4229 .42969 i + i 2.1011 .43164 i + i 2.7741 .43359 i + i 3.4333 .43555 i + i 4.0712 .43750 i + i 4.6811 .43945 i + i 5.2581 .44141 i + i 5.7981 .44336 i + i 6.2983 .44531 i + i 6.7565 .44727 i + i 7.1712 .44922 i + i 7.5412 .45117 i + i 7.8658 .45312 i + i 8.1443 .45508 i + i 8.3761 .45703 i + i 8.5605 .45898 i + i 8.6968 .46094 i + i 8.7842 .46289 i + i 8.8215 .46484 i + i 8.8074 .46680 i + i 8.7401 .46875 i + i 8.6175 .47070 i + i 8.4370 .47266 i + i 8.1952 .47461 i + i 7.8879 .47656 i + i 7.5103 .47852 i + i 7.0560 .48047 i + i 6.5172 .48242 i + i 5.8843 .48438 i + i 5.1455 .48633 i + i 4.2860 .48828 i + i 3.2887 .49023 i + i 2.1354 .49219 i + i .81256 .49414 i + i -.66790 .49609 i + i -2.2128 .49805 i + i -3.5304 .50000 i + i -4.0824 ierr is 0 0.00000000E+00 0.19531250E-02 0.39062500E-02 0.58593750E-02 0.78125000E-02 0.97656250E-02 0.11718750E-01 0.13671875E-01 0.15625000E-01 0.17578125E-01 0.19531250E-01 0.21484375E-01 0.23437500E-01 0.25390625E-01 0.27343750E-01 0.29296875E-01 0.31250000E-01 0.33203125E-01 0.35156250E-01 0.37109375E-01 0.39062500E-01 0.41015625E-01 0.42968750E-01 0.44921875E-01 0.46875000E-01 0.48828125E-01 0.50781250E-01 0.52734375E-01 0.54687500E-01 0.56640625E-01 0.58593750E-01 0.60546875E-01 0.62500000E-01 0.64453125E-01 0.66406250E-01 0.68359375E-01 0.70312500E-01 0.72265625E-01 0.74218750E-01 0.76171875E-01 0.78125000E-01 0.80078125E-01 0.82031250E-01 0.83984375E-01 0.85937500E-01 0.87890625E-01 0.89843750E-01 0.91796875E-01 0.93750000E-01 0.95703125E-01 0.97656250E-01 0.99609375E-01 0.10156250E+00 0.10351562E+00 0.10546875E+00 0.10742188E+00 0.10937500E+00 0.11132812E+00 0.11328125E+00 0.11523438E+00 0.11718750E+00 0.11914062E+00 0.12109375E+00 0.12304688E+00 0.12500000E+00 0.12695312E+00 0.12890625E+00 0.13085938E+00 0.13281250E+00 0.13476562E+00 0.13671875E+00 0.13867188E+00 0.14062500E+00 0.14257812E+00 0.14453125E+00 0.14648438E+00 0.14843750E+00 0.15039062E+00 0.15234375E+00 0.15429688E+00 0.15625000E+00 0.15820312E+00 0.16015625E+00 0.16210938E+00 0.16406250E+00 0.16601562E+00 0.16796875E+00 0.16992188E+00 0.17187500E+00 0.17382812E+00 0.17578125E+00 0.17773438E+00 0.17968750E+00 0.18164062E+00 0.18359375E+00 0.18554688E+00 0.18750000E+00 0.18945312E+00 0.19140625E+00 0.19335938E+00 0.19531250E+00 0.19726562E+00 0.19921875E+00 0.20117188E+00 0.20312500E+00 0.20507812E+00 0.20703125E+00 0.20898438E+00 0.21093750E+00 0.21289062E+00 0.21484375E+00 0.21679688E+00 0.21875000E+00 0.22070312E+00 0.22265625E+00 0.22460938E+00 0.22656250E+00 0.22851562E+00 0.23046875E+00 0.23242188E+00 0.23437500E+00 0.23632812E+00 0.23828125E+00 0.24023438E+00 0.24218750E+00 0.24414062E+00 0.24609375E+00 0.24804688E+00 0.25000000E+00 0.25195312E+00 0.25390625E+00 0.25585938E+00 0.25781250E+00 0.25976562E+00 0.26171875E+00 0.26367188E+00 0.26562500E+00 0.26757812E+00 0.26953125E+00 0.27148438E+00 0.27343750E+00 0.27539062E+00 0.27734375E+00 0.27929688E+00 0.28125000E+00 0.28320312E+00 0.28515625E+00 0.28710938E+00 0.28906250E+00 0.29101562E+00 0.29296875E+00 0.29492188E+00 0.29687500E+00 0.29882812E+00 0.30078125E+00 0.30273438E+00 0.30468750E+00 0.30664062E+00 0.30859375E+00 0.31054688E+00 0.31250000E+00 0.31445312E+00 0.31640625E+00 0.31835938E+00 0.32031250E+00 0.32226562E+00 0.32421875E+00 0.32617188E+00 0.32812500E+00 0.33007812E+00 0.33203125E+00 0.33398438E+00 0.33593750E+00 0.33789062E+00 0.33984375E+00 0.34179688E+00 0.34375000E+00 0.34570312E+00 0.34765625E+00 0.34960938E+00 0.35156250E+00 0.35351562E+00 0.35546875E+00 0.35742188E+00 0.35937500E+00 0.36132812E+00 0.36328125E+00 0.36523438E+00 0.36718750E+00 0.36914062E+00 0.37109375E+00 0.37304688E+00 0.37500000E+00 0.37695312E+00 0.37890625E+00 0.38085938E+00 0.38281250E+00 0.38476562E+00 0.38671875E+00 0.38867188E+00 0.39062500E+00 0.39257812E+00 0.39453125E+00 0.39648438E+00 0.39843750E+00 0.40039062E+00 0.40234375E+00 0.40429688E+00 0.40625000E+00 0.40820312E+00 0.41015625E+00 0.41210938E+00 0.41406250E+00 0.41601562E+00 0.41796875E+00 0.41992188E+00 0.42187500E+00 0.42382812E+00 0.42578125E+00 0.42773438E+00 0.42968750E+00 0.43164062E+00 0.43359375E+00 0.43554688E+00 0.43750000E+00 0.43945312E+00 0.44140625E+00 0.44335938E+00 0.44531250E+00 0.44726562E+00 0.44921875E+00 0.45117188E+00 0.45312500E+00 0.45507812E+00 0.45703125E+00 0.45898438E+00 0.46093750E+00 0.46289062E+00 0.46484375E+00 0.46679688E+00 0.46875000E+00 0.47070312E+00 0.47265625E+00 0.47460938E+00 0.47656250E+00 0.47851562E+00 0.48046875E+00 0.48242188E+00 0.48437500E+00 0.48632812E+00 0.48828125E+00 0.49023438E+00 0.49218750E+00 0.49414062E+00 0.49609375E+00 0.49804688E+00 0.50000000E+00 0.33177600E+06 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0.56246343E+01 0.65108027E+01 0.74179654E+01 0.83264627E+01 0.92152557E+01 0.10062549E+02 0.10846479E+02 0.11545861E+02 0.12140914E+02 0.12613958E+02 0.12950100E+02 0.13137803E+02 0.13169350E+02 0.13041233E+02 0.12754349E+02 0.12314070E+02 0.11730172E+02 0.11016622E+02 0.10191159E+02 0.92748117E+01 0.82912722E+01 0.72661610E+01 0.62262702E+01 0.51986942E+01 0.42100148E+01 0.32854669E+01 0.24481528E+01 0.17183408E+01 0.11128588E+01 0.64459199E+00 0.32212821E+00 0.14954963E+00 0.12636422E+00 0.24759990E+00 0.50404084E+00 0.88260162E+00 0.13668244E+01 0.19374820E+01 0.25732415E+01 0.32514157E+01 0.39487200E+01 0.46420298E+01 0.53091226E+01 0.59293647E+01 0.64843187E+01 0.69582748E+01 0.73386354E+01 0.76162305E+01 0.77854481E+01 0.78443031E+01 0.77942934E+01 0.76402211E+01 0.73898511E+01 0.70535135E+01 0.66435976E+01 0.61740317E+01 0.56597080E+01 0.51159143E+01 0.45577755E+01 0.39997327E+01 0.34550784E+01 0.29355736E+01 0.24511275E+01 0.20095863E+01 0.16166192E+01 0.12757022E+01 0.98818016E+00 0.75344837E+00 0.56917429E+00 0.43161210E+00 0.33594054E+00 0.27664346E+00 0.24788980E+00 0.24391574E+00 0.25936967E+00 0.28962132E+00 0.33101144E+00 0.38103813E+00 0.43846500E+00 0.50334996E+00 0.57699269E+00 0.66181529E+00 0.76116675E+00 0.87905836E+00 0.10198783E+01 0.11880462E+01 0.13876699E+01 0.16222099E+01 0.18941277E+01 0.22046261E+01 0.25533850E+01 0.29384089E+01 0.33558969E+01 0.38002563E+01 0.42641339E+01 0.47386012E+01 0.52133589E+01 0.56770453E+01 0.61176100E+01 0.65227385E+01 0.68802695E+01 0.71786985E+01 0.74076195E+01 0.75581646E+01 0.76234016E+01 0.75986776E+01 0.74818964E+01 0.72736917E+01 0.69774952E+01 0.65995693E+01 0.61488514E+01 0.56367579E+01 0.50768700E+01 0.44845424E+01 0.38764286E+01 0.32699809E+01 0.26828718E+01 0.21324286E+01 0.16350691E+01 0.12057465E+01 0.85745335E+00 0.60078323E+00 0.44356993E+00 0.39062500E+00 test of pgms starpac 2.08s (03/15/90) sample periodogram 0.285E-02 0.100E-01 0.500E-01 0.500E+00 0.500E+01 0.500E+02 -i----i------i--------------i-----i---------------i-----i--------------i------i--------------i-----i-i- 0.19531E-02 i+ i 0.28507E-02 0.39062E-02 i + i 0.12007E-01 0.58594E-02 i + i 0.29314E-01 0.78125E-02 i + i 0.57953E-01 0.97656E-02 i + i .10259 0.11719E-01 i + i .16958 0.13672E-01 i + i .26717 0.15625E-01 i + i .40571 0.17578E-01 i + i .59790 0.19531E-01 i + i .85890 0.21484E-01 i + i 1.2065 0.23438E-01 i + i 1.6609 0.25391E-01 i + i 2.2450 0.27344E-01 i + i 2.9839 0.29297E-01 i + i 3.9044 0.31250E-01 i + i 5.0348 0.33203E-01 i + i 6.4041 0.35156E-01 i + i 8.0413 0.37109E-01 i + i 9.9742 0.39062E-01 i + i 12.229 0.41016E-01 i + i 14.828 0.42969E-01 i + i 17.790 0.44922E-01 i + i 21.130 0.46875E-01 i + i 24.852 0.48828E-01 i + i 28.958 0.50781E-01 i + i 33.439 0.52734E-01 i + i 38.276 0.54688E-01 i + i 43.443 0.56641E-01 i + i 48.902 0.58594E-01 i + i 54.608 0.60547E-01 i + i 60.503 0.62500E-01 i + i 66.522 0.64453E-01 i + i 72.593 0.66406E-01 i + i 78.634 0.68359E-01 i + i 84.559 0.70312E-01 i + i 90.279 0.72266E-01 i + i 95.702 0.74219E-01 i + i 100.74 0.76172E-01 i + i 105.29 0.78125E-01 i + i 109.28 0.80078E-01 i +i 112.63 0.82031E-01 i +i 115.26 0.83984E-01 i +i 117.11 0.85938E-01 i +i 118.15 0.87891E-01 i +i 118.32 0.89844E-01 i +i 117.62 0.91797E-01 i +i 116.05 0.93750E-01 i +i 113.60 0.95703E-01 i + i 110.32 0.97656E-01 i + i 106.25 0.99609E-01 i + i 101.45 .10156 i + i 95.992 .10352 i + i 89.970 .10547 i + i 83.481 .10742 i + i 76.631 .10938 i + i 69.535 .11133 i + i 62.310 .11328 i + i 55.075 .11523 i + i 47.947 .11719 i + i 41.039 .11914 i + i 34.457 .12109 i + i 28.298 .12305 i + i 22.647 .12500 i + i 17.578 .12695 i + i 13.150 .12891 i + i 9.4037 .13086 i + i 6.3677 .13281 i + i 4.0522 .13477 i + i 2.4515 .13672 i + i 1.5447 .13867 i + i 1.2963 .14062 i + i 1.6577 .14258 i + i 2.5690 .14453 i + i 3.9607 .14648 i + i 5.7558 .14844 i + i 7.8723 .15039 i + i 10.225 .15234 i + i 12.729 .15430 i + i 15.300 .15625 i + i 17.857 .15820 i + i 20.327 .16016 i 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0.45117188E+00 0.45312500E+00 0.45507812E+00 0.45703125E+00 0.45898438E+00 0.46093750E+00 0.46289062E+00 0.46484375E+00 0.46679688E+00 0.46875000E+00 0.47070312E+00 0.47265625E+00 0.47460938E+00 0.47656250E+00 0.47851562E+00 0.48046875E+00 0.48242188E+00 0.48437500E+00 0.48632812E+00 0.48828125E+00 0.49023438E+00 0.49218750E+00 0.49414062E+00 0.49609375E+00 0.49804688E+00 0.50000000E+00 0.33177600E+06 0.28507500E-02 0.12007113E-01 0.29313829E-01 0.57952851E-01 0.10259269E+00 0.16958161E+00 0.26716533E+00 0.40570873E+00 0.59789699E+00 0.85889941E+00 0.12064593E+01 0.16609023E+01 0.22450256E+01 0.29838722E+01 0.39043601E+01 0.50347719E+01 0.64040980E+01 0.80412579E+01 0.99741840E+01 0.12228821E+02 0.14828029E+02 0.17790495E+02 0.21129591E+02 0.24852301E+02 0.28958199E+02 0.33438599E+02 0.38275829E+02 0.43442719E+02 0.48902267E+02 0.54607758E+02 0.60502850E+02 0.66522293E+02 0.72592667E+02 0.78633583E+02 0.84559097E+02 0.90279266E+02 0.95702240E+02 0.10073590E+03 0.10529043E+03 0.10928012E+03 0.11262579E+03 0.11525682E+03 0.11711299E+03 0.11814645E+03 0.11832311E+03 0.11762386E+03 0.11604546E+03 0.11360113E+03 0.11032037E+03 0.10624893E+03 0.10144781E+03 0.95992287E+02 0.89970291E+02 0.83480690E+02 0.76630943E+02 0.69534882E+02 0.62310028E+02 0.55075104E+02 0.47947151E+02 0.41039059E+02 0.34456909E+02 0.28297695E+02 0.22647236E+02 0.17578394E+02 0.13149561E+02 0.94037199E+01 0.63677340E+01 0.40521727E+01 0.24514885E+01 0.15446819E+01 0.12962836E+01 0.16577318E+01 0.25690355E+01 0.39606910E+01 0.57557893E+01 0.78722634E+01 0.10225159E+02 0.12728948E+02 0.15299696E+02 0.17857187E+02 0.20326756E+02 0.22640976E+02 0.24740967E+02 0.26577511E+02 0.28111725E+02 0.29315495E+02 0.30171499E+02 0.30672981E+02 0.30823187E+02 0.30634537E+02 0.30127596E+02 0.29329874E+02 0.28274426E+02 0.26998476E+02 0.25541920E+02 0.23945938E+02 0.22251579E+02 0.20498541E+02 0.18724051E+02 0.16961977E+02 0.15242003E+02 0.13589252E+02 0.12023832E+02 0.10560886E+02 0.92106123E+01 0.79785981E+01 0.68662729E+01 0.58714962E+01 0.49892049E+01 0.42121925E+01 0.35318432E+01 0.29388981E+01 0.24241352E+01 0.19790285E+01 0.15962586E+01 0.12701257E+01 0.99682051E+00 0.77456254E+00 0.60358852E+00 0.48600242E+00 0.42550346E+00 0.42700785E+00 0.49618000E+00 0.63889933E+00 0.86070418E+00 0.11662152E+01 0.15585966E+01 0.20390620E+01 0.26064506E+01 0.32569165E+01 0.39837232E+01 0.47771668E+01 0.56246343E+01 0.65108027E+01 0.74179654E+01 0.83264627E+01 0.92152557E+01 0.10062549E+02 0.10846479E+02 0.11545861E+02 0.12140914E+02 0.12613958E+02 0.12950100E+02 0.13137803E+02 0.13169350E+02 0.13041233E+02 0.12754349E+02 0.12314070E+02 0.11730172E+02 0.11016622E+02 0.10191159E+02 0.92748117E+01 0.82912722E+01 0.72661610E+01 0.62262702E+01 0.51986942E+01 0.42100148E+01 0.32854669E+01 0.24481528E+01 0.17183408E+01 0.11128588E+01 0.64459199E+00 0.32212821E+00 0.14954963E+00 0.12636422E+00 0.24759990E+00 0.50404084E+00 0.88260162E+00 0.13668244E+01 0.19374820E+01 0.25732415E+01 0.32514157E+01 0.39487200E+01 0.46420298E+01 0.53091226E+01 0.59293647E+01 0.64843187E+01 0.69582748E+01 0.73386354E+01 0.76162305E+01 0.77854481E+01 0.78443031E+01 0.77942934E+01 0.76402211E+01 0.73898511E+01 0.70535135E+01 0.66435976E+01 0.61740317E+01 0.56597080E+01 0.51159143E+01 0.45577755E+01 0.39997327E+01 0.34550784E+01 0.29355736E+01 0.24511275E+01 0.20095863E+01 0.16166192E+01 0.12757022E+01 0.98818016E+00 0.75344837E+00 0.56917429E+00 0.43161210E+00 0.33594054E+00 0.27664346E+00 0.24788980E+00 0.24391574E+00 0.25936967E+00 0.28962132E+00 0.33101144E+00 0.38103813E+00 0.43846500E+00 0.50334996E+00 0.57699269E+00 0.66181529E+00 0.76116675E+00 0.87905836E+00 0.10198783E+01 0.11880462E+01 0.13876699E+01 0.16222099E+01 0.18941277E+01 0.22046261E+01 0.25533850E+01 0.29384089E+01 0.33558969E+01 0.38002563E+01 0.42641339E+01 0.47386012E+01 0.52133589E+01 0.56770453E+01 0.61176100E+01 0.65227385E+01 0.68802695E+01 0.71786985E+01 0.74076195E+01 0.75581646E+01 0.76234016E+01 0.75986776E+01 0.74818964E+01 0.72736917E+01 0.69774952E+01 0.65995693E+01 0.61488514E+01 0.56367579E+01 0.50768700E+01 0.44845424E+01 0.38764286E+01 0.32699809E+01 0.26828718E+01 0.21324286E+01 0.16350691E+01 0.12057465E+01 0.85745335E+00 0.60078323E+00 0.44356993E+00 0.39062500E+00 test of fftr ierr is 0 0.42438507E-04 0.00000000E+00 0.39382726E-01 -0.30869931E+00 0.15614563E+00 -0.60240352E+00 0.34618628E+00 -0.86663902E+00 0.60282522E+00 -0.10879463E+01 0.91705859E+00 -0.12543377E+01 0.12778726E+01 -0.13557109E+01 0.16726489E+01 -0.13841631E+01 0.20876005E+01 -0.13342631E+01 0.25082746E+01 -0.12031860E+01 0.29200451E+01 -0.99078870E+00 0.33086329E+01 -0.69955617E+00 0.36605687E+01 -0.33447883E+00 0.39636636E+01 0.97194120E-01 0.42073727E+01 0.58624524E+00 0.43831310E+01 0.11218750E+01 0.44845791E+01 0.16921239E+01 0.45077095E+01 0.22843416E+01 0.44509096E+01 0.28856544E+01 0.43149195E+01 0.34834464E+01 0.41026850E+01 0.40657964E+01 0.38191290E+01 0.46218987E+01 0.34708478E+01 0.51423893E+01 0.30657406E+01 0.56196375E+01 0.26126108E+01 0.60479259E+01 0.21207027E+01 0.64235492E+01 0.15992823E+01 0.67448130E+01 0.10571789E+01 0.70119438E+01 0.50240397E+00 0.72269082E+01 -0.58226585E-01 0.73931408E+01 -0.61935258E+00 0.75152097E+01 -0.11772957E+01 0.75984249E+01 -0.17301626E+01 0.76484127E+01 -0.22778745E+01 0.76706610E+01 -0.28220778E+01 0.76700821E+01 -0.33659668E+01 0.76505852E+01 -0.39140186E+01 0.76147060E+01 -0.44716339E+01 0.75632830E+01 -0.50447235E+01 0.74952297E+01 -0.56392279E+01 0.74073877E+01 -0.62606235E+01 0.72944956E+01 -0.69134026E+01 0.71492424E+01 -0.76005769E+01 0.69624672E+01 -0.83232079E+01 0.67234387E+01 -0.90800056E+01 0.64202523E+01 -0.98669949E+01 0.60402946E+01 -0.10677284E+02 0.55708232E+01 -0.11500939E+02 0.49995475E+01 -0.12324997E+02 0.43152852E+01 -0.13133570E+02 0.35085976E+01 -0.13908130E+02 0.25724256E+01 -0.14627881E+02 0.15026799E+01 -0.15270267E+02 0.29875159E+00 -0.15811589E+02 -0.10360622E+01 -0.16227697E+02 -0.24942408E+01 -0.16494770E+02 -0.40638475E+01 -0.16590117E+02 -0.57285061E+01 -0.16493011E+02 -0.74675002E+01 -0.16185507E+02 -0.92560291E+01 -0.15653212E+02 -0.11065639E+02 -0.14885996E+02 -0.12864786E+02 -0.13878580E+02 -0.14619513E+02 -0.12631057E+02 -0.16294277E+02 -0.11149206E+02 -0.17852842E+02 -0.94447021E+01 -0.19259251E+02 -0.75350890E+01 -0.20478821E+02 -0.54436674E+01 -0.21479168E+02 -0.31991248E+01 -0.22231174E+02 -0.83502388E+00 -0.22709940E+02 0.16108761E+01 -0.22895586E+02 0.40974364E+01 -0.22774006E+02 0.65810843E+01 -0.22337429E+02 0.90168705E+01 -0.21584831E+02 0.11359566E+02 -0.20522167E+02 0.13564825E+02 -0.19162413E+02 0.15590322E+02 -0.17525417E+02 0.17396891E+02 -0.15637537E+02 0.18949551E+02 -0.13531090E+02 0.20218472E+02 -0.11243653E+02 0.21179817E+02 -0.88171949E+01 0.21816383E+02 -0.62970557E+01 0.22118099E+02 -0.37308414E+01 0.22082346E+02 -0.11672537E+01 0.21714046E+02 0.13451328E+01 0.21025553E+02 0.37590947E+01 0.20036346E+02 0.60299397E+01 0.18772522E+02 0.81166382E+01 0.17266121E+02 0.99828281E+01 0.15554271E+02 0.11597719E+02 0.13678198E+02 0.12936808E+02 0.11682156E+02 0.13982454E+02 0.96122532E+01 0.14724236E+02 0.75152574E+01 0.15159121E+02 0.54373755E+01 0.15291412E+02 0.34230866E+01 0.15132534E+02 0.15140263E+01 0.14700583E+02 -0.25203359E+00 0.14019726E+02 -0.18420815E+01 0.13119419E+02 -0.32286124E+01 0.12033538E+02 -0.43902059E+01 0.10799361E+02 -0.53119273E+01 0.94564981E+01 -0.59855347E+01 0.80457668E+01 -0.64095068E+01 0.66080861E+01 -0.65888681E+01 0.51833715E+01 -0.65348396E+01 0.38095036E+01 -0.62643309E+01 0.25213642E+01 -0.57992854E+01 0.13500118E+01 -0.51659007E+01 0.32196188E+00 -0.43937607E+01 -0.54135919E+00 -0.35148888E+01 -0.12240075E+01 -0.25627789E+01 -0.17157547E+01 -0.15714102E+01 -0.20121324E+01 -0.57428861E+00 -0.21143222E+01 0.39646745E+00 -0.20288815E+01 0.13109508E+01 -0.17673428E+01 0.21422176E+01 -0.13456628E+01 0.28668890E+01 -0.78358722E+00 0.34656539E+01 -0.10391521E+00 0.39236195E+01 0.66825843E+00 0.42305164E+01 0.15063753E+01 0.43807554E+01 0.23832123E+01 0.43733673E+01 0.32716842E+01 0.42117825E+01 0.41455812E+01 0.39035249E+01 0.49802656E+01 0.34597793E+01 0.57532616E+01 0.28948777E+01 0.64447680E+01 0.22257466E+01 0.70380502E+01 0.14712920E+01 0.75197430E+01 0.65176356E+00 test of fftr starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine fftr ------------------------------------- the input value of the parameter nfft ( 513) does not meet the requirements of singletons fft code. the next larger value which does is 514. however, the value 514 exceeds the length lyfft ( 512) of the vector yfft, and therefore cannot be used as the extended series length without redimensioning yfft. the input value of lab is 512. the length of ab , as indicated by the argument lab , must be greater than or equal to 514. the correct form of the call statement is call fftr (yfft, n, nfft, iextnd, nf, ab, lab) ierr is 1 0.42438507E-04 0.00000000E+00 0.39382726E-01 -0.30869931E+00 0.15614563E+00 -0.60240352E+00 0.34618628E+00 -0.86663902E+00 0.60282522E+00 -0.10879463E+01 0.91705859E+00 -0.12543377E+01 0.12778726E+01 -0.13557109E+01 0.16726489E+01 -0.13841631E+01 0.20876005E+01 -0.13342631E+01 0.25082746E+01 -0.12031860E+01 0.29200451E+01 -0.99078870E+00 0.33086329E+01 -0.69955617E+00 0.36605687E+01 -0.33447883E+00 0.39636636E+01 0.97194120E-01 0.42073727E+01 0.58624524E+00 0.43831310E+01 0.11218750E+01 0.44845791E+01 0.16921239E+01 0.45077095E+01 0.22843416E+01 0.44509096E+01 0.28856544E+01 0.43149195E+01 0.34834464E+01 0.41026850E+01 0.40657964E+01 0.38191290E+01 0.46218987E+01 0.34708478E+01 0.51423893E+01 0.30657406E+01 0.56196375E+01 0.26126108E+01 0.60479259E+01 0.21207027E+01 0.64235492E+01 0.15992823E+01 0.67448130E+01 0.10571789E+01 0.70119438E+01 0.50240397E+00 0.72269082E+01 -0.58226585E-01 0.73931408E+01 -0.61935258E+00 0.75152097E+01 -0.11772957E+01 0.75984249E+01 -0.17301626E+01 0.76484127E+01 -0.22778745E+01 0.76706610E+01 -0.28220778E+01 0.76700821E+01 -0.33659668E+01 0.76505852E+01 -0.39140186E+01 0.76147060E+01 -0.44716339E+01 0.75632830E+01 -0.50447235E+01 0.74952297E+01 -0.56392279E+01 0.74073877E+01 -0.62606235E+01 0.72944956E+01 -0.69134026E+01 0.71492424E+01 -0.76005769E+01 0.69624672E+01 -0.83232079E+01 0.67234387E+01 -0.90800056E+01 0.64202523E+01 -0.98669949E+01 0.60402946E+01 -0.10677284E+02 0.55708232E+01 -0.11500939E+02 0.49995475E+01 -0.12324997E+02 0.43152852E+01 -0.13133570E+02 0.35085976E+01 -0.13908130E+02 0.25724256E+01 -0.14627881E+02 0.15026799E+01 -0.15270267E+02 0.29875159E+00 -0.15811589E+02 -0.10360622E+01 -0.16227697E+02 -0.24942408E+01 -0.16494770E+02 -0.40638475E+01 -0.16590117E+02 -0.57285061E+01 -0.16493011E+02 -0.74675002E+01 -0.16185507E+02 -0.92560291E+01 -0.15653212E+02 -0.11065639E+02 -0.14885996E+02 -0.12864786E+02 -0.13878580E+02 -0.14619513E+02 -0.12631057E+02 -0.16294277E+02 -0.11149206E+02 -0.17852842E+02 -0.94447021E+01 -0.19259251E+02 -0.75350890E+01 -0.20478821E+02 -0.54436674E+01 -0.21479168E+02 -0.31991248E+01 -0.22231174E+02 -0.83502388E+00 -0.22709940E+02 0.16108761E+01 -0.22895586E+02 0.40974364E+01 -0.22774006E+02 0.65810843E+01 -0.22337429E+02 0.90168705E+01 -0.21584831E+02 0.11359566E+02 -0.20522167E+02 0.13564825E+02 -0.19162413E+02 0.15590322E+02 -0.17525417E+02 0.17396891E+02 -0.15637537E+02 0.18949551E+02 -0.13531090E+02 0.20218472E+02 -0.11243653E+02 0.21179817E+02 -0.88171949E+01 0.21816383E+02 -0.62970557E+01 0.22118099E+02 -0.37308414E+01 0.22082346E+02 -0.11672537E+01 0.21714046E+02 0.13451328E+01 0.21025553E+02 0.37590947E+01 0.20036346E+02 0.60299397E+01 0.18772522E+02 0.81166382E+01 0.17266121E+02 0.99828281E+01 0.15554271E+02 0.11597719E+02 0.13678198E+02 0.12936808E+02 0.11682156E+02 0.13982454E+02 0.96122532E+01 0.14724236E+02 0.75152574E+01 0.15159121E+02 0.54373755E+01 0.15291412E+02 0.34230866E+01 0.15132534E+02 0.15140263E+01 0.14700583E+02 -0.25203359E+00 0.14019726E+02 -0.18420815E+01 0.13119419E+02 -0.32286124E+01 0.12033538E+02 -0.43902059E+01 0.10799361E+02 -0.53119273E+01 0.94564981E+01 -0.59855347E+01 0.80457668E+01 -0.64095068E+01 0.66080861E+01 -0.65888681E+01 0.51833715E+01 -0.65348396E+01 0.38095036E+01 -0.62643309E+01 0.25213642E+01 -0.57992854E+01 0.13500118E+01 -0.51659007E+01 0.32196188E+00 -0.43937607E+01 -0.54135919E+00 -0.35148888E+01 -0.12240075E+01 -0.25627789E+01 -0.17157547E+01 -0.15714102E+01 -0.20121324E+01 -0.57428861E+00 -0.21143222E+01 0.39646745E+00 -0.20288815E+01 0.13109508E+01 -0.17673428E+01 0.21422176E+01 -0.13456628E+01 0.28668890E+01 -0.78358722E+00 0.34656539E+01 -0.10391521E+00 0.39236195E+01 0.66825843E+00 0.42305164E+01 0.15063753E+01 0.43807554E+01 0.23832123E+01 0.43733673E+01 0.32716842E+01 0.42117825E+01 0.41455812E+01 0.39035249E+01 0.49802656E+01 0.34597793E+01 0.57532616E+01 0.28948777E+01 0.64447680E+01 0.22257466E+01 0.70380502E+01 0.14712920E+01 0.75197430E+01 0.65176356E+00 test of center starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine center ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 2. the correct form of the call statement is call center (y, n, yc) ierr is 1 test of taper starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine taper ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call taper (y, n, taperp, yt) ierr is 1 test of pgm starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine pgm ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call pgm (yfft, n, lyfft, ldstak) ierr is 1 test of fftlen starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine fftlen ------------------------------------- the input value of n is -2. the value of the argument n must be greater than or equal to 1. the correct form of the call statement is call fftlen (n, ndiv, nfft) ierr is 1 test of pgms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine pgms ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call pgms (yfft, n, nfft, lyfft, + iextnd, nf, per, lper, freq, lfreq, nprt) nfft is -1 ierr is 1 test of mdflt starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mdflt ------------------------------------- the input value of nk is 0. the value of the argument nk must be greater than or equal to 1. the correct form of the call statement is call mdflt (per, nf, nk, kmd, perf, ldstak) ierr is 1 test of ipgmp starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ipgmp ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ipgmp (per, freq, nf, n, ldstak) ierr is 1 test of ipgmps starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ipgmps ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ipgmps (per, freq, nf, n, ldstak, peri, nprt) ierr is 1 test of ipgm starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ipgm ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ipgm (yfft, n, lyfft, ldstak) ierr is 1 test of ipgms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ipgms ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ipgms (yfft, n, lyfft, ldstak, + nf, peri, lperi, freq, lfreq, nprt) ierr is 1 test of fftr starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine fftr ------------------------------------- the input value of n is -1. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call fftr (yfft, n, nfft, iextnd, nf, ab, lab) ierr is 1 test of center ierr is 0 -0.41943287E+02 -0.35943287E+02 -0.30943287E+02 -0.23943287E+02 -0.10943287E+02 0.11056713E+02 -0.17943287E+02 -0.26943287E+02 -0.36943287E+02 -0.38943287E+02 -0.43943287E+02 -0.46943287E+02 -0.46943287E+02 -0.44943287E+02 -0.35943287E+02 -0.19943287E+02 0.56713104E-01 0.16056713E+02 0.13056713E+02 -0.79432869E+01 -0.18943287E+02 -0.20943287E+02 -0.24943287E+02 -0.35943287E+02 -0.25943287E+02 -0.69432869E+01 0.31056713E+02 0.75056717E+02 0.56056713E+02 0.26056713E+02 0.56713104E-01 -0.11943287E+02 -0.35943287E+02 -0.41943287E+02 -0.30943287E+02 -0.12943287E+02 0.23056713E+02 0.34056713E+02 0.64056717E+02 0.54056713E+02 0.26056713E+02 -0.69432869E+01 -0.26943287E+02 -0.30943287E+02 -0.41943287E+02 -0.35943287E+02 -0.24943287E+02 -0.69432869E+01 0.13056713E+02 0.33956715E+02 0.36456715E+02 0.75671387E+00 0.85671234E+00 -0.16243286E+02 -0.34743286E+02 -0.37343285E+02 -0.36743286E+02 -0.14543285E+02 0.65671158E+00 0.70567131E+01 0.15956715E+02 0.38956715E+02 0.14256714E+02 -0.18432884E+01 -0.10543285E+02 -0.26043287E+02 -0.35543289E+02 -0.91432877E+01 0.22856716E+02 0.59156712E+02 0.53856716E+02 0.34656712E+02 0.19556713E+02 -0.12143288E+02 -0.16343287E+02 -0.39943287E+02 -0.27143288E+02 0.45556713E+02 0.10745671E+03 0.78956711E+02 0.37856716E+02 0.21156712E+02 -0.84432869E+01 -0.24143288E+02 -0.36743286E+02 -0.22843287E+02 0.35956715E+02 0.85056717E+02 0.83956711E+02 0.71156708E+02 0.42956715E+02 0.19656712E+02 0.13056713E+02 -0.43285370E-01 -0.59432869E+01 -0.25643288E+02 -0.30943287E+02 -0.40543285E+02 -0.42843288E+02 -0.40143288E+02 -0.32443287E+02 -0.12943287E+02 -0.19432869E+01 -0.38432884E+01 0.55671310E+00 -0.47432861E+01 -0.18843287E+02 -0.36843285E+02 -0.38843285E+02 -0.44443287E+02 -0.46943287E+02 -0.45543285E+02 -0.41943287E+02 -0.34743286E+02 -0.33043289E+02 -0.11543285E+02 -0.11432877E+01 -0.58432884E+01 -0.16843287E+02 -0.23043287E+02 -0.31343287E+02 -0.40343288E+02 -0.42943287E+02 -0.45143288E+02 -0.38443287E+02 -0.30343287E+02 -0.10643288E+02 0.26567116E+01 0.17256710E+02 0.20056713E+02 0.23956715E+02 0.85671234E+00 -0.19443287E+02 -0.38443287E+02 -0.33743286E+02 0.99567146E+01 0.74556717E+02 0.91356720E+02 0.56256710E+02 0.38756710E+02 0.17656712E+02 -0.10243286E+02 -0.22743286E+02 -0.36243286E+02 -0.31943287E+02 -0.68432884E+01 0.14556713E+02 0.51556713E+02 0.77756714E+02 0.49356716E+02 0.19656712E+02 0.17556713E+02 0.71567116E+01 -0.79432869E+01 -0.26343287E+02 -0.40243286E+02 -0.42643288E+02 -0.24243286E+02 0.78567123E+01 0.46856716E+02 0.48856716E+02 0.30256710E+02 0.12156712E+02 -0.29432869E+01 0.56713104E-01 -0.16443287E+02 -0.30643288E+02 -0.39643288E+02 -0.93432884E+01 0.27056713E+02 0.92056717E+02 0.64256714E+02 0.54656712E+02 0.19256710E+02 -0.22432861E+01 -0.29943287E+02 -0.35643288E+02 -0.34543289E+02 -0.43543285E+02 -0.40943287E+02 -0.14643288E+02 0.73567123E+01 0.12756714E+02 0.16756714E+02 0.16556713E+02 0.52567139E+01 -0.21543287E+02 -0.33843285E+02 -0.40143288E+02 -0.40643288E+02 -0.39843288E+02 -0.11343288E+02 0.26056713E+02 0.38156712E+02 0.31056713E+02 0.17056713E+02 -0.51432877E+01 -0.20743286E+02 -0.20243286E+02 -0.34843285E+02 -0.37443287E+02 -0.44243286E+02 -0.41943287E+02 -0.22543287E+02 -0.49432869E+01 0.16556713E+02 0.68567123E+01 0.15056713E+02 0.15567131E+01 -0.30432854E+01 -0.28343287E+02 -0.41243286E+02 -0.43343288E+02 -0.45543285E+02 -0.37343285E+02 0.45671463E+00 0.10156712E+02 0.56956715E+02 0.33656712E+02 0.16656712E+02 -0.93432884E+01 -0.20843287E+02 -0.32743286E+02 -0.41143288E+02 -0.30243286E+02 -0.26432877E+01 0.16956715E+02 0.22056713E+02 0.30856716E+02 0.17956715E+02 -0.11243286E+02 -0.25743286E+02 -0.35843285E+02 -0.41243286E+02 -0.38243286E+02 -0.10843288E+02 0.32756710E+02 0.67456711E+02 0.62656712E+02 0.41856716E+02 0.20856716E+02 0.55671310E+00 -0.16343287E+02 -0.30643288E+02 -0.37343285E+02 -0.13743286E+02 0.45656712E+02 0.10465672E+03 0.89356720E+02 0.87756714E+02 0.36956715E+02 0.22456715E+02 -0.15443287E+02 -0.33043289E+02 -0.42543285E+02 -0.89432869E+01 0.94756714E+02 0.14325671E+03 0.13785672E+03 0.11205672E+03 0.65356720E+02 test of taper ierr is 0 -0.15290715E+00 -0.11678643E+01 -0.27387307E+01 -0.40329762E+01 -0.29288437E+01 0.42053347E+01 -0.89716434E+01 -0.16695620E+02 -0.27055847E+02 -0.32383732E+02 -0.40053955E+02 -0.45418011E+02 -0.46772152E+02 -0.44943287E+02 -0.35943287E+02 -0.19943287E+02 0.56713104E-01 0.16056713E+02 0.13056713E+02 -0.79432869E+01 -0.18943287E+02 -0.20943287E+02 -0.24943287E+02 -0.35943287E+02 -0.25943287E+02 -0.69432869E+01 0.31056713E+02 0.75056717E+02 0.56056713E+02 0.26056713E+02 0.56713104E-01 -0.11943287E+02 -0.35943287E+02 -0.41943287E+02 -0.30943287E+02 -0.12943287E+02 0.23056713E+02 0.34056713E+02 0.64056717E+02 0.54056713E+02 0.26056713E+02 -0.69432869E+01 -0.26943287E+02 -0.30943287E+02 -0.41943287E+02 -0.35943287E+02 -0.24943287E+02 -0.69432869E+01 0.13056713E+02 0.33956715E+02 0.36456715E+02 0.75671387E+00 0.85671234E+00 -0.16243286E+02 -0.34743286E+02 -0.37343285E+02 -0.36743286E+02 -0.14543285E+02 0.65671158E+00 0.70567131E+01 0.15956715E+02 0.38956715E+02 0.14256714E+02 -0.18432884E+01 -0.10543285E+02 -0.26043287E+02 -0.35543289E+02 -0.91432877E+01 0.22856716E+02 0.59156712E+02 0.53856716E+02 0.34656712E+02 0.19556713E+02 -0.12143288E+02 -0.16343287E+02 -0.39943287E+02 -0.27143288E+02 0.45556713E+02 0.10745671E+03 0.78956711E+02 0.37856716E+02 0.21156712E+02 -0.84432869E+01 -0.24143288E+02 -0.36743286E+02 -0.22843287E+02 0.35956715E+02 0.85056717E+02 0.83956711E+02 0.71156708E+02 0.42956715E+02 0.19656712E+02 0.13056713E+02 -0.43285370E-01 -0.59432869E+01 -0.25643288E+02 -0.30943287E+02 -0.40543285E+02 -0.42843288E+02 -0.40143288E+02 -0.32443287E+02 -0.12943287E+02 -0.19432869E+01 -0.38432884E+01 0.55671310E+00 -0.47432861E+01 -0.18843287E+02 -0.36843285E+02 -0.38843285E+02 -0.44443287E+02 -0.46943287E+02 -0.45543285E+02 -0.41943287E+02 -0.34743286E+02 -0.33043289E+02 -0.11543285E+02 -0.11432877E+01 -0.58432884E+01 -0.16843287E+02 -0.23043287E+02 -0.31343287E+02 -0.40343288E+02 -0.42943287E+02 -0.45143288E+02 -0.38443287E+02 -0.30343287E+02 -0.10643288E+02 0.26567116E+01 0.17256710E+02 0.20056713E+02 0.23956715E+02 0.85671234E+00 -0.19443287E+02 -0.38443287E+02 -0.33743286E+02 0.99567146E+01 0.74556717E+02 0.91356720E+02 0.56256710E+02 0.38756710E+02 0.17656712E+02 -0.10243286E+02 -0.22743286E+02 -0.36243286E+02 -0.31943287E+02 -0.68432884E+01 0.14556713E+02 0.51556713E+02 0.77756714E+02 0.49356716E+02 0.19656712E+02 0.17556713E+02 0.71567116E+01 -0.79432869E+01 -0.26343287E+02 -0.40243286E+02 -0.42643288E+02 -0.24243286E+02 0.78567123E+01 0.46856716E+02 0.48856716E+02 0.30256710E+02 0.12156712E+02 -0.29432869E+01 0.56713104E-01 -0.16443287E+02 -0.30643288E+02 -0.39643288E+02 -0.93432884E+01 0.27056713E+02 0.92056717E+02 0.64256714E+02 0.54656712E+02 0.19256710E+02 -0.22432861E+01 -0.29943287E+02 -0.35643288E+02 -0.34543289E+02 -0.43543285E+02 -0.40943287E+02 -0.14643288E+02 0.73567123E+01 0.12756714E+02 0.16756714E+02 0.16556713E+02 0.52567139E+01 -0.21543287E+02 -0.33843285E+02 -0.40143288E+02 -0.40643288E+02 -0.39843288E+02 -0.11343288E+02 0.26056713E+02 0.38156712E+02 0.31056713E+02 0.17056713E+02 -0.51432877E+01 -0.20743286E+02 -0.20243286E+02 -0.34843285E+02 -0.37443287E+02 -0.44243286E+02 -0.41943287E+02 -0.22543287E+02 -0.49432869E+01 0.16556713E+02 0.68567123E+01 0.15056713E+02 0.15567131E+01 -0.30432854E+01 -0.28343287E+02 -0.41243286E+02 -0.43343288E+02 -0.45543285E+02 -0.37343285E+02 0.45671463E+00 0.10156712E+02 0.56956715E+02 0.33656712E+02 0.16656712E+02 -0.93432884E+01 -0.20843287E+02 -0.32743286E+02 -0.41143288E+02 -0.30243286E+02 -0.26432877E+01 0.16956715E+02 0.22056713E+02 0.30856716E+02 0.17956715E+02 -0.11243286E+02 -0.25743286E+02 -0.35843285E+02 -0.41243286E+02 -0.38243286E+02 -0.10843288E+02 0.32756710E+02 0.67456711E+02 0.62656712E+02 0.41856716E+02 0.20856716E+02 0.55671310E+00 -0.16343287E+02 -0.30643288E+02 -0.37343285E+02 -0.13743286E+02 0.45656712E+02 0.10465672E+03 0.89030968E+02 0.84905327E+02 0.33685749E+02 0.18674135E+02 -0.11310071E+02 -0.20475534E+02 -0.21271643E+02 -0.34015093E+01 0.25360535E+02 0.24129974E+02 0.12201432E+02 0.36409314E+01 0.23826244E+00 test of pgm starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine pgm ------------------------------------- the input value of lyfft is 0. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 266. the correct form of the call statement is call pgm (yfft, n, lyfft, ldstak) ierr is 1 test of fftlen starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine fftlen ------------------------------------- the input value of n is -2. the value of the argument n must be greater than or equal to 1. the correct form of the call statement is call fftlen (n, ndiv, nfft) ierr is 1 test of pgms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine pgms ------------------------------------- the extended series length (nfft) must equal or exceed, the number of observations in the series (n= 261 plus 2. the input value of the parameter nfft ( -1) does not meet the requirements of singletons fft code. the next larger value which does is 266. the value 266 will be used for the extended series length. the input value of lyfft is 0. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 266. the input value of lper is 0. the length of per , as indicated by the argument lper , must be greater than or equal to 133. the input value of lfreq is 0. the length of freq , as indicated by the argument lfreq , must be greater than or equal to 133. the correct form of the call statement is call pgms (yfft, n, nfft, lyfft, + iextnd, nf, per, lper, freq, lfreq, nprt) nfft is -1 ierr is 1 test of mdflt ierr is 0 0.33177600E+06 0.28507500E-02 0.12007113E-01 0.29313829E-01 0.57952851E-01 0.10259269E+00 0.16958161E+00 0.26716533E+00 0.40570873E+00 0.59789699E+00 0.85889941E+00 0.12064593E+01 0.16609023E+01 0.22450256E+01 0.29838722E+01 0.39043601E+01 0.50347719E+01 0.64040980E+01 0.80412579E+01 0.99741840E+01 0.12228821E+02 0.14828029E+02 0.17790495E+02 0.21129591E+02 0.24852301E+02 0.28958199E+02 0.33438599E+02 0.38275829E+02 0.43442719E+02 0.48902267E+02 0.54607758E+02 0.60502850E+02 0.66522293E+02 0.72592667E+02 0.78633583E+02 0.84559097E+02 0.90279266E+02 0.95702240E+02 0.10073590E+03 0.10529043E+03 0.10928012E+03 0.11262579E+03 0.11525682E+03 0.11711299E+03 0.11814645E+03 0.11832311E+03 0.11762386E+03 0.11604546E+03 0.11360113E+03 0.11032037E+03 0.10624893E+03 0.10144781E+03 0.95992287E+02 0.89970291E+02 0.83480690E+02 0.76630943E+02 0.69534882E+02 0.62310028E+02 0.55075104E+02 0.47947151E+02 0.41039059E+02 0.34456909E+02 0.28297695E+02 0.22647236E+02 0.17578394E+02 0.13149561E+02 0.94037199E+01 0.63677340E+01 0.40521727E+01 0.24514885E+01 0.15446819E+01 0.12962836E+01 0.16577318E+01 0.25690355E+01 0.39606910E+01 0.57557893E+01 0.78722634E+01 0.10225159E+02 0.12728948E+02 0.15299696E+02 0.17857187E+02 0.20326756E+02 0.22640976E+02 0.24740967E+02 0.26577511E+02 0.28111725E+02 0.29315495E+02 0.30171499E+02 0.30672981E+02 0.30823187E+02 0.30634537E+02 0.30127596E+02 0.29329874E+02 0.28274426E+02 0.26998476E+02 0.25541920E+02 0.23945938E+02 0.22251579E+02 0.20498541E+02 0.18724051E+02 0.16961977E+02 0.15242003E+02 0.13589252E+02 0.12023832E+02 0.10560886E+02 0.92106123E+01 0.79785981E+01 0.68662729E+01 0.58714962E+01 0.49892049E+01 0.42121925E+01 0.35318432E+01 0.29388981E+01 0.24241352E+01 0.19790285E+01 0.15962586E+01 0.12701257E+01 0.99682051E+00 0.77456254E+00 0.60358852E+00 0.48600242E+00 0.42550346E+00 0.42700785E+00 0.49618000E+00 0.63889933E+00 0.86070418E+00 0.11662152E+01 0.15585966E+01 0.20390620E+01 0.26064506E+01 0.32569165E+01 0.39837232E+01 0.47771668E+01 display of periodogram smoothed with modified daniel filter starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 331775.9688 - + - i i i i 100000.0000 - - i i i i i i i i i i 10000.0000 - - i i i i i i i i i i i i 1000.0000 - - i i i i i i i i i +2++2 i 100.0000 - +2++2+ ++2++ - i 2++ 2++ i i +++ 2 i i ++ ++ 2++2++2++ i i 2+ + 2++ 2++ i i ++ ++ ++ +2 i 10.0000 - + + + +++ - i ++ + + +++ i i + + + 2 + i i 2 + ++ ++ i i + + + 2 ++ i i + + + + + i 1.0000 - + + ++ + - i + + ++ i i + ++ + i i + 2 i i + i i + i 0.1000 - + - i i i + i i i i + i i i i + i 0.0100 - - i i i i 0.0029 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0258 0.0516 0.0773 0.1031 0.1289 0.1547 0.1805 0.2062 0.2320 0.2578 test of ipgmp starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - +32323233232323233232323233232323323232+ - i 22323323232322 . i i . i i . i i . . i 0.9000 - . . - i . . i i . . i i . . i i . . i 0.8000 - . . - i . . i i . . i i . . i i . . i 0.7000 - . . - i . . i i . . i i . . i i . . i 0.6000 - . . - i . . i i . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - . . - i . . i i . . i i . . i i . . i 0.3000 - . . - i . . i i . . i i . . i i . . i 0.2000 - . . - i . . i i . . i i . . i i . . i 0.1000 - . . - i . . i i . i i . i i . i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 test of ipgmps starpac 2.08s (03/15/90) integrated sample periodogram (+) with 95 per cent test limits for white noise (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - +32323233232323233232323233232323323232+ - i 22323323232322 . i i . i i . i i . . i 0.9000 - . . - i . . i i . . i i . . i i . . i 0.8000 - . . - i . . i i . . i i . . i i . . i 0.7000 - . . - i . . i i . . i i . . i i . . i 0.6000 - . . - i . . i i . . i i . . i i . . i 0.5000 - . . - i . . i i . . i i . . i i . . i 0.4000 - . . - i . . i i . . i i . . i i . . i 0.3000 - . . - i . . i i . . i i . . i i . . i 0.2000 - . . - i . . i i . . i i . . i i . . i 0.1000 - . . - i . . i i . i i . i i . i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.19531250E-02 0.39062500E-02 0.58593750E-02 0.78125000E-02 0.97656250E-02 0.11718750E-01 0.13671875E-01 0.15625000E-01 0.17578125E-01 0.19531250E-01 0.21484375E-01 0.23437500E-01 0.25390625E-01 0.27343750E-01 0.29296875E-01 0.31250000E-01 0.33203125E-01 0.35156250E-01 0.37109375E-01 0.39062500E-01 0.41015625E-01 0.42968750E-01 0.44921875E-01 0.46875000E-01 0.48828125E-01 0.50781250E-01 0.52734375E-01 0.54687500E-01 0.56640625E-01 0.58593750E-01 0.60546875E-01 0.62500000E-01 0.64453125E-01 0.66406250E-01 0.68359375E-01 0.70312500E-01 0.72265625E-01 0.74218750E-01 0.76171875E-01 0.78125000E-01 0.80078125E-01 0.82031250E-01 0.83984375E-01 0.85937500E-01 0.87890625E-01 0.89843750E-01 0.91796875E-01 0.93750000E-01 0.95703125E-01 0.97656250E-01 0.99609375E-01 0.10156250E+00 0.10351562E+00 0.10546875E+00 0.10742188E+00 0.10937500E+00 0.11132812E+00 0.11328125E+00 0.11523438E+00 0.11718750E+00 0.11914062E+00 0.12109375E+00 0.12304688E+00 0.12500000E+00 0.12695312E+00 0.12890625E+00 0.13085938E+00 0.13281250E+00 0.13476562E+00 0.13671875E+00 0.13867188E+00 0.14062500E+00 0.14257812E+00 0.14453125E+00 0.14648438E+00 0.14843750E+00 0.15039062E+00 0.15234375E+00 0.15429688E+00 0.15625000E+00 0.15820312E+00 0.16015625E+00 0.16210938E+00 0.16406250E+00 0.16601562E+00 0.16796875E+00 0.16992188E+00 0.17187500E+00 0.17382812E+00 0.17578125E+00 0.17773438E+00 0.17968750E+00 0.18164062E+00 0.18359375E+00 0.18554688E+00 0.18750000E+00 0.18945312E+00 0.19140625E+00 0.19335938E+00 0.19531250E+00 0.19726562E+00 0.19921875E+00 0.20117188E+00 0.20312500E+00 0.20507812E+00 0.20703125E+00 0.20898438E+00 0.21093750E+00 0.21289062E+00 0.21484375E+00 0.21679688E+00 0.21875000E+00 0.22070312E+00 0.22265625E+00 0.22460938E+00 0.22656250E+00 0.22851562E+00 0.23046875E+00 0.23242188E+00 0.23437500E+00 0.23632812E+00 0.23828125E+00 0.24023438E+00 0.24218750E+00 0.24414062E+00 0.24609375E+00 0.24804688E+00 0.25000000E+00 0.25195312E+00 0.25390625E+00 0.25585938E+00 0.25781250E+00 0.98814511E+00 0.98814511E+00 0.98814511E+00 0.98814523E+00 0.98814541E+00 0.98814571E+00 0.98814619E+00 0.98814702E+00 0.98814821E+00 0.98815000E+00 0.98815250E+00 0.98815614E+00 0.98816109E+00 0.98816776E+00 0.98817658E+00 0.98818821E+00 0.98820323E+00 0.98822230E+00 0.98824620E+00 0.98827589E+00 0.98831230E+00 0.98835641E+00 0.98840940E+00 0.98847228E+00 0.98854631E+00 0.98863256E+00 0.98873216E+00 0.98884618E+00 0.98897552E+00 0.98912120E+00 0.98928380E+00 0.98946398E+00 0.98966217E+00 0.98987836E+00 0.99011254E+00 0.99036437E+00 0.99063325E+00 0.99091828E+00 0.99121833E+00 0.99153191E+00 0.99185735E+00 0.99219280E+00 0.99253607E+00 0.99288493E+00 0.99323684E+00 0.99358922E+00 0.99393952E+00 0.99428511E+00 0.99462342E+00 0.99495196E+00 0.99526840E+00 0.99557054E+00 0.99585646E+00 0.99612445E+00 0.99637300E+00 0.99660122E+00 0.99680835E+00 0.99699390E+00 0.99715793E+00 0.99730068E+00 0.99742287E+00 0.99752557E+00 0.99760985E+00 0.99767733E+00 0.99772978E+00 0.99776894E+00 0.99779695E+00 0.99781597E+00 0.99782807E+00 0.99783528E+00 0.99783987E+00 0.99784368E+00 0.99784863E+00 0.99785626E+00 0.99786806E+00 0.99788517E+00 0.99790865E+00 0.99793905E+00 0.99797696E+00 0.99802256E+00 0.99807572E+00 0.99813622E+00 0.99820369E+00 0.99827743E+00 0.99835652E+00 0.99844027E+00 0.99852759E+00 0.99861741E+00 0.99870878E+00 0.99880058E+00 0.99889177E+00 0.99898148E+00 0.99906892E+00 0.99915314E+00 0.99923354E+00 0.99930960E+00 0.99938089E+00 0.99944717E+00 0.99950820E+00 0.99956393E+00 0.99961448E+00 0.99965990E+00 0.99970037E+00 0.99973625E+00 0.99976766E+00 0.99979514E+00 0.99981886E+00 0.99983937E+00 0.99985683E+00 0.99987173E+00 0.99988431E+00 0.99989480E+00 0.99990356E+00 0.99991083E+00 0.99991667E+00 0.99992144E+00 0.99992526E+00 0.99992824E+00 0.99993056E+00 0.99993235E+00 0.99993384E+00 0.99993515E+00 0.99993640E+00 0.99993789E+00 0.99993980E+00 0.99994236E+00 0.99994582E+00 0.99995047E+00 0.99995655E+00 0.99996424E+00 0.99997395E+00 0.99998575E+00 0.10000000E+01 test of ipgm starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ipgm ------------------------------------- the input value of lyfft is 0. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 266. the correct form of the call statement is call ipgm (yfft, n, lyfft, ldstak) ierr is 1 test of ipgms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ipgms ------------------------------------- the input value of lyfft is 0. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 266. the input value of lperi is 0. the length of peri , as indicated by the argument lperi , must be greater than or equal to 133. the input value of lfreq is 0. the length of freq , as indicated by the argument lfreq , must be greater than or equal to 133. the correct form of the call statement is call ipgms (yfft, n, lyfft, ldstak, + nf, peri, lperi, freq, lfreq, nprt) ierr is 1 test of fftr starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine fftr ------------------------------------- the extended series length (nfft) must equal or exceed, the number of observations in the series (n= 50 plus 2. the input value of the parameter nfft ( 0) does not meet the requirements of singletons fft code. the next larger value which does is 52. the value 52 will be used for the extended series length. the input value of lab is 0. the length of ab , as indicated by the argument lab , must be greater than or equal to 52. the correct form of the call statement is call fftr (yfft, n, nfft, iextnd, nf, ab, lab) ierr is 1 1test of pp test number 0 n = 144 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 ++ + + i i 2 + +++ i 155.8000 - + - i ++ + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of ppm test number 0 n = 144 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 + + i i 2 + +++ i 155.8000 - + - i + + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of spp test number 0 n = 144 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 bd k k i i 2 d jla i 155.8000 - i - i gh c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppm test number 0 n = 144 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 d k i i 2 d jla i 155.8000 - i - i h c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of mpp test number 0 n = 12 + / m = 12 / iym = 12 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - l - i i i l i i i i i 570.2000 - - i k i i k i i l i i i 518.4000 - - i j l i i i i j i i l k i 466.6000 - i i k - i l l i i i i j l i i k i i 414.8000 - l l h - i k h 2 k k i i l k l i i i i h i 363.0000 - k j j g j k - i i 2 i h i i j k g i i i 2 i i j h h i 311.2000 - i h g g j - i i f h i h i i f i i h h g i i g g g e g h i 259.4000 - f e f - i i i g 2 e f e d e g i i g f e d f f i i d i 207.6000 - f d e f - i e e d c c 2 i i f d c d i i d c d c c 2 i i d c b b c c i 155.8000 - b - i c c b b a a c i i a b a a b b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of mppm test number 0 n = 12 + / m = 12 / iym = 12 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 606.0000 - l - i i i i i i i i 555.8000 - k - i i i l i i i i i 505.6000 - j l - i i i i i l k i i l i k l i 455.4000 - - i i i j l i i i i i l l k i 405.2000 - k h 2 k k - i l k i i l i i h i i j j k i 355.0000 - k i i h j - i j k 2 g i i i 2 i i i i i j h h h g g j i 304.8000 - i h i h - i f i i h i i h g e g h g i i g g f i 254.6000 - f - i g e d i i g 2 e f e g i i f e f f i i d e i 204.4000 - f d f e - i e e d c d d i i f d d c i i d c c c b d i i c c c i 154.2000 - c b - i c b b a c b i i a b a a b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of ppl test number 0 n = 144 + / ilog = -1 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 ++ + + i i 2 + +++ i 155.8000 - + - i ++ + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of ppml test number 0 n = 144 + / ilog = -1 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 + + i i 2 + +++ i 155.8000 - + - i + + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppl test number 0 n = 144 + / ilog = -1 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 bd k k i i 2 d jla i 155.8000 - i - i gh c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppml test number 0 n = 144 + / ilog = -1 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 d k i i 2 d jla i 155.8000 - i - i h c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of mppl test number 0 n = 12 + / m = 12 / iym = 12 + / ilog = -1 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - l - i i i l i i i i i 570.2000 - - i k i i k i i l i i i 518.4000 - - i j l i i i i j i i l k i 466.6000 - i i k - i l l i i i i j l i i k i i 414.8000 - l l h - i k h 2 k k i i l k l i i i i h i 363.0000 - k j j g j k - i i 2 i h i i j k g i i i 2 i i j h h i 311.2000 - i h g g j - i i f h i h i i f i i h h g i i g g g e g h i 259.4000 - f e f - i i i g 2 e f e d e g i i g f e d f f i i d i 207.6000 - f d e f - i e e d c c 2 i i f d c d i i d c d c c 2 i i d c b b c c i 155.8000 - b - i c c b b a a c i i a b a a b b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of mppml test number 0 n = 12 + / m = 12 / iym = 12 + / ilog = -1 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 606.0000 - l - i i i i i i i i 555.8000 - k - i i i l i i i i i 505.6000 - j l - i i i i i l k i i l i k l i 455.4000 - - i i i j l i i i i i l l k i 405.2000 - k h 2 k k - i l k i i l i i h i i j j k i 355.0000 - k i i h j - i j k 2 g i i i 2 i i i i i j h h h g g j i 304.8000 - i h i h - i f i i h i i h g e g h g i i g g f i 254.6000 - f - i g e d i i g 2 e f e g i i f e f f i i d e i 204.4000 - f d f e - i e e d c d d i i f d d c i i d c c c b d i i c c c i 154.2000 - c b - i c b b a c b i i a b a a b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of ppc test number 0 n = 144 + / ilog = -1 isize = -1 / nout = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 ++ + + i i 2 + +++ i 155.8000 - + - i ++ + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of ppmc test number 0 n = 144 + / ilog = -1 isize = -1 / nout = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 + + i i 2 + +++ i 155.8000 - + - i + + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppc test number 0 n = 144 + / ilog = -1 isize = -1 / nout = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 bd k k i i 2 d jla i 155.8000 - i - i gh c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppmc test number 0 n = 144 + / ilog = -1 isize = -1 / nout = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 d k i i 2 d jla i 155.8000 - i - i h c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of mppc test number 0 n = 12 + / m = 12 / iym = 12 + / ilog = -1 isize = -1 / nout = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - l - i i i l i i i i i 570.2000 - - i k i i k i i l i i i 518.4000 - - i j l i i i i j i i l k i 466.6000 - i i k - i l l i i i i j l i i k i i 414.8000 - l l h - i k h 2 k k i i l k l i i i i h i 363.0000 - k j j g j k - i i 2 i h i i j k g i i i 2 i i j h h i 311.2000 - i h g g j - i i f h i h i i f i i h h g i i g g g e g h i 259.4000 - f e f - i i i g 2 e f e d e g i i g f e d f f i i d i 207.6000 - f d e f - i e e d c c 2 i i f d c d i i d c d c c 2 i i d c b b c c i 155.8000 - b - i c c b b a a c i i a b a a b b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of mppmc test number 0 n = 12 + / m = 12 / iym = 12 + / ilog = -1 isize = -1 / nout = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 606.0000 - l - i i i i i i i i 555.8000 - k - i i i l i i i i i 505.6000 - j l - i i i i i l k i i l i k l i 455.4000 - - i i i j l i i i i i l l k i 405.2000 - k h 2 k k - i l k i i l i i h i i j j k i 355.0000 - k i i h j - i j k 2 g i i i 2 i i i i i j h h h g g j i 304.8000 - i h i h - i f i i h i i h g e g h g i i g g f i 254.6000 - f - i g e d i i g 2 e f e g i i f e f f i i d e i 204.4000 - f d f e - i e e d c d d i i f d d c i i d c c c b d i i c c c i 154.2000 - c b - i c b b a c b i i a b a a b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of ppl test number 1 n = 144 + / ilog = 0 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 ++ + + i i 2 + +++ i 155.8000 - + - i ++ + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of ppml test number 1 n = 144 + / ilog = 0 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - + - i i i + i i i i i 570.2000 - - i + i i + i i + i i i 518.4000 - - i + + i i i i + i i + + i 466.6000 - ++ + - i + + i i i i + + i i + + i 414.8000 - + + + - i + + + + + + i i + + + i i i i + i 363.0000 - + ++ + + + - i + 2+ + i i + + + + i i + + i i + + + i 311.2000 - + + + + + - i + ++ + + i i + i i +2 i i + 2+ + + i 259.4000 - + + + - i i i + +++ + + + + + i i + + + + ++ i i + i 207.6000 - + + + + - i ++ + +2 + i i + + + + i i + 2 + + i i 2 + +++ i 155.8000 - + - i + + + 2 + i i + + + + ++ i i ++ + + i i ++ + 2 + i 104.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppl test number 1 n = 144 + / ilog = 0 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 bd k k i i 2 d jla i 155.8000 - i - i gh c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppml test number 1 n = 144 + / ilog = 0 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - g - i i i h i i i i i 570.2000 - - i h i i g i i f i i i 518.4000 - - i h i i i i i g i i f e i 466.6000 - gh i - i d j i i i i f l i i f e i 414.8000 - g a c - i h i i c j l i i d b k i i i i f i 363.0000 - g ce j a k - i i 2e d i i h j a b i i l l i i c e b i 311.2000 - f i d a k - i g jl b k i i h i i l2 i i h 2e j k i 259.4000 - g f i - i i i h cdf i c e a k i i g e d j lb i i f i 207.6000 - i j a k - i gh c l2 l i i i e j b i i c 2 d k i i 2 d jla i 155.8000 - i - i h c f 2 k i i c f i d jl i i de b e i i +z j 2 k i 104.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of mppl test number 1 n = 12 + / m = 12 / iym = 12 + / ilog = 0 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - l - i i i l i i i i i 570.2000 - - i k i i k i i l i i i 518.4000 - - i j l i i i i j i i l k i 466.6000 - i i k - i l l i i i i j l i i k i i 414.8000 - l l h - i k h 2 k k i i l k l i i i i h i 363.0000 - k j j g j k - i i 2 i h i i j k g i i i 2 i i j h h i 311.2000 - i h g g j - i i f h i h i i f i i h h g i i g g g e g h i 259.4000 - f e f - i i i g 2 e f e d e g i i g f e d f f i i d i 207.6000 - f d e f - i e e d c c 2 i i f d c d i i d c d c c 2 i i d c b b c c i 155.8000 - b - i c c b b a a c i i a b a a b b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of mppml test number 1 n = 12 + / m = 12 / iym = 12 + / ilog = 0 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 606.0000 - l - i i i i i i i i 555.8000 - k - i i i l i i i i i 505.6000 - j l - i i i i i l k i i l i k l i 455.4000 - - i i i j l i i i i i l l k i 405.2000 - k h 2 k k - i l k i i l i i h i i j j k i 355.0000 - k i i h j - i j k 2 g i i i 2 i i i i i j h h h g g j i 304.8000 - i h i h - i f i i h i i h g e g h g i i g g f i 254.6000 - f - i g e d i i g 2 e f e g i i f e f f i i d e i 204.4000 - f d f e - i e e d c d d i i f d d c i i d c c c b d i i c c c i 154.2000 - c b - i c b b a c b i i a b a a b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of ppc test number 1 n = 144 + / ilog = 0 isize = 0 / nout = 0 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 700.0000 - - i i i i i i i i 640.0000 - - i i i i i i i i 580.0000 - - i i i i i i i i 520.0000 - - i i i i i i i i 460.0000 - - i i i i i i i i 400.0000 - - i i i i i i i i 340.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 160.0000 - - i + + i i + + + + i i + + + + + i i + i 100.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 131values fell outside the specified limit s** ierr = 0 1test of ppmc test number 1 n = 144 + / ilog = 0 isize = 0 / nout = 0 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 700.0000 - - i i i i i i i i 640.0000 - - i i i i i i i i 580.0000 - - i i i i i i i i 520.0000 - - i i i i i i i i 460.0000 - - i i i i i i i i 400.0000 - - i i i i i i i i 340.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 160.0000 - - i + i i + + + + i i + + + + + i i + i 100.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 129values fell outside the specified limit s** ierr = 0 1test of sppc test number 1 n = 144 + / ilog = 0 isize = 0 / nout = 0 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 700.0000 - - i i i i i i i i 640.0000 - - i i i i i i i i 580.0000 - - i i i i i i i i 520.0000 - - i i i i i i i i 460.0000 - - i i i i i i i i 400.0000 - - i i i i i i i i 340.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 160.0000 - - i g h i i f i c d i i d e j l b i i a i 100.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 131values fell outside the specified limit s** ierr = 0 1test of sppmc test number 1 n = 144 + / ilog = 0 isize = 0 / nout = 0 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 700.0000 - - i i i i i i i i 640.0000 - - i i i i i i i i 580.0000 - - i i i i i i i i 520.0000 - - i i i i i i i i 460.0000 - - i i i i i i i i 400.0000 - - i i i i i i i i 340.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 160.0000 - - i h i i f i c d i i d e j l b i i a i 100.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 129values fell outside the specified limit s** ierr = 0 1test of mppc test number 1 n = 12 + / m = 12 / iym = 12 + / ilog = 0 isize = 0 / nout = 0 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 700.0000 - - i i i i i i i i 640.0000 - - i l i i i i l i i i 580.0000 - - i i i k i i k i i l i 520.0000 - - i j l i i j i i i i l k i i 460.0000 - l i k l - i i i j l i i k i i i h k i 400.0000 - k h 2 k - i l i i h i i j g j k i i 2 i g h i i 340.0000 - 2 - i i i h h g g j i i f h i h i i f i 280.0000 - g g - i g g f e e h i i f i i e d i i 2 2 d e f g f i 220.0000 - d - i d e f i i c c d 2 i i d d c c e i i c b b d c i 160.0000 - c b c - i b a a c i i b a a b b i i a 2 a a i i b i 100.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 36values fell outside the specified limit s** ierr = 0 1test of mppmc test number 1 n = 12 + / m = 12 / iym = 12 + / ilog = 0 isize = 0 / nout = 0 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 700.0000 - - i i i i i i i i 640.0000 - - i i i i i l i i i 580.0000 - - i i i k i i i i l i 520.0000 - - i j l i i i i i i l k i i 460.0000 - l k l - i i i j l i i k i i i k i 400.0000 - k h 2 k - i l i i h i i j j k i i 2 i g h i i 340.0000 - 2 - i i i h h g g j i i h i h i i f i 280.0000 - g g - i g g f e h i i f i i e d i i 2 2 e f g f i 220.0000 - d - i d e f i i c d 2 i i d d c c i i c b d c i 160.0000 - c b c - i b a c i i b a a b b i i a 2 a a i i b i 100.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 35values fell outside the specified limit s** ierr = 0 1test of ppl test number 2 n = 144 + / ilog = 2 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i i i i i i i 800.0000 - - i i i i i i i i i + i 600.0000 - + - i i i ++ i i + i i 2 + i i + + i i ++ + + + i i + + i i + + + + + i 400.0000 - + + + ++ + ++ + - i + i i + + ++ ++ + + + i i + + + + + + i i + + i i + + +++ + + + i i + ++ + + i i + + i i + + +++ + i i + + + 2 i i i i + +++ + + + + i i + + ++ + ++ i i + i i + + i 200.0000 - ++ 2 ++ + - i + + + + i i + + + +++ + i i 2 + + + i i + + i i + + i i ++ + + i i + + i i + + 2 + i i ++ + i i + + i i + + + + i i + + i i + i i + i 100.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of ppml test number 2 n = 144 + / ilog = 2 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i i i i i i i 800.0000 - - i i i i i i i i i + i 600.0000 - + - i i i ++ i i + i i 2 + i i + + i i ++ + + + i i + + i i + + + + + i 400.0000 - + + + ++ + ++ + - i + i i + + ++ ++ + + + i i + + + + + + i i + + i i + + +++ + + + i i + ++ + + i i + + i i + + +++ + i i + + + 2 i i i i + +++ + + + + i i + + ++ + ++ i i + i i + + i 200.0000 - ++ 2 ++ + - i + + + + i i + + + ++ i i 2 + + + i i + + i i + + i i + + + i i + + i i + + 2 + i i ++ + i i + + i i + + + + i i + + i i + i i + i 100.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppl test number 2 n = 144 + / ilog = 2 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i i i i i i i 800.0000 - - i i i i i i i i i g i 600.0000 - h - i i i gh i i f i i 2 i i i f e i i gh i d j i i f l i i g f e a c i 400.0000 - h i i cd j lb k - i f i i g i ce ce j a k i i h d j a d b i i l l i i f i cde a b k i i g jl b k i i h a i i h e jlb k i i g f i 2 i i i i h cdf i c a k i i g e de j lb i i f i i i j i 200.0000 - gh 2 la k - i c j l b i i c f i bde k i i 2 e a k i i d l i i i j i i gh f b i i a k i i f i 2 l i i cd j i i b e i i z e j l i i a k i i + i i k i 100.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of sppml test number 2 n = 144 + / ilog = 2 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i i i i i i i 800.0000 - - i i i i i i i i i g i 600.0000 - h - i i i gh i i f i i 2 i i i f e i i gh i d j i i f l i i g f e a c i 400.0000 - h i i cd j lb k - i f i i g i ce ce j a k i i h d j a d b i i l l i i f i cde a b k i i g jl b k i i h a i i h e jlb k i i g f i 2 i i i i h cdf i c a k i i g e de j lb i i f i i i j i 200.0000 - gh 2 la k - i c j l b i i c f i de i i 2 e a k i i d l i i i j i i h f b i i a k i i f i 2 l i i cd j i i b e i i z e j l i i a k i i + i i k i 100.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 15.3000 29.6000 43.9000 58.2000 72.5000 86.8000 101.1000 115.4000 129.7000 144.0000 ierr = 0 1test of mppl test number 2 n = 12 + / m = 12 / iym = 12 + / ilog = 2 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i i i i i i i 800.0000 - - i i i i i i i i i l i 600.0000 - l - i i i k k i i l i i j j l i i l k i i l i i k l i i j l i i l l k i h i 400.0000 - l k k h 2 k l k - i h i i k 2 2 g h j k i i j k 2 g i i i 2 i i i j h h h g g j i i i f h i h i i h f i i h g e g h g i i g g f e f i i i i g 2 e e d e g i i g f 2 d f f i i d i i d e i 200.0000 - 2 e c c f e - i f d d d i i d c d d c c e i i d c b b d i i c c i i b c i i c b a a i i c c i i b b a a b i i a a b i i b b i i a a a a i i b b i i a i i a i 100.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of mppml test number 2 n = 12 + / m = 12 / iym = 12 + / ilog = 2 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i i i i i i i 800.0000 - - i i i i i i i i i i 600.0000 - l - i i i k i i l i i j l i i l k i i l i k l i i j l i i l l k i i 400.0000 - l k k h 2 k l k - i h i i k 2 2 h j k i i j k 2 g i i i 2 i i i j h h h g g j i i i h i h i i h f i i h g e g h g i i g g f f i i i i g 2 e e d e g i i g f 2 f f i i d i i d e i 200.0000 - 2 e c f e - i f d d d i i c d d c c i i d c b d i i c c i i b c i i c b a i i c c i i b b a a b i i a a b i i b b i i a a a a i i b b i i a i i a i 100.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 ierr = 0 1test of ppc test number 2 n = 144 + / ilog = 2 isize = 2 / nout = 5 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i i i i i i i i 200.0000 - - i i i i i i i + + + i i + + + + + i i + + + + i i i 100.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 131values fell outside the specified limit s** see next page for list 1 the first 5 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 + 0.30000000E+01 0.13200000E+03 + 0.17000000E+02 0.12500000E+03 + 0.18000000E+02 0.14900000E+03 + ierr = 0 1test of ppmc test number 2 n = 144 + / ilog = 2 isize = 2 / nout = 5 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i i i i i i i i 200.0000 - - i i i i i i i + + i i + + + + + i i + + + + i i i 100.0000 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 129values fell outside the specified limit s** see next page for list 1 the first 5 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 + 0.30000000E+01 0.13200000E+03 + 0.17000000E+02 0.12500000E+03 + 0.18000000E+02 0.14900000E+03 + ierr = 0 1test of sppc test number 2 n = 144 + / ilog = 2 isize = 2 / nout = 5 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i i i i i i i i 200.0000 - - i i i i i i i g h c i i d f i b d i i e j l a i i i 100.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 131values fell outside the specified limit s** see next page for list 1 the first 5 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 z 0.30000000E+01 0.13200000E+03 c 0.17000000E+02 0.12500000E+03 e 0.18000000E+02 0.14900000E+03 f ierr = 0 1test of sppmc test number 2 n = 144 + / ilog = 2 isize = 2 / nout = 5 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i i i i i i i i 200.0000 - - i i i i i i i h c i i d f i b d i i e j l a i i i 100.0000 - k - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 129values fell outside the specified limit s** see next page for list 1 the first 5 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 z 0.30000000E+01 0.13200000E+03 c 0.17000000E+02 0.12500000E+03 e 0.18000000E+02 0.14900000E+03 f ierr = 0 1test of mppc test number 2 n = 12 + / m = 12 / iym = 12 + / ilog = 2 isize = 2 / nout = 5 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i 800.0000 - - i i i i i l l i 600.0000 - k - i l k j l i i l l k 2 i k l i i k 2 l i 400.0000 - k h h 2 k l k - i 2 2 h g g h 2 k i i h 2 i i h g f f g h 2 h i i g g f e e g h g i i e d f i i 2 2 d e f g f i 200.0000 - d d e f e - i d c c c d d i i d c c b b 2 c i i c b c i i b a a c b i i 2 a a b i i 2 a a i i b i 100.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 36values fell outside the specified limit s** see next page for list 1 the first 5 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 a 0.10000000E+01 0.11500000E+03 b 0.10000000E+01 0.14500000E+03 c 0.10000000E+01 0.17100000E+03 d 0.10000000E+01 0.19600000E+03 e ierr = 0 1test of mppmc test number 2 n = 12 + / m = 12 / iym = 12 + / ilog = 2 isize = 2 / nout = 5 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1000.0000 - - i i 800.0000 - - i i i i i l i 600.0000 - k - i l j l i i l l k i k l i i k 2 l i 400.0000 - k h 2 k l k - i 2 2 h g h 2 k i i h 2 i i h g f g h 2 h i i g g f e g h g i i e d f i i 2 2 e f g f i 200.0000 - d d e f e - i d c c d d i i d c c b d c i i c b c i i b a c b i i 2 a a b i i 2 a a i i b i 100.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 5.2000 6.4000 7.6000 8.8000 10.0000 11.2000 12.4000 13.6000 14.8000 16.0000 **note 35values fell outside the specified limit s** see next page for list 1 the first 5 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 a 0.10000000E+01 0.11500000E+03 b 0.10000000E+01 0.14500000E+03 c 0.10000000E+01 0.17100000E+03 d 0.10000000E+01 0.19600000E+03 e ierr = 0 1test of ppc test number 3 n = 144 + / ilog = 20 isize = 20 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 300.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 260.0000 - - i i i i i i i i 240.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 200.0000 - - i i i i i i i i 180.0000 - - i i i i i i i i 160.0000 - - i i i i i + + i i i 140.0000 - + - i + + + i i i i + + i i i 120.0000 - + + + - i + i i i i i i + i 100.0000 - - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 131values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 + 0.30000000E+01 0.13200000E+03 + 0.17000000E+02 0.12500000E+03 + 0.18000000E+02 0.14900000E+03 + 0.19000000E+02 0.17000000E+03 + 0.20000000E+02 0.17000000E+03 + 0.21000000E+02 0.15800000E+03 + 0.22000000E+02 0.13300000E+03 + 0.23000000E+02 0.11400000E+03 + 0.24000000E+02 0.14000000E+03 + 0.25000000E+02 0.14500000E+03 + 0.26000000E+02 0.15000000E+03 + 0.27000000E+02 0.17800000E+03 + 0.28000000E+02 0.16300000E+03 + 0.29000000E+02 0.17200000E+03 + 0.30000000E+02 0.17800000E+03 + 0.31000000E+02 0.19900000E+03 + 0.32000000E+02 0.19900000E+03 + 0.33000000E+02 0.18400000E+03 + 0.34000000E+02 0.16200000E+03 + 0.35000000E+02 0.14600000E+03 + 0.36000000E+02 0.16600000E+03 + 0.37000000E+02 0.17100000E+03 + 0.38000000E+02 0.18000000E+03 + 0.39000000E+02 0.19300000E+03 + 0.40000000E+02 0.18100000E+03 + 0.41000000E+02 0.18300000E+03 + 0.42000000E+02 0.21800000E+03 + 0.43000000E+02 0.23000000E+03 + 0.44000000E+02 0.24200000E+03 + 0.45000000E+02 0.20900000E+03 + 0.46000000E+02 0.19100000E+03 + 0.47000000E+02 0.17200000E+03 + 0.48000000E+02 0.19400000E+03 + 0.49000000E+02 0.19600000E+03 + 0.50000000E+02 0.19600000E+03 + 0.51000000E+02 0.23600000E+03 + 0.52000000E+02 0.23500000E+03 + 0.53000000E+02 0.22900000E+03 + 0.54000000E+02 0.24300000E+03 + 0.55000000E+02 0.26400000E+03 + 0.56000000E+02 0.27200000E+03 + 0.57000000E+02 0.23700000E+03 + 0.58000000E+02 0.21100000E+03 + 0.59000000E+02 0.18000000E+03 + 0.60000000E+02 0.20100000E+03 + 0.61000000E+02 0.20400000E+03 + 0.62000000E+02 0.18800000E+03 + 0.63000000E+02 0.23500000E+03 + ierr = 0 1test of ppmc test number 3 n = 144 + / ilog = 20 isize = 20 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 300.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 260.0000 - - i i i i i i i i 240.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 200.0000 - - i i i i i i i i 180.0000 - - i i i i i i i i 160.0000 - - i i i i i + i i i 140.0000 - + - i + + + i i i i + + i i i 120.0000 - + + + - i + i i i i i i + i 100.0000 - - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 129values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 + 0.30000000E+01 0.13200000E+03 + 0.17000000E+02 0.12500000E+03 + 0.18000000E+02 0.14900000E+03 + 0.19000000E+02 0.17000000E+03 + 0.20000000E+02 0.17000000E+03 + 0.21000000E+02 0.15800000E+03 + 0.22000000E+02 0.13300000E+03 + 0.23000000E+02 0.11400000E+03 + 0.24000000E+02 0.14000000E+03 + 0.25000000E+02 0.14500000E+03 + 0.26000000E+02 0.15000000E+03 + 0.27000000E+02 0.17800000E+03 + 0.28000000E+02 0.16300000E+03 + 0.29000000E+02 0.17200000E+03 + 0.30000000E+02 0.17800000E+03 + 0.31000000E+02 0.19900000E+03 + 0.32000000E+02 0.19900000E+03 + 0.33000000E+02 0.18400000E+03 + 0.34000000E+02 0.16200000E+03 + 0.35000000E+02 0.14600000E+03 + 0.36000000E+02 0.16600000E+03 + 0.37000000E+02 0.17100000E+03 + 0.39000000E+02 0.19300000E+03 + 0.40000000E+02 0.18100000E+03 + 0.41000000E+02 0.18300000E+03 + 0.42000000E+02 0.21800000E+03 + 0.43000000E+02 0.23000000E+03 + 0.44000000E+02 0.24200000E+03 + 0.45000000E+02 0.20900000E+03 + 0.46000000E+02 0.19100000E+03 + 0.47000000E+02 0.17200000E+03 + 0.48000000E+02 0.19400000E+03 + 0.49000000E+02 0.19600000E+03 + 0.50000000E+02 0.19600000E+03 + 0.51000000E+02 0.23600000E+03 + 0.52000000E+02 0.23500000E+03 + 0.53000000E+02 0.22900000E+03 + 0.54000000E+02 0.24300000E+03 + 0.55000000E+02 0.26400000E+03 + 0.56000000E+02 0.27200000E+03 + 0.57000000E+02 0.23700000E+03 + 0.58000000E+02 0.21100000E+03 + 0.60000000E+02 0.20100000E+03 + 0.61000000E+02 0.20400000E+03 + 0.62000000E+02 0.18800000E+03 + 0.63000000E+02 0.23500000E+03 + 0.64000000E+02 0.22700000E+03 + 0.65000000E+02 0.23400000E+03 + ierr = 0 1test of sppc test number 3 n = 144 + / ilog = 20 isize = 20 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 300.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 260.0000 - - i i i i i i i i 240.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 200.0000 - - i i i i i i i i 180.0000 - - i i i i i i i i 160.0000 - - i i i i i g h i i i 140.0000 - c - i f i d i i i i d b i i i 120.0000 - e j l - i a i i i i i i k i 100.0000 - - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 131values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 z 0.30000000E+01 0.13200000E+03 c 0.17000000E+02 0.12500000E+03 e 0.18000000E+02 0.14900000E+03 f 0.19000000E+02 0.17000000E+03 g 0.20000000E+02 0.17000000E+03 h 0.21000000E+02 0.15800000E+03 i 0.22000000E+02 0.13300000E+03 j 0.23000000E+02 0.11400000E+03 k 0.24000000E+02 0.14000000E+03 l 0.25000000E+02 0.14500000E+03 a 0.26000000E+02 0.15000000E+03 b 0.27000000E+02 0.17800000E+03 c 0.28000000E+02 0.16300000E+03 d 0.29000000E+02 0.17200000E+03 e 0.30000000E+02 0.17800000E+03 f 0.31000000E+02 0.19900000E+03 g 0.32000000E+02 0.19900000E+03 h 0.33000000E+02 0.18400000E+03 i 0.34000000E+02 0.16200000E+03 j 0.35000000E+02 0.14600000E+03 k 0.36000000E+02 0.16600000E+03 l 0.37000000E+02 0.17100000E+03 a 0.38000000E+02 0.18000000E+03 b 0.39000000E+02 0.19300000E+03 c 0.40000000E+02 0.18100000E+03 d 0.41000000E+02 0.18300000E+03 e 0.42000000E+02 0.21800000E+03 f 0.43000000E+02 0.23000000E+03 g 0.44000000E+02 0.24200000E+03 h 0.45000000E+02 0.20900000E+03 i 0.46000000E+02 0.19100000E+03 j 0.47000000E+02 0.17200000E+03 k 0.48000000E+02 0.19400000E+03 l 0.49000000E+02 0.19600000E+03 a 0.50000000E+02 0.19600000E+03 b 0.51000000E+02 0.23600000E+03 c 0.52000000E+02 0.23500000E+03 d 0.53000000E+02 0.22900000E+03 e 0.54000000E+02 0.24300000E+03 f 0.55000000E+02 0.26400000E+03 g 0.56000000E+02 0.27200000E+03 h 0.57000000E+02 0.23700000E+03 i 0.58000000E+02 0.21100000E+03 j 0.59000000E+02 0.18000000E+03 k 0.60000000E+02 0.20100000E+03 l 0.61000000E+02 0.20400000E+03 a 0.62000000E+02 0.18800000E+03 b 0.63000000E+02 0.23500000E+03 c ierr = 0 1test of sppmc test number 3 n = 144 + / ilog = 20 isize = 20 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 300.0000 - - i i i i i i i i 280.0000 - - i i i i i i i i 260.0000 - - i i i i i i i i 240.0000 - - i i i i i i i i 220.0000 - - i i i i i i i i 200.0000 - - i i i i i i i i 180.0000 - - i i i i i i i i 160.0000 - - i i i i i h i i i 140.0000 - c - i f i d i i i i d b i i i 120.0000 - e j l - i a i i i i i i k i 100.0000 - - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 129values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 + 0.20000000E+01 0.11800000E+03 z 0.30000000E+01 0.13200000E+03 c 0.17000000E+02 0.12500000E+03 e 0.18000000E+02 0.14900000E+03 f 0.19000000E+02 0.17000000E+03 g 0.20000000E+02 0.17000000E+03 h 0.21000000E+02 0.15800000E+03 i 0.22000000E+02 0.13300000E+03 j 0.23000000E+02 0.11400000E+03 k 0.24000000E+02 0.14000000E+03 l 0.25000000E+02 0.14500000E+03 a 0.26000000E+02 0.15000000E+03 b 0.27000000E+02 0.17800000E+03 c 0.28000000E+02 0.16300000E+03 d 0.29000000E+02 0.17200000E+03 e 0.30000000E+02 0.17800000E+03 f 0.31000000E+02 0.19900000E+03 g 0.32000000E+02 0.19900000E+03 h 0.33000000E+02 0.18400000E+03 i 0.34000000E+02 0.16200000E+03 j 0.35000000E+02 0.14600000E+03 k 0.36000000E+02 0.16600000E+03 l 0.37000000E+02 0.17100000E+03 a 0.39000000E+02 0.19300000E+03 c 0.40000000E+02 0.18100000E+03 d 0.41000000E+02 0.18300000E+03 e 0.42000000E+02 0.21800000E+03 f 0.43000000E+02 0.23000000E+03 g 0.44000000E+02 0.24200000E+03 h 0.45000000E+02 0.20900000E+03 i 0.46000000E+02 0.19100000E+03 j 0.47000000E+02 0.17200000E+03 k 0.48000000E+02 0.19400000E+03 l 0.49000000E+02 0.19600000E+03 a 0.50000000E+02 0.19600000E+03 b 0.51000000E+02 0.23600000E+03 c 0.52000000E+02 0.23500000E+03 d 0.53000000E+02 0.22900000E+03 e 0.54000000E+02 0.24300000E+03 f 0.55000000E+02 0.26400000E+03 g 0.56000000E+02 0.27200000E+03 h 0.57000000E+02 0.23700000E+03 i 0.58000000E+02 0.21100000E+03 j 0.60000000E+02 0.20100000E+03 l 0.61000000E+02 0.20400000E+03 a 0.62000000E+02 0.18800000E+03 b 0.63000000E+02 0.23500000E+03 c 0.64000000E+02 0.22700000E+03 d 0.65000000E+02 0.23400000E+03 e ierr = 0 1test of mppc test number 3 n = 12 + / m = 12 / iym = 12 + / ilog = 20 isize = 20 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 300.0000 - - i i i f i i i i i 280.0000 - g - i g i i g e h i i g i i f e i 260.0000 - f - i i i i i i i e d i 240.0000 - - i e f e g i i d i i f e f f i i i 220.0000 - d - i i i e i i d i i f i 200.0000 - c c e - i d i i d i i i i d c i 180.0000 - d c e - i i i c b b d i i c i i c c i 160.0000 - b - i i i i i b a a c i i i 140.0000 - b - i b a a i i b i i a i i b i 120.0000 - a a a - i b i i i i i i a i 100.0000 - - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 85values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 a 0.10000000E+01 0.11500000E+03 b 0.10000000E+01 0.14500000E+03 c 0.10000000E+01 0.17100000E+03 d 0.10000000E+01 0.19600000E+03 e 0.10000000E+01 0.20400000E+03 f 0.10000000E+01 0.24200000E+03 g 0.10000000E+01 0.28400000E+03 h 0.10000000E+01 0.31500000E+03 i 0.10000000E+01 0.34000000E+03 j 0.10000000E+01 0.36000000E+03 k 0.10000000E+01 0.41700000E+03 l 0.20000000E+01 0.11800000E+03 a 0.20000000E+01 0.12600000E+03 b 0.20000000E+01 0.15000000E+03 c 0.20000000E+01 0.18000000E+03 d 0.20000000E+01 0.19600000E+03 e 0.20000000E+01 0.18800000E+03 f 0.20000000E+01 0.23300000E+03 g 0.20000000E+01 0.27700000E+03 h 0.20000000E+01 0.30100000E+03 i 0.20000000E+01 0.31800000E+03 j 0.20000000E+01 0.34200000E+03 k 0.20000000E+01 0.39100000E+03 l 0.30000000E+01 0.13200000E+03 a 0.30000000E+01 0.14100000E+03 b 0.30000000E+01 0.17800000E+03 c 0.30000000E+01 0.19300000E+03 d 0.30000000E+01 0.23600000E+03 e 0.30000000E+01 0.23500000E+03 f 0.30000000E+01 0.26700000E+03 g 0.30000000E+01 0.31700000E+03 h 0.30000000E+01 0.35600000E+03 i 0.30000000E+01 0.36200000E+03 j 0.30000000E+01 0.40600000E+03 k 0.30000000E+01 0.41900000E+03 l 0.40000000E+01 0.31300000E+03 h 0.40000000E+01 0.34800000E+03 i 0.40000000E+01 0.34800000E+03 j 0.40000000E+01 0.39600000E+03 k 0.40000000E+01 0.46100000E+03 l 0.50000000E+01 0.31800000E+03 h 0.50000000E+01 0.35500000E+03 i 0.50000000E+01 0.36300000E+03 j 0.50000000E+01 0.42000000E+03 k 0.50000000E+01 0.47200000E+03 l 0.60000000E+01 0.31500000E+03 g 0.60000000E+01 0.37400000E+03 h 0.60000000E+01 0.42200000E+03 i 0.60000000E+01 0.43500000E+03 j ierr = 0 1test of mppmc test number 3 n = 12 + / m = 12 / iym = 12 + / ilog = 20 isize = 20 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 300.0000 - - i i i f i i i i i 280.0000 - g - i g i i g e h i i g i i f i 260.0000 - f - i i i i i i i e d i 240.0000 - - i e f e g i i i i f e f f i i i 220.0000 - d - i i i e i i d i i f i 200.0000 - c e - i d i i d i i i i d c i 180.0000 - d c - i i i c b d i i c i i c c i 160.0000 - b - i i i i i b a c i i i 140.0000 - b - i b a a i i b i i a i i b i 120.0000 - a a a - i b i i i i i i a i 100.0000 - - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 77values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 a 0.10000000E+01 0.11500000E+03 b 0.10000000E+01 0.14500000E+03 c 0.10000000E+01 0.17100000E+03 d 0.10000000E+01 0.19600000E+03 e 0.10000000E+01 0.20400000E+03 f 0.10000000E+01 0.24200000E+03 g 0.10000000E+01 0.28400000E+03 h 0.10000000E+01 0.31500000E+03 i 0.10000000E+01 0.34000000E+03 j 0.10000000E+01 0.36000000E+03 k 0.10000000E+01 0.41700000E+03 l 0.20000000E+01 0.11800000E+03 a 0.20000000E+01 0.12600000E+03 b 0.20000000E+01 0.15000000E+03 c 0.20000000E+01 0.19600000E+03 e 0.20000000E+01 0.18800000E+03 f 0.20000000E+01 0.23300000E+03 g 0.20000000E+01 0.27700000E+03 h 0.20000000E+01 0.30100000E+03 i 0.20000000E+01 0.31800000E+03 j 0.20000000E+01 0.34200000E+03 k 0.20000000E+01 0.39100000E+03 l 0.30000000E+01 0.13200000E+03 a 0.30000000E+01 0.14100000E+03 b 0.30000000E+01 0.17800000E+03 c 0.30000000E+01 0.19300000E+03 d 0.30000000E+01 0.23600000E+03 e 0.30000000E+01 0.23500000E+03 f 0.30000000E+01 0.26700000E+03 g 0.30000000E+01 0.31700000E+03 h 0.30000000E+01 0.35600000E+03 i 0.30000000E+01 0.36200000E+03 j 0.30000000E+01 0.40600000E+03 k 0.30000000E+01 0.41900000E+03 l 0.40000000E+01 0.31300000E+03 h 0.40000000E+01 0.34800000E+03 i 0.40000000E+01 0.34800000E+03 j 0.40000000E+01 0.39600000E+03 k 0.40000000E+01 0.46100000E+03 l 0.50000000E+01 0.31800000E+03 h 0.50000000E+01 0.35500000E+03 i 0.50000000E+01 0.36300000E+03 j 0.50000000E+01 0.42000000E+03 k 0.50000000E+01 0.47200000E+03 l 0.60000000E+01 0.31500000E+03 g 0.60000000E+01 0.37400000E+03 h 0.60000000E+01 0.42200000E+03 i 0.60000000E+01 0.43500000E+03 j 0.60000000E+01 0.47200000E+03 k ierr = 0 1test of mppc test number 4 n = 12 + / m = 12 / iym = 12 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i f i i g g f e e g h g i i e d f i i 2 2 d e f g f i 200.0000 - d d e f e - i d c c c d d i i d c c b b 2 c i i c b c i i b a a c b i i 2 a a b i i 2 a a i i b i 100.0000 - a - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 85values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 a 0.10000000E+01 0.11500000E+03 b 0.10000000E+01 0.14500000E+03 c 0.10000000E+01 0.17100000E+03 d 0.10000000E+01 0.19600000E+03 e 0.10000000E+01 0.20400000E+03 f 0.10000000E+01 0.24200000E+03 g 0.10000000E+01 0.28400000E+03 h 0.10000000E+01 0.31500000E+03 i 0.10000000E+01 0.34000000E+03 j 0.10000000E+01 0.36000000E+03 k 0.10000000E+01 0.41700000E+03 l 0.20000000E+01 0.11800000E+03 a 0.20000000E+01 0.12600000E+03 b 0.20000000E+01 0.15000000E+03 c 0.20000000E+01 0.18000000E+03 d 0.20000000E+01 0.19600000E+03 e 0.20000000E+01 0.18800000E+03 f 0.20000000E+01 0.23300000E+03 g 0.20000000E+01 0.27700000E+03 h 0.20000000E+01 0.30100000E+03 i 0.20000000E+01 0.31800000E+03 j 0.20000000E+01 0.34200000E+03 k 0.20000000E+01 0.39100000E+03 l 0.30000000E+01 0.13200000E+03 a 0.30000000E+01 0.14100000E+03 b 0.30000000E+01 0.17800000E+03 c 0.30000000E+01 0.19300000E+03 d 0.30000000E+01 0.23600000E+03 e 0.30000000E+01 0.23500000E+03 f 0.30000000E+01 0.26700000E+03 g 0.30000000E+01 0.31700000E+03 h 0.30000000E+01 0.35600000E+03 i 0.30000000E+01 0.36200000E+03 j 0.30000000E+01 0.40600000E+03 k 0.30000000E+01 0.41900000E+03 l 0.40000000E+01 0.31300000E+03 h 0.40000000E+01 0.34800000E+03 i 0.40000000E+01 0.34800000E+03 j 0.40000000E+01 0.39600000E+03 k 0.40000000E+01 0.46100000E+03 l 0.50000000E+01 0.31800000E+03 h 0.50000000E+01 0.35500000E+03 i 0.50000000E+01 0.36300000E+03 j 0.50000000E+01 0.42000000E+03 k 0.50000000E+01 0.47200000E+03 l 0.60000000E+01 0.31500000E+03 g 0.60000000E+01 0.37400000E+03 h 0.60000000E+01 0.42200000E+03 i 0.60000000E+01 0.43500000E+03 j ierr = 0 1test of mppmc test number 4 n = 12 + / m = 12 / iym = 12 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i f i i g g f e g h g i i e d f i i 2 2 e f g f i 200.0000 - d d e f e - i d c c d d i i d c c b d c i i c b c i i b a c b i i 2 a a b i i 2 a a i i b i 100.0000 - a - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 77values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+01 0.11200000E+03 a 0.10000000E+01 0.11500000E+03 b 0.10000000E+01 0.14500000E+03 c 0.10000000E+01 0.17100000E+03 d 0.10000000E+01 0.19600000E+03 e 0.10000000E+01 0.20400000E+03 f 0.10000000E+01 0.24200000E+03 g 0.10000000E+01 0.28400000E+03 h 0.10000000E+01 0.31500000E+03 i 0.10000000E+01 0.34000000E+03 j 0.10000000E+01 0.36000000E+03 k 0.10000000E+01 0.41700000E+03 l 0.20000000E+01 0.11800000E+03 a 0.20000000E+01 0.12600000E+03 b 0.20000000E+01 0.15000000E+03 c 0.20000000E+01 0.19600000E+03 e 0.20000000E+01 0.18800000E+03 f 0.20000000E+01 0.23300000E+03 g 0.20000000E+01 0.27700000E+03 h 0.20000000E+01 0.30100000E+03 i 0.20000000E+01 0.31800000E+03 j 0.20000000E+01 0.34200000E+03 k 0.20000000E+01 0.39100000E+03 l 0.30000000E+01 0.13200000E+03 a 0.30000000E+01 0.14100000E+03 b 0.30000000E+01 0.17800000E+03 c 0.30000000E+01 0.19300000E+03 d 0.30000000E+01 0.23600000E+03 e 0.30000000E+01 0.23500000E+03 f 0.30000000E+01 0.26700000E+03 g 0.30000000E+01 0.31700000E+03 h 0.30000000E+01 0.35600000E+03 i 0.30000000E+01 0.36200000E+03 j 0.30000000E+01 0.40600000E+03 k 0.30000000E+01 0.41900000E+03 l 0.40000000E+01 0.31300000E+03 h 0.40000000E+01 0.34800000E+03 i 0.40000000E+01 0.34800000E+03 j 0.40000000E+01 0.39600000E+03 k 0.40000000E+01 0.46100000E+03 l 0.50000000E+01 0.31800000E+03 h 0.50000000E+01 0.35500000E+03 i 0.50000000E+01 0.36300000E+03 j 0.50000000E+01 0.42000000E+03 k 0.50000000E+01 0.47200000E+03 l 0.60000000E+01 0.31500000E+03 g 0.60000000E+01 0.37400000E+03 h 0.60000000E+01 0.42200000E+03 i 0.60000000E+01 0.43500000E+03 j 0.60000000E+01 0.47200000E+03 k ierr = 0 1test of mppc test number 5 n = 1 + / m = 144 / iym = 1 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i z i i x i i 4 i i x i 200.0000 - 6 - i x i i x i i 3 i i 8 i i 7 i i 6 i i 2 i 100.0000 - k - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 62values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+02 0.30200000E+03 z 0.10000000E+02 0.31500000E+03 z 0.10000000E+02 0.36400000E+03 z 0.10000000E+02 0.34700000E+03 z 0.10000000E+02 0.31200000E+03 z 0.10000000E+02 0.31700000E+03 z 0.10000000E+02 0.31300000E+03 z 0.10000000E+02 0.31800000E+03 z 0.10000000E+02 0.37400000E+03 z 0.10000000E+02 0.41300000E+03 z 0.10000000E+02 0.40500000E+03 z 0.10000000E+02 0.35500000E+03 z 0.10000000E+02 0.30600000E+03 z 0.10000000E+02 0.30600000E+03 z 0.10000000E+02 0.31500000E+03 z 0.10000000E+02 0.30100000E+03 z 0.10000000E+02 0.35600000E+03 z 0.10000000E+02 0.34800000E+03 z 0.10000000E+02 0.35500000E+03 z 0.10000000E+02 0.42200000E+03 z 0.10000000E+02 0.46500000E+03 z 0.10000000E+02 0.46700000E+03 z 0.10000000E+02 0.40400000E+03 z 0.10000000E+02 0.34700000E+03 z 0.10000000E+02 0.30500000E+03 z 0.10000000E+02 0.33600000E+03 z 0.10000000E+02 0.34000000E+03 z 0.10000000E+02 0.31800000E+03 z 0.10000000E+02 0.36200000E+03 z 0.10000000E+02 0.34800000E+03 z 0.10000000E+02 0.36300000E+03 z 0.10000000E+02 0.43500000E+03 z 0.10000000E+02 0.49100000E+03 z 0.10000000E+02 0.50500000E+03 z 0.10000000E+02 0.40400000E+03 z 0.10000000E+02 0.35900000E+03 z 0.10000000E+02 0.31000000E+03 z 0.10000000E+02 0.33700000E+03 z 0.10000000E+02 0.36000000E+03 z 0.10000000E+02 0.34200000E+03 z 0.10000000E+02 0.40600000E+03 z 0.10000000E+02 0.39600000E+03 z 0.10000000E+02 0.42000000E+03 z 0.10000000E+02 0.47200000E+03 z 0.10000000E+02 0.54800000E+03 z 0.10000000E+02 0.55900000E+03 z 0.10000000E+02 0.46300000E+03 z 0.10000000E+02 0.40700000E+03 z 0.10000000E+02 0.36200000E+03 z 0.10000000E+02 0.40500000E+03 z ierr = 0 1test of mppmc test number 5 n = 1 + / m = 144 / iym = 1 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xub = 16.0000 starpac 2.08s (03/15/90) -i--------i-----i----i--------------i--------------i- 1000.0000 - - i i 800.0000 - - i i i i i i 600.0000 - - i i i i i i 400.0000 - - i i i i i z i i x i i 4 i i x i 200.0000 - 6 - i x i i 9 i i 3 i i 8 i i 7 i i 6 i i 2 i 100.0000 - k - -i--------i-----i----i--------------i--------------i- 4.0000 8.0000 20.0000 40.0000 **note 62values fell outside the specified limit s** see next page for list 1 the first 50 values outside the plot limits are x y sym 0.10000000E+02 0.30200000E+03 z 0.10000000E+02 0.31500000E+03 z 0.10000000E+02 0.36400000E+03 z 0.10000000E+02 0.34700000E+03 z 0.10000000E+02 0.31200000E+03 z 0.10000000E+02 0.31700000E+03 z 0.10000000E+02 0.31300000E+03 z 0.10000000E+02 0.31800000E+03 z 0.10000000E+02 0.37400000E+03 z 0.10000000E+02 0.41300000E+03 z 0.10000000E+02 0.40500000E+03 z 0.10000000E+02 0.35500000E+03 z 0.10000000E+02 0.30600000E+03 z 0.10000000E+02 0.30600000E+03 z 0.10000000E+02 0.31500000E+03 z 0.10000000E+02 0.30100000E+03 z 0.10000000E+02 0.35600000E+03 z 0.10000000E+02 0.34800000E+03 z 0.10000000E+02 0.35500000E+03 z 0.10000000E+02 0.42200000E+03 z 0.10000000E+02 0.46500000E+03 z 0.10000000E+02 0.46700000E+03 z 0.10000000E+02 0.40400000E+03 z 0.10000000E+02 0.34700000E+03 z 0.10000000E+02 0.30500000E+03 z 0.10000000E+02 0.33600000E+03 z 0.10000000E+02 0.34000000E+03 z 0.10000000E+02 0.31800000E+03 z 0.10000000E+02 0.36200000E+03 z 0.10000000E+02 0.34800000E+03 z 0.10000000E+02 0.36300000E+03 z 0.10000000E+02 0.43500000E+03 z 0.10000000E+02 0.49100000E+03 z 0.10000000E+02 0.50500000E+03 z 0.10000000E+02 0.40400000E+03 z 0.10000000E+02 0.35900000E+03 z 0.10000000E+02 0.31000000E+03 z 0.10000000E+02 0.33700000E+03 z 0.10000000E+02 0.36000000E+03 z 0.10000000E+02 0.34200000E+03 z 0.10000000E+02 0.40600000E+03 z 0.10000000E+02 0.39600000E+03 z 0.10000000E+02 0.42000000E+03 z 0.10000000E+02 0.47200000E+03 z 0.10000000E+02 0.54800000E+03 z 0.10000000E+02 0.55900000E+03 z 0.10000000E+02 0.46300000E+03 z 0.10000000E+02 0.40700000E+03 z 0.10000000E+02 0.36200000E+03 z 0.10000000E+02 0.40500000E+03 z ierr = 0 1test of ppc test number 6 n = 36 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---i-----i----i--------------i--------------i----i- 5.0000 - - i i 4.0000 - - i i i i i i i i i i i i i i 2.0000 - - i i i i i i i i i i i i 1.0000 - x - i i i i 0.8000 - - i i i i 0.6000 - - i i 0.5000 - - -i---i-----i----i--------------i--------------i----i- 0.5000 0.8000 2.0000 4.0000 ierr = 0 1test of ppmc test number 6 n = 36 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---i-----i----i--------------i--------------i----i- 5.0000 - - i i 4.0000 - - i i i i i i i i i i i i i i 2.0000 - - i i i i i i i i i i i i 1.0000 - x - i i i i 0.8000 - - i i i i 0.6000 - - i i 0.5000 - - -i---i-----i----i--------------i--------------i----i- 0.5000 0.8000 2.0000 4.0000 ierr = 0 1test of sppc test number 6 n = 36 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---i-----i----i--------------i--------------i----i- 5.0000 - - i i 4.0000 - - i i i i i i i i i i i i i i 2.0000 - - i i i i i i i i i i i i 1.0000 - x - i i i i 0.8000 - - i i i i 0.6000 - - i i 0.5000 - - -i---i-----i----i--------------i--------------i----i- 0.5000 0.8000 2.0000 4.0000 ierr = 0 1test of sppmc test number 6 n = 36 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---i-----i----i--------------i--------------i----i- 5.0000 - - i i 4.0000 - - i i i i i i i i i i i i i i 2.0000 - - i i i i i i i i i i i i 1.0000 - x - i i i i 0.8000 - - i i i i 0.6000 - - i i 0.5000 - - -i---i-----i----i--------------i--------------i----i- 0.5000 0.8000 2.0000 4.0000 ierr = 0 1test of mppc test number 6 n = 6 + / m = 6 / iym = 12 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---i-----i----i--------------i--------------i----i- 5.0000 - - i i 4.0000 - - i i i i i i i i i i i i i i 2.0000 - - i i i i i i i i i i i i 1.0000 - x - i i i i 0.8000 - - i i i i 0.6000 - - i i 0.5000 - - -i---i-----i----i--------------i--------------i----i- 0.5000 0.8000 2.0000 4.0000 ierr = 0 1test of mppmc test number 6 n = 6 + / m = 6 / iym = 12 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) -i---i-----i----i--------------i--------------i----i- 5.0000 - - i i 4.0000 - - i i i i i i i i i i i i i i 2.0000 - - i i i i i i i i i i i i 1.0000 - x - i i i i 0.8000 - - i i i i 0.6000 - - i i 0.5000 - - -i---i-----i----i--------------i--------------i----i- 0.5000 0.8000 2.0000 4.0000 ierr = 0 1test of pp test number 7 n = 0 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine pp ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call pp (y, x, n) ierr = 1 1test of ppm test number 7 n = 0 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ppm ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call ppm (y, ymiss, x, xmiss, n) ierr = 1 1test of spp test number 7 n = 0 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine spp ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call spp (y, x, n, isym) ierr = 1 1test of sppm test number 7 n = 0 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine sppm ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call sppm (y, ymiss, x, xmiss, n, isym) ierr = 1 1test of mpp test number 7 n = 0 + / m = 0 / iym = -1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mpp ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mpp (ym, x, n, m, iym) ierr = 1 1test of mppm test number 7 n = 0 + / m = 0 / iym = -1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppm ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mppm (ym, ymmiss, x, xmiss, n, m, iym) ierr = 1 1test of ppl test number 7 n = 0 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ppl ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call ppl (y, x, n, ilog) ierr = 1 1test of ppml test number 7 n = 0 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ppml ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call ppml (y, ymiss, x, xmiss, n, ilog) ierr = 1 1test of sppl test number 7 n = 0 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine sppl ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call sppl (y, x, n, isym, ilog) ierr = 1 1test of sppml test number 7 n = 0 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine sppml ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call sppml (y, ymiss, x, xmiss, n, isym, ilog) ierr = 1 1test of mppl test number 7 n = 0 + / m = 0 / iym = -1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppl ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mppl (ym, x, n, m, iym, ilog) ierr = 1 1test of mppml test number 7 n = 0 + / m = 0 / iym = -1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppml ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mppml (ym, ymmiss, x, xmiss, n, m, iym, ilog) ierr = 1 1test of ppc test number 7 n = 0 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ppc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call ppc (y, x, n, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of ppmc test number 7 n = 0 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ppmc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call ppmc (y, ymiss, x, xmiss, n, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of sppc test number 7 n = 0 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine sppc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call sppc (y, x, n, isym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of sppmc test number 7 n = 0 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine sppmc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call sppmc (y, ymiss, x, xmiss, n, isym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of mppc test number 7 n = 0 + / m = 0 / iym = -1 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mppc (ym, x, n, m, iym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of mppmc test number 7 n = 0 + / m = 0 / iym = -1 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppmc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mppmc (ym, ymmiss, x, xmiss, n, m, iym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of mppc test number 8 n = 12 + / m = 12 / iym = -1 + / ilog = 22 isize = 22 / nout = 55 + / ylb = -1.0000 / yub = 0.0000 / xlb = -1.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppc ------------------------------------- the input value of iym is -1. the first dimension of ym , as indicated by the argument iym , must be greater than or equal to n . the input value of xlb is -1.0000000 . the value of the argument xlb must be greater than .00000000000000 . the input value of xub is .00000000 . the value of the argument xub must be greater than .00000000000000 . the input value of ylb is -1.0000000 . the value of the argument ylb must be greater than .00000000000000 . the input value of yub is .00000000 . the value of the argument yub must be greater than .00000000000000 . the correct form of the call statement is call mppc (ym, x, n, m, iym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of mppmc test number 8 n = 12 + / m = 12 / iym = -1 + / ilog = 22 isize = 22 / nout = 55 + / ylb = -1.0000 / yub = 0.0000 / xlb = -1.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppmc ------------------------------------- the input value of iym is -1. the first dimension of ym , as indicated by the argument iym , must be greater than or equal to n . the input value of xlb is -1.0000000 . the value of the argument xlb must be greater than .00000000000000 . the input value of xub is .00000000 . the value of the argument xub must be greater than .00000000000000 . the input value of ylb is -1.0000000 . the value of the argument ylb must be greater than .00000000000000 . the input value of yub is .00000000 . the value of the argument yub must be greater than .00000000000000 . the correct form of the call statement is call mppmc (ym, ymmiss, x, xmiss, n, m, iym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of mppmc test number 9 n = 12 + / m = 12 / iym = 12 + / ilog = 22 isize = 22 / nout = 55 + / ylb = -1.0000 / yub = 0.0000 / xlb = -1.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppmc ------------------------------------- the number of values in vector x less than or equal to 0.0000000E+00 is 1. the values in the vector x must all be greater than 0.0000000E+00. the input value of xlb is -1.0000000 . the value of the argument xlb must be greater than .00000000000000 . the input value of xub is .00000000 . the value of the argument xub must be greater than .00000000000000 . the number of values in array ym less than or equal to 0.0000000E+00 is 1. the values in the array ym must all be greater than 0.0000000E+00. the input value of ylb is -1.0000000 . the value of the argument ylb must be greater than .00000000000000 . the input value of yub is .00000000 . the value of the argument yub must be greater than .00000000000000 . the correct form of the call statement is call mppmc (ym, ymmiss, x, xmiss, n, m, iym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test of mppmc test number 10 n = 12 + / m = 12 / iym = 12 + / ilog = 22 isize = 22 / nout = 55 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xub = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mppmc ------------------------------------- no non-missing plot coordinates were found. the plot has been suppressed. the correct form of the call statement is call mppmc (ym, ymmiss, x, xmiss, n, m, iym, ilog, + isize, nout, ylb, yub, xlb, xub) ierr = 1 1test runs for the statistical analysis family routines. 1try two or fewer elements. call to stat starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stat ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to three . the correct form of the call statement is call stat (y, n, ldstak) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 1 call to stats starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stats ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to three . the correct form of the call statement is call stats (y, n, ldstak, sts, nprt) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 1 call to statw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statw ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to three . the number of nonzero weights found is 2. the number of nonzero weights in wt must be greater than or equal to 3. the correct form of the call statement is call statw (y, wt, n, ldstak) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 1 call to statws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statws ------------------------------------- the input value of n is 2. the value of the argument n must be greater than or equal to three . the number of nonzero weights found is 2. the number of nonzero weights in wt must be greater than or equal to 3. the correct form of the call statement is call statws (y, wt, n, ldstak, sts, nprt) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 1 1try insufficient work area. call to stat starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stat ------------------------------------- the input value of ldstak is 21. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 49. the correct form of the call statement is call stat (y, n, ldstak) the value of ierr is 1 call to stats starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stats ------------------------------------- the input value of ldstak is 21. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 49. the correct form of the call statement is call stats (y, n, ldstak, sts, nprt) the value of ierr is 1 call to statw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statw ------------------------------------- the input value of ldstak is 21. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 49. the correct form of the call statement is call statw (y, wt, n, ldstak) the value of ierr is 1 call to statws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statws ------------------------------------- the input value of ldstak is 21. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 49. the correct form of the call statement is call statws (y, wt, n, ldstak, sts, nprt) the value of ierr is 1 1try negative weights. call to statw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statw ------------------------------------- negative values were found in the vector wt . all weights must be greater than or equal to zero. the correct form of the call statement is call statw (y, wt, n, ldstak) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 1 call to statws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statws ------------------------------------- negative values were found in the vector wt . all weights must be greater than or equal to zero. the correct form of the call statement is call statws (y, wt, n, ldstak, sts, nprt) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 1 1try all weights zero (and constant y). call to statw starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statw ------------------------------------- the number of nonzero weights found is 0. the number of nonzero weights in wt must be greater than or equal to 3. the correct form of the call statement is call statw (y, wt, n, ldstak) the value of ierr is 1 call to statws starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine statws ------------------------------------- the number of nonzero weights found is 0. the number of nonzero weights in wt must be greater than or equal to 3. the correct form of the call statement is call statws (y, wt, n, ldstak, sts, nprt) the value of ierr is 1 1test runs to be sure code is not reading outside data array. 1 call to stat starpac 2.08s (03/15/90) +statistical analysis n = 8 frequency distribution (1-6) 8 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 8.0000000E+00 wtd sum of squares = 8.0000000E+00 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 8.0000000E+00 expected no. of runs = 5.0 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.05 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 8 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1 call to stats starpac 2.08s (03/15/90) +statistical analysis n = 8 frequency distribution (1-6) 8 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 8.0000000E+00 wtd sum of squares = 8.0000000E+00 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 8.0000000E+00 expected no. of runs = 5.0 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.05 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 8 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1 call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 8 frequency distribution (1-6) 8 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 4.0000000E+00 wtd sum of squares = 4.0000000E+00 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 4.0000000E+00 expected no. of runs = 5.0 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.05 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 8 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1 call to statws starpac 2.08s (03/15/90) +weighted statistical analysis n = 8 frequency distribution (1-6) 8 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 4.0000000E+00 wtd sum of squares = 4.0000000E+00 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 4.0000000E+00 expected no. of runs = 5.0 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.05 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 8 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1try constant y. 1 call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 10 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 1.0000000E+01 wtd sum of squares = 1.0000000E+01 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 1.0000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 10 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1 call to stats starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 10 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 1.0000000E+01 wtd sum of squares = 1.0000000E+01 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 1.0000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 10 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1 call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 10 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 5.0000000E+00 wtd sum of squares = 5.0000000E+00 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 5.0000000E+00 expected no. of runs = 6.3 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 10 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1 call to statws starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 10 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 1.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 1.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 1.0000000E+00 range = 0.0000000E+00 mid-range = 1.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 1.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 1.0000000E+00 coef. of. var. (percent) = 0.0000000E+00 a two-sided 95 pct confidence interval for mean is 1.0000000E+00 to 1.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 5.0000000E+00 wtd sum of squares = 5.0000000E+00 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 5.0000000E+00 expected no. of runs = 6.3 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 10 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1test3. try turning off the print for those routines which allow it. try turning the print off for stats. call to stats data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 try turning the print off for statws. call to statws data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 1test 4. make working runs of all routines to check the statistics. 1run stat on the davis-harrison pikes peak data. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 3 frequency distribution (1-6) 1 0 0 0 0 0 0 0 0 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.0800004E-01 wtd standard deviation = 1.1269394E-03 weighted mean = 6.0800004E-01 weighted s.d. of mean = 6.5063877E-04 median = 6.0860002E-01 range = 1.9999743E-03 mid-range = 6.0769999E-01 mean deviation = 8.6665154E-04 25 pct unwtd trimmed mean= 6.0800004E-01 variance = 1.2699924E-06 25 pct wtd trimmed mean = 6.0800004E-01 coef. of. var. (percent) = 1.8535186E-01 a two-sided 95 pct confidence interval for mean is 6.0520059E-01 to 6.1079949E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 5.8672600E-04 to 7.0824912E-03 (2-7) linear trend statistics (5-1) other statistics slope = 9.4988942E-04 minimum = 6.0670000E-01 s.d. of slope = 6.0638477E-04 maximum = 6.0869998E-01 slope/s.d. of slope = t = 1.5664797E+00 beta one = 4.9137717E-01 prob exceeding abs value of obs t = 0.362 beta two = 1.5001210E+00 wtd sum of values = 1.8240001E+00 wtd sum of squares = 1.1089946E+00 tests for non-randomness wtd sum of dev squared = 2.5399847E-06 students t = 9.3446637E+02 hno. of runs up and down = 2 wtd sum absolute values = 1.8240001E+00 expected no. of runs = 1.7 wtd ave absolute values = 6.0800004E-01 s.d. of no. of runs = 0.46 mean sq successive diff = 2.0049442E-06 mean sq succ diff/var = 1.579 deviations from wtd mean no. of + signs = 2 no. of - signs = 1 no. of runs = 2 expected no. of runs= 2.3 s.d. of runs = 0.47 diff./s.d. of runs = -0.707 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) 1run stat on the davis-harrison pikes peak data. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 84 frequency distribution (1-6) 5 25 35 8 1 0 0 4 4 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.3734055E-01 wtd standard deviation = 3.2405213E-02 weighted mean = 6.3734055E-01 weighted s.d. of mean = 3.5356984E-03 median = 6.2915003E-01 range = 1.4670002E-01 mid-range = 6.6845000E-01 mean deviation = 2.1076450E-02 25 pct unwtd trimmed mean= 6.2885946E-01 variance = 1.0500979E-03 25 pct wtd trimmed mean = 6.2885946E-01 coef. of. var. (percent) = 5.0844421E+00 a two-sided 95 pct confidence interval for mean is 6.3030815E-01 to 6.4437294E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.8136952E-02 to 3.8211718E-02 (2-7) linear trend statistics (5-1) other statistics slope = -2.4737162E-04 minimum = 5.9509999E-01 s.d. of slope = 1.4414075E-04 maximum = 7.4180001E-01 slope/s.d. of slope = t = -1.7161810E+00 beta one = 3.7288008E+00 prob exceeding abs value of obs t = 0.090 beta two = 5.9283767E+00 wtd sum of values = 5.3536606E+01 wtd sum of squares = 3.4208202E+01 tests for non-randomness wtd sum of dev squared = 8.7158121E-02 students t = 1.8025874E+02 hno. of runs up and down = 47 wtd sum absolute values = 5.3536606E+01 expected no. of runs = 55.7 wtd ave absolute values = 6.3734055E-01 s.d. of no. of runs = 3.82 mean sq successive diff = 3.6382340E-04 mean sq succ diff/var = 0.346 deviations from wtd mean no. of + signs = 22 no. of - signs = 62 no. of runs = 14 expected no. of runs= 33.5 s.d. of runs = 3.51 diff./s.d. of runs = -5.550 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 0 1run statw on the davis-harrison pikes peak data. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 3 frequency distribution (1-6) 1 0 0 0 0 0 0 0 0 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.0800004E-01 wtd standard deviation = 7.9686649E-04 weighted mean = 6.0800004E-01 weighted s.d. of mean = 6.5063871E-04 median = 6.0864997E-01 range = 1.9999743E-03 mid-range = 6.0769999E-01 mean deviation = 8.6665154E-04 25 pct unwtd trimmed mean= 6.0800004E-01 variance = 6.3499618E-07 25 pct wtd trimmed mean = 6.0800004E-01 coef. of. var. (percent) = 1.3106357E-01 a two-sided 95 pct confidence interval for mean is 6.0520059E-01 to 6.1079949E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 4.1487793E-04 to 5.0080772E-03 (2-7) linear trend statistics (5-1) other statistics slope = 9.4988942E-04 minimum = 6.0670000E-01 s.d. of slope = 6.0638477E-04 maximum = 6.0869998E-01 slope/s.d. of slope = t = 1.5664797E+00 beta one = 4.9137717E-01 prob exceeding abs value of obs t = 0.362 beta two = 1.5001210E+00 wtd sum of values = 9.1200006E-01 wtd sum of squares = 5.5449730E-01 tests for non-randomness wtd sum of dev squared = 1.2699924E-06 students t = 9.3446637E+02 hno. of runs up and down = 2 wtd sum absolute values = 9.1200006E-01 expected no. of runs = 1.7 wtd ave absolute values = 6.0800004E-01 s.d. of no. of runs = 0.46 mean sq successive diff = 2.0049442E-06 mean sq succ diff/var = 3.157 deviations from wtd mean no. of + signs = 2 no. of - signs = 1 no. of runs = 2 expected no. of runs= 2.3 s.d. of runs = 0.47 diff./s.d. of runs = -0.707 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) 1run statw on the davis-harrison pikes peak data. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 72 (no. of non-zero wts) length = 84 all computations are based on observations with non-zero weights frequency distribution (1-6) 7 8 13 10 17 9 6 0 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.2726671E-01 wtd standard deviation = 9.4298599E-03 weighted mean = 6.2970537E-01 weighted s.d. of mean = 1.2601180E-03 median = 6.2715000E-01 range = 5.5599988E-02 mid-range = 6.3450003E-01 mean deviation = 8.8535249E-03 25 pct unwtd trimmed mean= 6.2723887E-01 variance = 8.8922250E-05 25 pct wtd trimmed mean = 6.2813753E-01 coef. of. var. (percent) = 1.4975035E+00 a two-sided 95 pct confidence interval for mean is 6.2719274E-01 to 6.3221800E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 8.1016403E-03 to 1.1283091E-02 (2-7) linear trend statistics (5-1) other statistics slope = 1.4308283E-04 minimum = 6.0670000E-01 s.d. of slope = 6.1556275E-05 maximum = 6.6229999E-01 slope/s.d. of slope = t = 2.3244231E+00 beta one = 8.3442859E-02 prob exceeding abs value of obs t = 0.023 beta two = 2.9809062E+00 wtd sum of values = 3.5263500E+01 wtd sum of squares = 2.2211927E+01 tests for non-randomness wtd sum of dev squared = 6.3134795E-03 students t = 4.9971936E+02 hno. of runs up and down = 43 wtd sum absolute values = 3.5263500E+01 expected no. of runs = 47.7 wtd ave absolute values = 6.2970537E-01 s.d. of no. of runs = 3.53 mean sq successive diff = 6.6715191E-05 mean sq succ diff/var = 0.750 deviations from wtd mean no. of + signs = 31 no. of - signs = 41 no. of runs = 12 expected no. of runs= 36.3 s.d. of runs = 4.13 diff./s.d. of runs = -5.885 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 0 1run stats on the davis-harrison pikes peak data. call to stats starpac 2.08s (03/15/90) +statistical analysis n = 3 frequency distribution (1-6) 1 0 0 0 0 0 0 0 0 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.0800004E-01 wtd standard deviation = 1.1269394E-03 weighted mean = 6.0800004E-01 weighted s.d. of mean = 6.5063877E-04 median = 6.0860002E-01 range = 1.9999743E-03 mid-range = 6.0769999E-01 mean deviation = 8.6665154E-04 25 pct unwtd trimmed mean= 6.0800004E-01 variance = 1.2699924E-06 25 pct wtd trimmed mean = 6.0800004E-01 coef. of. var. (percent) = 1.8535186E-01 a two-sided 95 pct confidence interval for mean is 6.0520059E-01 to 6.1079949E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 5.8672600E-04 to 7.0824912E-03 (2-7) linear trend statistics (5-1) other statistics slope = 9.4988942E-04 minimum = 6.0670000E-01 s.d. of slope = 6.0638477E-04 maximum = 6.0869998E-01 slope/s.d. of slope = t = 1.5664797E+00 beta one = 4.9137717E-01 prob exceeding abs value of obs t = 0.362 beta two = 1.5001210E+00 wtd sum of values = 1.8240001E+00 wtd sum of squares = 1.1089946E+00 tests for non-randomness wtd sum of dev squared = 2.5399847E-06 students t = 9.3446637E+02 hno. of runs up and down = 2 wtd sum absolute values = 1.8240001E+00 expected no. of runs = 1.7 wtd ave absolute values = 6.0800004E-01 s.d. of no. of runs = 0.46 mean sq successive diff = 2.0049442E-06 mean sq succ diff/var = 1.579 deviations from wtd mean no. of + signs = 2 no. of - signs = 1 no. of runs = 2 expected no. of runs= 2.3 s.d. of runs = 0.47 diff./s.d. of runs = -0.707 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) 1run stats on the davis-harrison pikes peak data. call to stats starpac 2.08s (03/15/90) +statistical analysis n = 84 frequency distribution (1-6) 5 25 35 8 1 0 0 4 4 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.3734055E-01 wtd standard deviation = 3.2405213E-02 weighted mean = 6.3734055E-01 weighted s.d. of mean = 3.5356984E-03 median = 6.2915003E-01 range = 1.4670002E-01 mid-range = 6.6845000E-01 mean deviation = 2.1076450E-02 25 pct unwtd trimmed mean= 6.2885946E-01 variance = 1.0500979E-03 25 pct wtd trimmed mean = 6.2885946E-01 coef. of. var. (percent) = 5.0844421E+00 a two-sided 95 pct confidence interval for mean is 6.3030815E-01 to 6.4437294E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.8136952E-02 to 3.8211718E-02 (2-7) linear trend statistics (5-1) other statistics slope = -2.4737162E-04 minimum = 5.9509999E-01 s.d. of slope = 1.4414075E-04 maximum = 7.4180001E-01 slope/s.d. of slope = t = -1.7161810E+00 beta one = 3.7288008E+00 prob exceeding abs value of obs t = 0.090 beta two = 5.9283767E+00 wtd sum of values = 5.3536606E+01 wtd sum of squares = 3.4208202E+01 tests for non-randomness wtd sum of dev squared = 8.7158121E-02 students t = 1.8025874E+02 hno. of runs up and down = 47 wtd sum absolute values = 5.3536606E+01 expected no. of runs = 55.7 wtd ave absolute values = 6.3734055E-01 s.d. of no. of runs = 3.82 mean sq successive diff = 3.6382340E-04 mean sq succ diff/var = 0.346 deviations from wtd mean no. of + signs = 22 no. of - signs = 62 no. of runs = 14 expected no. of runs= 33.5 s.d. of runs = 3.51 diff./s.d. of runs = -5.550 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 0 1run statws on the davis-harrison pikes peak data. call to statws starpac 2.08s (03/15/90) +weighted statistical analysis n = 3 frequency distribution (1-6) 1 0 0 0 0 0 0 0 0 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.0800004E-01 wtd standard deviation = 7.9686649E-04 weighted mean = 6.0800004E-01 weighted s.d. of mean = 6.5063871E-04 median = 6.0864997E-01 range = 1.9999743E-03 mid-range = 6.0769999E-01 mean deviation = 8.6665154E-04 25 pct unwtd trimmed mean= 6.0800004E-01 variance = 6.3499618E-07 25 pct wtd trimmed mean = 6.0800004E-01 coef. of. var. (percent) = 1.3106357E-01 a two-sided 95 pct confidence interval for mean is 6.0520059E-01 to 6.1079949E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 4.1487793E-04 to 5.0080772E-03 (2-7) linear trend statistics (5-1) other statistics slope = 9.4988942E-04 minimum = 6.0670000E-01 s.d. of slope = 6.0638477E-04 maximum = 6.0869998E-01 slope/s.d. of slope = t = 1.5664797E+00 beta one = 4.9137717E-01 prob exceeding abs value of obs t = 0.362 beta two = 1.5001210E+00 wtd sum of values = 9.1200006E-01 wtd sum of squares = 5.5449730E-01 tests for non-randomness wtd sum of dev squared = 1.2699924E-06 students t = 9.3446637E+02 hno. of runs up and down = 2 wtd sum absolute values = 9.1200006E-01 expected no. of runs = 1.7 wtd ave absolute values = 6.0800004E-01 s.d. of no. of runs = 0.46 mean sq successive diff = 2.0049442E-06 mean sq succ diff/var = 3.157 deviations from wtd mean no. of + signs = 2 no. of - signs = 1 no. of runs = 2 expected no. of runs= 2.3 s.d. of runs = 0.47 diff./s.d. of runs = -0.707 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) 1run statws on the davis-harrison pikes peak data. call to statws starpac 2.08s (03/15/90) +weighted statistical analysis n = 72 (no. of non-zero wts) length = 84 all computations are based on observations with non-zero weights frequency distribution (1-6) 7 8 13 10 17 9 6 0 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.2726671E-01 wtd standard deviation = 9.4298599E-03 weighted mean = 6.2970537E-01 weighted s.d. of mean = 1.2601180E-03 median = 6.2715000E-01 range = 5.5599988E-02 mid-range = 6.3450003E-01 mean deviation = 8.8535249E-03 25 pct unwtd trimmed mean= 6.2723887E-01 variance = 8.8922250E-05 25 pct wtd trimmed mean = 6.2813753E-01 coef. of. var. (percent) = 1.4975035E+00 a two-sided 95 pct confidence interval for mean is 6.2719274E-01 to 6.3221800E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 8.1016403E-03 to 1.1283091E-02 (2-7) linear trend statistics (5-1) other statistics slope = 1.4308283E-04 minimum = 6.0670000E-01 s.d. of slope = 6.1556275E-05 maximum = 6.6229999E-01 slope/s.d. of slope = t = 2.3244231E+00 beta one = 8.3442859E-02 prob exceeding abs value of obs t = 0.023 beta two = 2.9809062E+00 wtd sum of values = 3.5263500E+01 wtd sum of squares = 2.2211927E+01 tests for non-randomness wtd sum of dev squared = 6.3134795E-03 students t = 4.9971936E+02 hno. of runs up and down = 43 wtd sum absolute values = 3.5263500E+01 expected no. of runs = 47.7 wtd ave absolute values = 6.2970537E-01 s.d. of no. of runs = 3.53 mean sq successive diff = 6.6715191E-05 mean sq succ diff/var = 0.750 deviations from wtd mean no. of + signs = 31 no. of - signs = 41 no. of runs = 12 expected no. of runs= 36.3 s.d. of runs = 4.13 diff./s.d. of runs = -5.885 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) data = 0.6067 0.6087 0.6086 0.6134 0.6108 0.6138 0.6125 0.6122 0.6110 0.6104 the value of ierr is 0 1run statw on the davis-harrison pikes peak data. weights all equal to one. compare to stat above, not to statw. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 84 frequency distribution (1-6) 5 25 35 8 1 0 0 4 4 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.3734055E-01 wtd standard deviation = 3.2405213E-02 weighted mean = 6.3734055E-01 weighted s.d. of mean = 3.5356984E-03 median = 6.2915003E-01 range = 1.4670002E-01 mid-range = 6.6845000E-01 mean deviation = 2.1076450E-02 25 pct unwtd trimmed mean= 6.2885946E-01 variance = 1.0500979E-03 25 pct wtd trimmed mean = 6.2885946E-01 coef. of. var. (percent) = 5.0844421E+00 a two-sided 95 pct confidence interval for mean is 6.3030815E-01 to 6.4437294E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.8136952E-02 to 3.8211718E-02 (2-7) linear trend statistics (5-1) other statistics slope = -2.4737162E-04 minimum = 5.9509999E-01 s.d. of slope = 1.4414075E-04 maximum = 7.4180001E-01 slope/s.d. of slope = t = -1.7161810E+00 beta one = 3.7288008E+00 prob exceeding abs value of obs t = 0.090 beta two = 5.9283767E+00 wtd sum of values = 5.3536606E+01 wtd sum of squares = 3.4208202E+01 tests for non-randomness wtd sum of dev squared = 8.7158121E-02 students t = 1.8025874E+02 hno. of runs up and down = 47 wtd sum absolute values = 5.3536606E+01 expected no. of runs = 55.7 wtd ave absolute values = 6.3734055E-01 s.d. of no. of runs = 3.82 mean sq successive diff = 3.6382340E-04 mean sq succ diff/var = 0.346 deviations from wtd mean no. of + signs = 22 no. of - signs = 62 no. of runs = 14 expected no. of runs= 33.5 s.d. of runs = 3.51 diff./s.d. of runs = -5.550 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 test 6. try different paths through the summation code. 1run stat on 1, ..., 10. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 5.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = 5.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = 5.5000000E+00 range = 9.0000000E+00 mid-range = 5.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= 5.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = 5.5000000E+00 coef. of. var. (percent) = 5.5048187E+01 a two-sided 95 pct confidence interval for mean is 3.3320742E+00 to 7.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+01 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = 5.5000000E+01 wtd sum of squares = 3.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = 5.7445626E+00 hno. of runs up and down = 1 wtd sum absolute values = 5.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 5.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run statw on 1, ..., 10. weights are all 1. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 5.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = 5.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = 5.5000000E+00 range = 9.0000000E+00 mid-range = 5.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= 5.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = 5.5000000E+00 coef. of. var. (percent) = 5.5048187E+01 a two-sided 95 pct confidence interval for mean is 3.3320742E+00 to 7.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = 1.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 1.0000000E+01 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = 5.5000000E+01 wtd sum of squares = 3.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = 5.7445626E+00 hno. of runs up and down = 1 wtd sum absolute values = 5.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 5.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run stat on -1, ..., -10. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = -5.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = -5.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = -5.5000000E+00 range = 9.0000000E+00 mid-range = -5.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= -5.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = -5.5000000E+00 coef. of. var. (percent) = 5.5048187E+01 a two-sided 95 pct confidence interval for mean is-7.6679258E+00 to -3.3320742E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = -1.0000000E+00 minimum = -1.0000000E+01 s.d. of slope = 0.0000000E+00 maximum = -1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = -5.5000000E+01 wtd sum of squares = 3.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = -5.7445626E+00 hno. of runs up and down = 1 wtd sum absolute values = 5.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 5.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run statw on -1, ..., -10. weights are all 1. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = -5.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = -5.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = -5.5000000E+00 range = 9.0000000E+00 mid-range = -5.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= -5.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = -5.5000000E+00 coef. of. var. (percent) = 5.5048187E+01 a two-sided 95 pct confidence interval for mean is-7.6679258E+00 to -3.3320742E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = -1.0000000E+00 minimum = -1.0000000E+01 s.d. of slope = 0.0000000E+00 maximum = -1.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = -5.5000000E+01 wtd sum of squares = 3.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = -5.7445626E+00 hno. of runs up and down = 1 wtd sum absolute values = 5.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 5.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run stat on 0, ..., 9. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 4.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = 4.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = 4.5000000E+00 range = 9.0000000E+00 mid-range = 4.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= 4.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = 4.5000000E+00 coef. of. var. (percent) = 6.7281120E+01 a two-sided 95 pct confidence interval for mean is 2.3320742E+00 to 6.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = 0.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 9.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = 4.5000000E+01 wtd sum of squares = 2.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = 4.7000966E+00 hno. of runs up and down = 1 wtd sum absolute values = 4.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 4.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run statw on 0, ..., 9. weights are all 1. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 4.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = 4.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = 4.5000000E+00 range = 9.0000000E+00 mid-range = 4.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= 4.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = 4.5000000E+00 coef. of. var. (percent) = 6.7281120E+01 a two-sided 95 pct confidence interval for mean is 2.3320742E+00 to 6.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = 0.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 9.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = 4.5000000E+01 wtd sum of squares = 2.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = 4.7000966E+00 hno. of runs up and down = 1 wtd sum absolute values = 4.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 4.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run stat on 0, ..., -9. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = -4.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = -4.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = -4.5000000E+00 range = 9.0000000E+00 mid-range = -4.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= -4.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = -4.5000000E+00 coef. of. var. (percent) = 6.7281120E+01 a two-sided 95 pct confidence interval for mean is-6.6679258E+00 to -2.3320742E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = -1.0000000E+00 minimum = -9.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 0.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = -4.5000000E+01 wtd sum of squares = 2.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = -4.7000966E+00 hno. of runs up and down = 1 wtd sum absolute values = 4.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 4.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run statw on 0, ..., -9. weights are all 1. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = -4.5000000E+00 wtd standard deviation = 3.0276504E+00 weighted mean = -4.5000000E+00 weighted s.d. of mean = 9.5742708E-01 median = -4.5000000E+00 range = 9.0000000E+00 mid-range = -4.5000000E+00 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= -4.5000000E+00 variance = 9.1666670E+00 25 pct wtd trimmed mean = -4.5000000E+00 coef. of. var. (percent) = 6.7281120E+01 a two-sided 95 pct confidence interval for mean is-6.6679258E+00 to -2.3320742E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = -1.0000000E+00 minimum = -9.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 0.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = -4.5000000E+01 wtd sum of squares = 2.8500000E+02 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = -4.7000966E+00 hno. of runs up and down = 1 wtd sum absolute values = 4.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 4.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1stat on -5, ..., 4. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = -5.0000000E-01 wtd standard deviation = 3.0276504E+00 weighted mean = -5.0000000E-01 weighted s.d. of mean = 9.5742708E-01 median = -5.0000000E-01 range = 9.0000000E+00 mid-range = -5.0000000E-01 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= -5.0000000E-01 variance = 9.1666670E+00 25 pct wtd trimmed mean = -5.0000000E-01 coef. of. var. (percent) = 6.0553009E+02 a two-sided 95 pct confidence interval for mean is-2.6679258E+00 to 1.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = -5.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 4.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = -5.0000000E+00 wtd sum of squares = 8.5000000E+01 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = -5.2223295E-01 hno. of runs up and down = 1 wtd sum absolute values = 2.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 2.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run statw on -5, ..., 4. weights are all 1. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = -5.0000000E-01 wtd standard deviation = 3.0276504E+00 weighted mean = -5.0000000E-01 weighted s.d. of mean = 9.5742708E-01 median = -5.0000000E-01 range = 9.0000000E+00 mid-range = -5.0000000E-01 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= -5.0000000E-01 variance = 9.1666670E+00 25 pct wtd trimmed mean = -5.0000000E-01 coef. of. var. (percent) = 6.0553009E+02 a two-sided 95 pct confidence interval for mean is-2.6679258E+00 to 1.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = -5.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 4.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = -5.0000000E+00 wtd sum of squares = 8.5000000E+01 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = -5.2223295E-01 hno. of runs up and down = 1 wtd sum absolute values = 2.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 2.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run stat on -4, ..., 5. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 5.0000000E-01 wtd standard deviation = 3.0276504E+00 weighted mean = 5.0000000E-01 weighted s.d. of mean = 9.5742708E-01 median = 5.0000000E-01 range = 9.0000000E+00 mid-range = 5.0000000E-01 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= 5.0000000E-01 variance = 9.1666670E+00 25 pct wtd trimmed mean = 5.0000000E-01 coef. of. var. (percent) = 6.0553009E+02 a two-sided 95 pct confidence interval for mean is-1.6679258E+00 to 2.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = -4.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 5.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = 5.0000000E+00 wtd sum of squares = 8.5000000E+01 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = 5.2223295E-01 hno. of runs up and down = 1 wtd sum absolute values = 2.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 2.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run statw on -4, ..., 5. weights are all 1. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 1 1 1 1 1 1 1 1 1 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 5.0000000E-01 wtd standard deviation = 3.0276504E+00 weighted mean = 5.0000000E-01 weighted s.d. of mean = 9.5742708E-01 median = 5.0000000E-01 range = 9.0000000E+00 mid-range = 5.0000000E-01 mean deviation = 2.5000000E+00 25 pct unwtd trimmed mean= 5.0000000E-01 variance = 9.1666670E+00 25 pct wtd trimmed mean = 5.0000000E-01 coef. of. var. (percent) = 6.0553009E+02 a two-sided 95 pct confidence interval for mean is-1.6679258E+00 to 2.6679258E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.0824852E+00 to 5.5273046E+00 (2-7) linear trend statistics (5-1) other statistics slope = 1.0000000E+00 minimum = -4.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 5.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 1.7757577E+00 wtd sum of values = 5.0000000E+00 wtd sum of squares = 8.5000000E+01 tests for non-randomness wtd sum of dev squared = 8.2500000E+01 students t = 5.2223295E-01 hno. of runs up and down = 1 wtd sum absolute values = 2.5000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 2.5000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 1.0000000E+00 mean sq succ diff/var = 0.109 deviations from wtd mean no. of + signs = 5 no. of - signs = 5 no. of runs = 2 expected no. of runs= 6.0 s.d. of runs = 1.49 diff./s.d. of runs = -2.683 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run stat on -1, 8*0, 1. call to stat starpac 2.08s (03/15/90) +statistical analysis n = 10 frequency distribution (1-6) 1 0 0 0 8 0 0 0 0 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 0.0000000E+00 wtd standard deviation = 2.3570225E+00 weighted mean = 0.0000000E+00 weighted s.d. of mean = 7.4535596E-01 median = 0.0000000E+00 range = 1.0000000E+01 mid-range = 0.0000000E+00 mean deviation = 1.0000000E+00 25 pct unwtd trimmed mean= 0.0000000E+00 variance = 5.5555553E+00 25 pct wtd trimmed mean = 0.0000000E+00 coefficient of variation = undefined (mean is zero) a two-sided 95 pct confidence interval for mean is-1.6877279E+00 to 1.6877279E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 1.6212125E+00 to 4.3030005E+00 (2-7) linear trend statistics (5-1) other statistics slope = 5.4545456E-01 minimum = -5.0000000E+00 s.d. of slope = 1.9638608E-01 maximum = 5.0000000E+00 slope/s.d. of slope = t = 2.7774603E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 0.024 beta two = 5.0000000E+00 wtd sum of values = 0.0000000E+00 wtd sum of squares = 5.0000000E+01 tests for non-randomness wtd sum of dev squared = 5.0000000E+01 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 1.0000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 5.5555553E+00 mean sq succ diff/var = 1.000 deviations from wtd mean no. of + signs = 9 no. of - signs = 1 no. of runs = 2 expected no. of runs= 2.8 s.d. of runs = 0.40 diff./s.d. of runs = -2.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1run statw on -1, 8*0, 1. weights are all 1. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 10 frequency distribution (1-6) 1 0 0 0 8 0 0 0 0 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 0.0000000E+00 wtd standard deviation = 2.3570225E+00 weighted mean = 0.0000000E+00 weighted s.d. of mean = 7.4535596E-01 median = 0.0000000E+00 range = 1.0000000E+01 mid-range = 0.0000000E+00 mean deviation = 1.0000000E+00 25 pct unwtd trimmed mean= 0.0000000E+00 variance = 5.5555553E+00 25 pct wtd trimmed mean = 0.0000000E+00 coefficient of variation = undefined (mean is zero) a two-sided 95 pct confidence interval for mean is-1.6877279E+00 to 1.6877279E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 1.6212125E+00 to 4.3030005E+00 (2-7) linear trend statistics (5-1) other statistics slope = 5.4545456E-01 minimum = -5.0000000E+00 s.d. of slope = 1.9638608E-01 maximum = 5.0000000E+00 slope/s.d. of slope = t = 2.7774603E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 0.024 beta two = 5.0000000E+00 wtd sum of values = 0.0000000E+00 wtd sum of squares = 5.0000000E+01 tests for non-randomness wtd sum of dev squared = 5.0000000E+01 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 1.0000000E+01 expected no. of runs = 6.3 wtd ave absolute values = 1.0000000E+00 s.d. of no. of runs = 1.21 mean sq successive diff = 5.5555553E+00 mean sq succ diff/var = 1.000 deviations from wtd mean no. of + signs = 9 no. of - signs = 1 no. of runs = 2 expected no. of runs= 2.8 s.d. of runs = 0.40 diff./s.d. of runs = -2.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 series with nonzero values weighted zero. call to statw starpac 2.08s (03/15/90) +weighted statistical analysis n = 8 (no. of non-zero wts) length = 10 all computations are based on observations with non-zero weights frequency distribution (1-6) 8 0 0 0 0 0 0 0 0 0 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 0.0000000E+00 wtd standard deviation = 0.0000000E+00 weighted mean = 0.0000000E+00 weighted s.d. of mean = 0.0000000E+00 median = 0.0000000E+00 range = 0.0000000E+00 mid-range = 0.0000000E+00 mean deviation = 0.0000000E+00 25 pct unwtd trimmed mean= 0.0000000E+00 variance = 0.0000000E+00 25 pct wtd trimmed mean = 0.0000000E+00 coefficient of variation = undefined (mean is zero) a two-sided 95 pct confidence interval for mean is 0.0000000E+00 to 0.0000000E+00 (2-2) a two-sided 95 pct confidence interval for s.d. is 0.0000000E+00 to 0.0000000E+00 (2-7) linear trend statistics (5-1) other statistics slope = 0.0000000E+00 minimum = 0.0000000E+00 s.d. of slope = 0.0000000E+00 maximum = 0.0000000E+00 slope/s.d. of slope = t = 0.0000000E+00 beta one = 0.0000000E+00 prob exceeding abs value of obs t = 1.000 beta two = 0.0000000E+00 wtd sum of values = 0.0000000E+00 wtd sum of squares = 0.0000000E+00 tests for non-randomness wtd sum of dev squared = 0.0000000E+00 students t = 0.0000000E+00 hno. of runs up and down = 1 wtd sum absolute values = 0.0000000E+00 expected no. of runs = 5.0 wtd ave absolute values = 0.0000000E+00 s.d. of no. of runs = 1.05 mean sq successive diff = 0.0000000E+00 mean sq succ diff/var = 0.000 deviations from wtd mean no. of + signs = 8 no. of - signs = 0 no. of runs = 1 expected no. of runs= 1.0 s.d. of runs = -0.00 diff./s.d. of runs = 0.000 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 derivative step size selection subroutine test number 1 simple example test of stpls starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default 0.21544345E-01 0 2 3.1250000 default 0.17051697E-02 7 + 48 49 50 51 52 53 54 3 1.0000000 default 0.21544337E-01 0 4 2.0000000 default 0.78148842E-02 4 + 1 2 100 101 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 10 number of observations (n) 101 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 0 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1705170E-02 0.2154434E-01 0.7814884E-02 derivative step size selection subroutine test number 2 simple example input - neta = 0, exmpt = .00000000 , scale(1) = 1.0000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 1.0000000 0.21544345E-01 0 2 3.1250000 1.0000000 0.17089843E-03 0 3 1.0000000 1.0000000 0.21544337E-01 0 4 2.0000000 1.0000000 0.78153610E-03 2 + + 50 52 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 0, exmpt = .00000000 , scale(1) = 1.0000000 , nprt = 1 ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-03 0.2154434E-01 0.7815361E-03 1derivative step size selection subroutine test number 1 check error handling - test 1 test of stpls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stpls ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to one . the input value of m is -5. the value of the argument m must be greater than or equal to one . the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the input value of npar is -10. the value of the argument npar must be greater than or equal to one . the correct form of the call statement is call stpls (xm, n, m, ixm, nlsmdl, par, npar, ldstak, stp) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 test of stplsc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stplsc ------------------------------------- the input value of n is -5. the value of the argument n must be greater than or equal to one . the input value of m is -5. the value of the argument m must be greater than or equal to one . the input value of ixm is -10. the first dimension of xm , as indicated by the argument ixm , must be greater than or equal to n . the input value of npar is -10. the value of the argument npar must be greater than or equal to one . the correct form of the call statement is call stplsc (xm, n, m, ixm, nlsmdl, par, npar, ldstak, stp, + neta, exmpt, scale, nprt) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 1derivative step size selection subroutine test number 2 check error handling - test 2 test of stpls starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stpls ------------------------------------- the input value of ldstak is 635. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 636. the correct form of the call statement is call stpls (xm, n, m, ixm, nlsmdl, par, npar, ldstak, stp) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 test of stplsc starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine stplsc ------------------------------------- the input value of ldstak is 635. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 636. the number of values in vector scale less than or equal to zero is 1. since the first value of the vector scale is greater than zero all of the values must be greater than zero . the correct form of the call statement is call stplsc (xm, n, m, ixm, nlsmdl, par, npar, ldstak, stp, + neta, exmpt, scale, nprt) ***** returned results ***** (-1 indicates value not changed by called subroutine) ierr is 1 1derivative step size selection subroutine test number 1 simple example input - neta = 0, exmpt = -1.0000000 , scale(1) = 1.0000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 1.0000000 0.21544345E-01 0 2 3.1250000 1.0000000 0.17051697E-02 7 + 48 49 50 51 52 53 54 3 1.0000000 1.0000000 0.21544337E-01 0 4 2.0000000 1.0000000 0.78148842E-02 4 + 1 2 100 101 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 10 number of observations (n) 101 output - neta = 0, exmpt = -1.0000000 , scale(1) = 1.0000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 0 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1705170E-02 0.2154434E-01 0.7814884E-02 1derivative step size selection subroutine test number 2 simple example input - neta = 0, exmpt = 0.99999997E-04, scale(1) = 1.0000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 1.0000000 0.21544345E-01 0 2 3.1250000 1.0000000 0.17089843E-03 0 3 1.0000000 1.0000000 0.21544337E-01 0 4 2.0000000 1.0000000 0.78153610E-03 2 + + 50 52 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0001 number of observations exempted from selection criteria 1 number of observations (n) 101 output - neta = 0, exmpt = 0.99999997E-04, scale(1) = 1.0000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-03 0.2154434E-01 0.7815361E-03 1derivative step size selection subroutine test number 3 simple example input - neta = 0, exmpt = .50000000 , scale(1) = 1.0000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 1.0000000 0.21544345E-01 0 2 3.1250000 1.0000000 0.17053986E-02 20 + 1 2 3 4 5 6 7 8 9 10 11 45 46 47 48 49 50 51 52 53 3 1.0000000 1.0000000 0.21544337E-01 0 4 2.0000000 1.0000000 0.78148842E-02 4 + 1 2 100 101 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.5000 number of observations exempted from selection criteria 50 number of observations (n) 101 output - neta = 0, exmpt = .50000000 , scale(1) = 1.0000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 0 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1705399E-02 0.2154434E-01 0.7814884E-02 1derivative step size selection subroutine test number 4 simple example input - neta = 0, exmpt = 1.0000000 , scale(1) = 1.0000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 1.0000000 0.21544345E-01 0 2 3.1250000 1.0000000 0.17053986E-02 20 + 1 2 3 4 5 6 7 8 9 10 11 45 46 47 48 49 50 51 52 53 3 1.0000000 1.0000000 0.21544337E-01 0 4 2.0000000 1.0000000 0.78148842E-02 4 + 1 2 100 101 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 1.0000 number of observations exempted from selection criteria 101 number of observations (n) 101 output - neta = 0, exmpt = 1.0000000 , scale(1) = 1.0000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 0 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1705399E-02 0.2154434E-01 0.7814884E-02 1derivative step size selection subroutine test number 5 simple example input - neta = 0, exmpt = 1.1000000 , scale(1) = 1.0000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 1.0000000 0.21544345E-01 0 2 3.1250000 1.0000000 0.17051697E-02 7 + 48 49 50 51 52 53 54 3 1.0000000 1.0000000 0.21544337E-01 0 4 2.0000000 1.0000000 0.78148842E-02 4 + 1 2 100 101 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 10 number of observations (n) 101 output - neta = 0, exmpt = 1.1000000 , scale(1) = 1.0000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 0 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1705170E-02 0.2154434E-01 0.7814884E-02 1derivative step size selection subroutine test number 6 simple example input - neta = -1, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default 0.21544345E-01 0 2 3.1250000 default 0.17089843E-03 0 3 1.0000000 default 0.21544337E-01 0 4 2.0000000 default 0.78153610E-03 2 + + 50 52 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = -1, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-03 0.2154434E-01 0.7815361E-03 1derivative step size selection subroutine test number 7 simple example input - neta = 0, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default 0.21544345E-01 0 2 3.1250000 default 0.17089843E-03 0 3 1.0000000 default 0.21544337E-01 0 4 2.0000000 default 0.78153610E-03 2 + + 50 52 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 0, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-03 0.2154434E-01 0.7815361E-03 1derivative step size selection subroutine test number 8 simple example input - neta = 1, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default 0.21544345E-01 0 2 3.1250000 default 0.17089843E-03 0 3 1.0000000 default 0.21544337E-01 0 4 2.0000000 default 0.78153610E-03 2 + + 50 52 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 1, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-03 0.2154434E-01 0.7815361E-03 1derivative step size selection subroutine test number 9 simple example input - neta = 2, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default .21544346 0 2 3.1250000 default 0.68053436E-02 97 + + 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 3 1.0000000 default .21544349 0 4 2.0000000 default 0.24559855E-01 96 + + 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 2 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 2, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value .2154435 0.6805344E-02 .2154435 0.2455986E-01 1derivative step size selection subroutine test number 10 simple example input - neta = 6, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default 0.99999998E-02 0 2 3.1250000 default 0.54473876E-04 3 + + 50 51 52 3 1.0000000 default 0.99999905E-02 0 4 2.0000000 default 0.24536848E-02 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 6, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.1000000E-01 0.5447388E-04 0.9999990E-02 0.2453685E-02 1derivative step size selection subroutine test number 11 simple example input - neta = 7, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default 0.21544345E-01 0 2 3.1250000 default 0.17089843E-03 0 3 1.0000000 default 0.21544337E-01 0 4 2.0000000 default 0.78153610E-03 2 + + 50 52 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 7, exmpt = .00000000 , scale(1) = .00000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-03 0.2154434E-01 0.7815361E-03 1derivative step size selection subroutine test number 12 simple example input - neta = 0, exmpt = .00000000 , scale(1) = 1.0000000 , nprt = 0 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 1.0000000 0.21544345E-01 0 2 3.1250000 1.0000000 0.17089843E-03 0 3 1.0000000 1.0000000 0.21544337E-01 0 4 2.0000000 1.0000000 0.78153610E-03 2 + + 50 52 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. ** row numbers are only listed when number of observations failing step size selection criteria exceeds number of exemptions allowed. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 0, exmpt = .00000000 , scale(1) = 1.0000000 , nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-03 0.2154434E-01 0.7815361E-03 1derivative step size selection subroutine test number 13 large calculation error problem input - neta = 0, exmpt = .00000000 , scale(1) = -1.0000000 , nprt = 1 test of stplsc starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .00000000 default 0.21544345E-01 0 2 3.1250000 default 0.17089844E-04 1 + + 52 3 100.00000 default 0.21544343E-01 0 4 2.0000000 default 0.78034401E-03 2 + + 1 101 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 5 proportion of observations exempted from selection criteria (exmpt) 0.0000 number of observations exempted from selection criteria 0 number of observations (n) 101 output - neta = 0, exmpt = .00000000 , scale(1) = -1.0000000 , nprt = 1 returned results (-1 indicates value not changed by called subroutine) ierr is 2 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1708984E-04 0.2154434E-01 0.7803440E-03 1derivative step size selection subroutine test number 14 large calculation error problem input - neta = 0, exmpt = .11000000 , scale(1) = -1.0000000 , nprt = 0 test of stplsc output - neta = 0, exmpt = .11000000 , scale(1) = -1.0000000 , nprt = 0 returned results (-1 indicates value not changed by called subroutine) ierr is 0 returned values of stp index 1 2 3 4 value 0.2154434E-01 0.1705170E-02 0.2154434E-01 0.7803321E-02 check error handling - test 1 test of uas starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uas ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call uas (y, n) Return value of IERR is 1 Test 1 of UASS: starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uass ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of iar is -2. the value of the argument iar must be between -lagmax and +lagmax , inclusive. the input value of lag is -2. the value of the argument lag must be between -lagmax and +lagmax , inclusive. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. UASS - Fatal error! Nonzero error return from UASER. Return value of IERR is 1 test of uasf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasf ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call uasf (yfft, n, lyfft, ldstak) Return value of IERR is 1 test of uasfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasfs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of iar is -2. the value of the argument iar must be between -lagmax and +lagmax , inclusive. the input value of lag is -2. the value of the argument lag must be between -lagmax and +lagmax , inclusive. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the correct form of the call statement is call uasfs (yfft, n, lyfft, ldstak, + iar, phi, lagmax, lag, nf, fmin, fmax, nprt + spca, spcf, freq) Return value of IERR is 1 test of uasv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasv ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call uasv (acov, lagmax, n) Return value of IERR is 1 test of uasvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasvs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of iar is -2. the value of the argument iar must be between -lagmax and +lagmax , inclusive. the input value of lag is -2. the value of the argument lag must be between -lagmax and +lagmax , inclusive. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the correct form of the call statement is call uasvs (acov, lagmax, y, n, + iar, phi, lag, nf, fmin, fmax, nprt, + spca, spcf, freq, ldstak) Return value of IERR is 1 check error handling - test 2 Test 2 of UASS: starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uass ------------------------------------- the input value of iar is 101. the value of the argument iar must be between -lagmax and +lagmax , inclusive. the input value of lagmax is 50. the value of the argument lagmax must be between 1 and n-1 , inclusive. the input value of lag is 101. the value of the argument lag must be between -lagmax and +lagmax , inclusive. UASS - Fatal error! Nonzero error return from UASER. Return value of IERR is 1 test of uasf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasf ------------------------------------- the input value of lyfft is -11. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 86. the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 50. the correct form of the call statement is call uasf (yfft, n, lyfft, ldstak) Return value of IERR is 1 test of uasfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasfs ------------------------------------- the input value of lyfft is -11. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 102. the input value of iar is 101. the value of the argument iar must be between -lagmax and +lagmax , inclusive. the input value of lagmax is 50. the value of the argument lagmax must be between 1 and n-1 , inclusive. the input value of lag is 101. the value of the argument lag must be between -lagmax and +lagmax , inclusive. the correct form of the call statement is call uasfs (yfft, n, lyfft, ldstak, + iar, phi, lagmax, lag, nf, fmin, fmax, nprt + spca, spcf, freq) Return value of IERR is 1 test of uasv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasv ------------------------------------- the input value of lagmax is 50. the value of the argument lagmax must be between 1 and n-1 , inclusive. the correct form of the call statement is call uasv (acov, lagmax, n) Return value of IERR is 1 test of uasvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasvs ------------------------------------- the input value of lagmax is 50. the value of the argument lagmax must be between 1 and n-1 , inclusive. the input value of iar is 101. the value of the argument iar must be between -lagmax and +lagmax , inclusive. the input value of lag is 101. the value of the argument lag must be between -lagmax and +lagmax , inclusive. the correct form of the call statement is call uasvs (acov, lagmax, y, n, + iar, phi, lag, nf, fmin, fmax, nprt, + spca, spcf, freq, ldstak) Return value of IERR is 1 lds too small Test 3 of UASS: starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uass ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 226. UASS - Fatal error! Nonzero error return from UASER. Return value of IERR is 1 test of uasf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasf ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 50. the correct form of the call statement is call uasf (yfft, n, lyfft, ldstak) Return value of IERR is 1 test of uasfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasfs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 252. the correct form of the call statement is call uasfs (yfft, n, lyfft, ldstak, + iar, phi, lagmax, lag, nf, fmin, fmax, nprt + spca, spcf, freq) Return value of IERR is 1 test of uasvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine uasvs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 200. the correct form of the call statement is call uasvs (acov, lagmax, y, n, + iar, phi, lag, nf, fmin, fmax, nprt, + spca, spcf, freq, ldstak) Return value of IERR is 1 VALID PROBLEM: test of uas starpac 2.08s (03/15/90) Autoregressive Order Selection Statistics: lag 1 2 3 4 5 6 7 8 9 10 11 12 aic 5.08 0.00 1.91 3.54 3.90 5.93 7.30 7.35 9.30 11.37 13.46 15.40 f ratio 23.53 7.15 0.09 0.35 1.48 0.01 0.57 1.68 0.10 0.01 0.02 0.14 f probability 0.00 0.01 0.76 0.55 0.23 0.93 0.45 0.20 0.75 0.91 0.89 0.71 lag 13 14 15 16 17 18 19 20 21 22 23 24 aic 16.80 18.98 20.84 22.94 25.12 27.38 25.94 27.94 29.59 31.88 34.44 36.43 f ratio 0.55 0.00 0.24 0.10 0.06 0.04 2.37 0.24 0.45 0.12 0.00 0.32 f probability 0.46 0.96 0.63 0.75 0.81 0.84 0.13 0.63 0.51 0.74 0.94 0.58 lag 25 26 27 28 29 30 31 32 aic 38.98 41.68 44.55 46.93 49.91 53.00 55.64 59.10 f ratio 0.08 0.04 0.00 0.25 0.04 0.04 0.25 0.00 f probability 0.79 0.85 0.99 0.62 0.85 0.85 0.63 1.00 order autoregressive process selected = 2 one step prediction variance of process selected = .883569 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 coefficient value 0.7819-0.3634 starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0981 - .... c - i .... ...++ i i ... ++..+++ i i +++++ .. ++ . + i i +++ .... ++ .. + i -1.3503 - ...2++ + . + - i ..... ++ ++ . + i i +++++++ . + i i . i i .+ i -2.7988 - .+ - i i i .+ i i . i i 2 i -4.2472 - . b * w - i +. i i +. i i . i i + . i -5.6957 - . - i + . i i + . i i + . i i + . i -7.1441 - + . i - i + . i i ++ . i i + . i i ++.. i -8.5926 - + . - i + . i i + .. i i + . i i + .. i -10.0410 - + . - i + .. i i ++ .. +++ i i + .. ++++ +++ i i +++ 22+ + i -11.4895 - +++ .. ++ - i ... + i i ... + i i .... + i i .....2 i -12.9380 - 2....... - i + i i + i i + i i ++ i -14.3864 - ++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. Return value of IERR is 0 VALID PROBLEM: Test 4 of UASS: starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.7034 - . . c - 6.0000 - . . - i . . i i . i i . . i i . i i . i 4.0000 - . . - i . . + i i . . + + + i i + + + + + 2 i i + + i i + + + i i + + + + . + b * w i i i 2.0000 - . - i + i i i i . i i + i i i i . i i i + i i . i 1.0000 - - i 2 i i i 0.8000 - 2 - i + i i . i i + i 0.6000 - . + - i + i i . i i + i i . + i 0.4000 - . - i + i i . + i i . + + + + i i . + + + + + i i . + + i i . + i i . + i i . i 0.2000 - . + - i . . i i . + i i . . + i i . . . + i 0.1350 - . . . . 2 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. Return value of IERR is 0 0.00000000E+00 0.33742750E+01 0.31765008E+01 0.99999998E-02 0.34010139E+01 0.31546259E+01 0.20000000E-01 0.34823813E+01 0.30898814E+01 0.29999999E-01 0.36217921E+01 0.29855804E+01 0.39999999E-01 0.38247168E+01 0.28492470E+01 0.49999997E-01 0.40980287E+01 0.26947193E+01 0.59999995E-01 0.44482832E+01 0.25434000E+01 0.69999993E-01 0.48777318E+01 0.24232385E+01 0.79999991E-01 0.53762422E+01 0.23644519E+01 0.89999989E-01 0.59075289E+01 0.23920674E+01 0.99999987E-01 0.63923178E+01 0.25169067E+01 0.10999998E+00 0.67034168E+01 0.27280111E+01 0.11999998E+00 0.67011247E+01 0.29899344E+01 0.12999998E+00 0.63156419E+01 0.32472711E+01 0.13999999E+00 0.56108236E+01 0.34364262E+01 0.14999999E+00 0.47490959E+01 0.35017123E+01 0.16000000E+00 0.38920703E+01 0.34107442E+01 0.17000000E+00 0.31369665E+01 0.31637948E+01 0.18000001E+00 0.25163260E+01 0.27936499E+01 0.19000001E+00 0.20246077E+01 0.23558958E+01 0.20000002E+00 0.16414006E+01 0.19130828E+01 0.21000002E+00 0.13440670E+01 0.15183274E+01 0.22000003E+00 0.11127702E+01 0.12037824E+01 0.23000003E+00 0.93169010E+00 0.97712886E+00 0.24000004E+00 0.78873998E+00 0.82595944E+00 0.25000003E+00 0.67485464E+00 0.72707671E+00 0.26000002E+00 0.58327699E+00 0.65647447E+00 0.27000001E+00 0.50896591E+00 0.59642541E+00 0.28000000E+00 0.44814247E+00 0.53805006E+00 0.28999999E+00 0.39795426E+00 0.47992694E+00 0.29999998E+00 0.35623175E+00 0.42470348E+00 0.30999997E+00 0.32131001E+00 0.37583613E+00 0.31999996E+00 0.29190108E+00 0.33575833E+00 0.32999995E+00 0.26700008E+00 0.30558991E+00 0.33999994E+00 0.24581660E+00 0.28565729E+00 0.34999993E+00 0.22772458E+00 0.27592516E+00 0.35999992E+00 0.21222451E+00 0.27586174E+00 0.36999992E+00 0.19891508E+00 0.28386891E+00 0.37999991E+00 0.18747248E+00 0.29681349E+00 0.38999990E+00 0.17763387E+00 0.31017292E+00 0.39999989E+00 0.16918525E+00 0.31893790E+00 0.40999988E+00 0.16195190E+00 0.31896615E+00 0.41999987E+00 0.15579107E+00 0.30820858E+00 0.42999986E+00 0.15058613E+00 0.28727424E+00 0.43999985E+00 0.14624228E+00 0.25911653E+00 0.44999984E+00 0.14268309E+00 0.22800970E+00 0.45999983E+00 0.13984768E+00 0.19827282E+00 0.46999982E+00 0.13768870E+00 0.17324841E+00 0.47999981E+00 0.13617091E+00 0.15486634E+00 0.48999980E+00 0.13526981E+00 0.14382780E+00 0.50000000E+00 0.13497099E+00 0.14016998E+00 iar = 2 lag = 16 phi = 0.10000000E+01 -0.50000000E+00 VALID PROBLEM: test of uasf starpac 2.08s (03/15/90) Autoregressive Order Selection Statistics: lag 1 2 3 4 5 6 7 8 9 10 11 12 aic 5.08 0.00 1.91 3.54 3.90 5.93 7.30 7.35 9.30 11.37 13.46 15.40 f ratio 23.53 7.15 0.09 0.35 1.48 0.01 0.57 1.68 0.10 0.01 0.02 0.14 f probability 0.00 0.01 0.76 0.55 0.23 0.93 0.45 0.20 0.75 0.91 0.89 0.71 lag 13 14 15 16 17 18 19 20 21 22 23 24 aic 16.80 18.98 20.84 22.94 25.12 27.38 25.94 27.94 29.59 31.88 34.44 36.43 f ratio 0.55 0.00 0.24 0.10 0.06 0.04 2.37 0.24 0.45 0.12 0.00 0.32 f probability 0.46 0.96 0.63 0.75 0.81 0.84 0.13 0.63 0.51 0.74 0.94 0.58 lag 25 26 27 28 29 30 31 32 aic 38.98 41.68 44.55 46.93 49.91 53.00 55.64 59.10 f ratio 0.08 0.04 0.00 0.25 0.04 0.04 0.25 0.00 f probability 0.79 0.85 0.99 0.62 0.85 0.85 0.63 1.00 order autoregressive process selected = 2 one step prediction variance of process selected = .883570 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 coefficient value 0.7819-0.3634 starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0981 - .... c - i .... ...++ i i ... ++..+++ i i +++++ .. ++ . + i i +++ .... ++ .. + i -1.3503 - ...2++ + . + - i ..... ++ ++ . + i i +++++++ . + i i . i i .+ i -2.7988 - .+ - i i i .+ i i . i i 2 i -4.2472 - . b * w - i +. i i +. i i . i i + . i -5.6957 - . - i + . i i + . i i + . i i + . i -7.1441 - + . i - i + . i i ++ . i i + . i i ++.. i -8.5926 - + . - i + . i i + .. i i + . i i + .. i -10.0410 - + . - i + .. i i ++ .. +++ i i + .. ++++ +++ i i +++ 22+ + i -11.4895 - +++ .. ++ - i ... + i i ... + i i .... + i i .....2 i -12.9379 - 2....... - i + i i + i i + i i ++ i -14.3864 - ++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. Return value of IERR is 0 VALID PROBLEM: test of uasfs starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.7034 - . . c - 6.0000 - . . - i . . i i . i i . . i i . i i . i 4.0000 - . . - i . . + i i . . + + + i i + + + + + 2 i i + + i i + + + i i + + + + . + b * w i i i 2.0000 - . - i + i i i i . i i + i i i i . i i i + i i . i 1.0000 - - i 2 i i i 0.8000 - 2 - i + i i . i i + i 0.6000 - . + - i + i i . i i + i i . + i 0.4000 - . - i + i i . + i i . + + + + i i . + + + + + i i . + + i i . + i i . + i i . i 0.2000 - . + - i . . i i . + i i . . + i i . . . + i 0.1350 - . . . . 2 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. Return value of IERR is 0 0.00000000E+00 0.33742750E+01 0.31765015E+01 0.99999998E-02 0.34010139E+01 0.31546264E+01 0.20000000E-01 0.34823813E+01 0.30898819E+01 0.29999999E-01 0.36217921E+01 0.29855816E+01 0.39999999E-01 0.38247168E+01 0.28492475E+01 0.49999997E-01 0.40980287E+01 0.26947198E+01 0.59999995E-01 0.44482832E+01 0.25434005E+01 0.69999993E-01 0.48777318E+01 0.24232390E+01 0.79999991E-01 0.53762422E+01 0.23644521E+01 0.89999989E-01 0.59075289E+01 0.23920677E+01 0.99999987E-01 0.63923178E+01 0.25169067E+01 0.10999998E+00 0.67034168E+01 0.27280116E+01 0.11999998E+00 0.67011247E+01 0.29899349E+01 0.12999998E+00 0.63156419E+01 0.32472713E+01 0.13999999E+00 0.56108236E+01 0.34364262E+01 0.14999999E+00 0.47490959E+01 0.35017123E+01 0.16000000E+00 0.38920703E+01 0.34107444E+01 0.17000000E+00 0.31369665E+01 0.31637950E+01 0.18000001E+00 0.25163260E+01 0.27936499E+01 0.19000001E+00 0.20246077E+01 0.23558960E+01 0.20000002E+00 0.16414006E+01 0.19130828E+01 0.21000002E+00 0.13440670E+01 0.15183275E+01 0.22000003E+00 0.11127702E+01 0.12037827E+01 0.23000003E+00 0.93169010E+00 0.97712898E+00 0.24000004E+00 0.78873998E+00 0.82595968E+00 0.25000003E+00 0.67485464E+00 0.72707683E+00 0.26000002E+00 0.58327699E+00 0.65647459E+00 0.27000001E+00 0.50896591E+00 0.59642547E+00 0.28000000E+00 0.44814247E+00 0.53805012E+00 0.28999999E+00 0.39795426E+00 0.47992706E+00 0.29999998E+00 0.35623175E+00 0.42470348E+00 0.30999997E+00 0.32131001E+00 0.37583625E+00 0.31999996E+00 0.29190108E+00 0.33575857E+00 0.32999995E+00 0.26700008E+00 0.30559027E+00 0.33999994E+00 0.24581660E+00 0.28565753E+00 0.34999993E+00 0.22772458E+00 0.27592540E+00 0.35999992E+00 0.21222451E+00 0.27586186E+00 0.36999992E+00 0.19891508E+00 0.28386903E+00 0.37999991E+00 0.18747248E+00 0.29681361E+00 0.38999990E+00 0.17763387E+00 0.31017292E+00 0.39999989E+00 0.16918525E+00 0.31893778E+00 0.40999988E+00 0.16195190E+00 0.31896603E+00 0.41999987E+00 0.15579107E+00 0.30820835E+00 0.42999986E+00 0.15058613E+00 0.28727400E+00 0.43999985E+00 0.14624228E+00 0.25911617E+00 0.44999984E+00 0.14268309E+00 0.22800934E+00 0.45999983E+00 0.13984768E+00 0.19827223E+00 0.46999982E+00 0.13768870E+00 0.17324781E+00 0.47999981E+00 0.13617091E+00 0.15486586E+00 0.48999980E+00 0.13526981E+00 0.14382720E+00 0.50000000E+00 0.13497099E+00 0.14016938E+00 iar = 2 lag = 16 phi = 0.10000000E+01 -0.50000000E+00 VALID PROBLEM: test of uasv starpac 2.08s (03/15/90) Autoregressive Order Selection Statistics: lag 1 2 3 4 5 6 7 8 9 10 11 12 aic 5.08 0.00 1.91 3.54 3.90 5.93 7.30 7.35 9.30 11.37 13.46 15.40 f ratio 23.53 7.15 0.09 0.35 1.48 0.01 0.57 1.68 0.10 0.01 0.02 0.14 f probability 0.00 0.01 0.76 0.55 0.23 0.93 0.45 0.20 0.75 0.91 0.89 0.71 lag 13 14 15 16 17 18 19 20 21 22 23 24 aic 16.80 18.98 20.84 22.94 25.12 27.38 25.94 27.94 29.59 31.88 34.44 36.43 f ratio 0.55 0.00 0.24 0.10 0.06 0.04 2.37 0.24 0.45 0.12 0.00 0.32 f probability 0.46 0.96 0.63 0.75 0.81 0.84 0.13 0.63 0.51 0.74 0.94 0.58 lag 25 26 27 28 29 30 31 32 33 34 35 36 aic 38.98 41.68 44.55 46.93 49.91 53.00 55.64 59.10 62.23 66.05 69.92 73.97 f ratio 0.08 0.04 0.00 0.25 0.04 0.04 0.25 0.00 0.16 0.00 0.05 0.06 f probability 0.79 0.85 0.99 0.62 0.85 0.85 0.63 1.00 0.69 1.00 0.83 0.81 lag 37 38 39 40 41 42 43 44 45 46 47 48 aic 78.39 83.30 88.62 94.39 100.82 108.04 116.14 125.56 137.00 151.89 171.75 206.71 f ratio 0.04 0.00 0.00 0.01 0.00 0.00 0.02 0.02 0.02 0.00 0.04 0.00 f probability 0.85 0.96 0.98 0.93 1.00 1.00 0.90 0.89 0.90 0.99 0.86 0.96 lag 49 aic f ratio f probability This value cannot be computed because LAG = N-1. order autoregressive process selected = 2 one step prediction variance of process selected = .883569 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 coefficient value 0.7819-0.3634 starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0981 - .... c - i .... ...++ i i ... ++..+++ i i +++++ .. ++ . + i i +++ .... ++ .. + i -1.3503 - ...2++ + . + - i ..... ++ ++ . + i i +++++++ . + i i . i i .+ i -2.7988 - .+ - i i i .+ i i . i i 2 i -4.2472 - . b * w - i +. i i +. i i . i i + . i -5.6957 - . - i + . i i + . i i + . i i + . i -7.1441 - + . i - i + . i i ++ . i i + . i i ++.. i -8.5926 - + . - i + . i i + .. i i + . i i + .. i -10.0410 - + . - i + .. i i ++ .. +++ i i + .. ++++ +++ i i +++ 22+ + i -11.4895 - +++ .. ++ - i ... + i i ... + i i .... + i i .....2 i -12.9380 - 2....... - i + i i + i i + i i ++ i -14.3864 - ++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. Return value of IERR is 0 VALID PROBLEM: Test 5 of UASS: starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.7034 - . . c - 6.0000 - . . - i . . i i . i i . . i i . i i . i 4.0000 - . . - i . . + i i . . + + + i i + + + + + 2 i i + + i i + + + i i + + + + . + b * w i i i 2.0000 - . - i + i i i i . i i + i i i i . i i i + i i . i 1.0000 - - i 2 i i i 0.8000 - 2 - i + i i . i i + i 0.6000 - . + - i + i i . i i + i i . + i 0.4000 - . - i + i i . + i i . + + + + i i . + + + + + i i . + + i i . + i i . + i i . i 0.2000 - . + - i . . i i . + i i . . + i i . . . + i 0.1350 - . . . . 2 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. Return value of IERR is 0 0.00000000E+00 0.33742750E+01 0.31765008E+01 0.99999998E-02 0.34010139E+01 0.31546259E+01 0.20000000E-01 0.34823813E+01 0.30898814E+01 0.29999999E-01 0.36217921E+01 0.29855804E+01 0.39999999E-01 0.38247168E+01 0.28492470E+01 0.49999997E-01 0.40980287E+01 0.26947193E+01 0.59999995E-01 0.44482832E+01 0.25434000E+01 0.69999993E-01 0.48777318E+01 0.24232385E+01 0.79999991E-01 0.53762422E+01 0.23644519E+01 0.89999989E-01 0.59075289E+01 0.23920674E+01 0.99999987E-01 0.63923178E+01 0.25169067E+01 0.10999998E+00 0.67034168E+01 0.27280111E+01 0.11999998E+00 0.67011247E+01 0.29899344E+01 0.12999998E+00 0.63156419E+01 0.32472711E+01 0.13999999E+00 0.56108236E+01 0.34364262E+01 0.14999999E+00 0.47490959E+01 0.35017123E+01 0.16000000E+00 0.38920703E+01 0.34107442E+01 0.17000000E+00 0.31369665E+01 0.31637948E+01 0.18000001E+00 0.25163260E+01 0.27936499E+01 0.19000001E+00 0.20246077E+01 0.23558958E+01 0.20000002E+00 0.16414006E+01 0.19130828E+01 0.21000002E+00 0.13440670E+01 0.15183274E+01 0.22000003E+00 0.11127702E+01 0.12037824E+01 0.23000003E+00 0.93169010E+00 0.97712886E+00 0.24000004E+00 0.78873998E+00 0.82595944E+00 0.25000003E+00 0.67485464E+00 0.72707671E+00 0.26000002E+00 0.58327699E+00 0.65647447E+00 0.27000001E+00 0.50896591E+00 0.59642541E+00 0.28000000E+00 0.44814247E+00 0.53805006E+00 0.28999999E+00 0.39795426E+00 0.47992694E+00 0.29999998E+00 0.35623175E+00 0.42470348E+00 0.30999997E+00 0.32131001E+00 0.37583613E+00 0.31999996E+00 0.29190108E+00 0.33575833E+00 0.32999995E+00 0.26700008E+00 0.30558991E+00 0.33999994E+00 0.24581660E+00 0.28565729E+00 0.34999993E+00 0.22772458E+00 0.27592516E+00 0.35999992E+00 0.21222451E+00 0.27586174E+00 0.36999992E+00 0.19891508E+00 0.28386891E+00 0.37999991E+00 0.18747248E+00 0.29681349E+00 0.38999990E+00 0.17763387E+00 0.31017292E+00 0.39999989E+00 0.16918525E+00 0.31893790E+00 0.40999988E+00 0.16195190E+00 0.31896615E+00 0.41999987E+00 0.15579107E+00 0.30820858E+00 0.42999986E+00 0.15058613E+00 0.28727424E+00 0.43999985E+00 0.14624228E+00 0.25911653E+00 0.44999984E+00 0.14268309E+00 0.22800970E+00 0.45999983E+00 0.13984768E+00 0.19827282E+00 0.46999982E+00 0.13768870E+00 0.17324841E+00 0.47999981E+00 0.13617091E+00 0.15486634E+00 0.48999980E+00 0.13526981E+00 0.14382780E+00 0.50000000E+00 0.13497099E+00 0.14016998E+00 iar = 2 lag = 16 phi = 0.10000000E+01 -0.50000000E+00 minimum problem size test of uas starpac 2.08s (03/15/90) Autoregressive Order Selection Statistics: lag 1 2 3 4 5 6 7 8 9 10 11 12 aic 6.76 0.00 1.63 2.25 4.48 6.52 9.00 10.86 13.66 16.86 20.49 23.19 f ratio 5.43 9.50 0.36 1.13 0.00 0.18 0.00 0.39 0.05 0.02 0.02 0.41 f probability 0.03 0.01 0.56 0.31 0.95 0.68 0.96 0.55 0.84 0.91 0.90 0.56 lag 13 14 15 16 aic 28.52 35.65 47.85 f ratio 0.02 0.04 0.01 f probability 0.89 0.87 0.95 This value cannot be computed because LAG = N-1. order autoregressive process selected = 2 one step prediction variance of process selected = .797400 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 coefficient value 0.8436-0.6359 starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 8 BW =0.2514 IDF = 9 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ... c - i .. . i i . i i . i i . . i -1.8364 - . . - i . i i . i i . i i . . i -3.6728 - . ++++++++++ - i 2++ +++ . i i ++ ++ . i i ++ . ++ i i ++ . +2 b * w i -5.5092 - ++ .. ++ - i + . . + i i + . . + i i ++ .. ++ i i + . . + i -7.3456 - ++ ... . + - i ++... + i i 2222.. . + i i . + i i i + i -9.1820 - . + - i . + i i . + i i . ++ i i . + i -11.0184 - . + - i . + i i . + i i . + i i . ++ i -12.8548 - . + - i .. ++ i i . + i i .. ++ i i . ++ i -14.6913 - .. ++ - i . + i i .. ++ i i ... ++ i i .. ++ i -16.5277 - ... ++ - i ... ++ i i ... ++ i i .....2+ i i .2222..... i -18.3641 - +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. Return value of IERR is 0 minimum problem size Test 6 of UASS: starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 1 BW =1.0000 IDF = 34 and order 1 autoregressive spectrum (.) The plot has been supressed because fewer than four valid (positive) spectral estimates could be computed. Return value of IERR is 0 0.00000000E+00 0.53259912E+01 0.15017184E+01 iar = -1 lag = 1 phi = 0.51566237E+00 check handling of fmin and fmax, lag and iar equal to zero Test 7 of UASS: starpac 2.08s (03/15/90) Autoregressive Order Selection Statistics: lag 1 2 3 4 5 6 7 8 9 10 11 12 aic 5.08 0.00 1.91 3.54 3.90 5.93 7.30 7.35 9.30 11.37 13.46 15.40 f ratio 23.53 7.15 0.09 0.35 1.48 0.01 0.57 1.68 0.10 0.01 0.02 0.14 f probability 0.00 0.01 0.76 0.55 0.23 0.93 0.45 0.20 0.75 0.91 0.89 0.71 lag 13 14 15 16 17 18 19 20 21 22 23 24 aic 16.80 18.98 20.84 22.94 25.12 27.38 25.94 27.94 29.59 31.88 34.44 36.43 f ratio 0.55 0.00 0.24 0.10 0.06 0.04 2.37 0.24 0.45 0.12 0.00 0.32 f probability 0.46 0.96 0.63 0.75 0.81 0.84 0.13 0.63 0.51 0.74 0.94 0.58 lag 25 26 27 28 29 30 31 32 33 34 35 36 aic 38.98 41.68 44.55 46.93 49.91 53.00 55.64 59.10 62.23 66.05 69.92 73.97 f ratio 0.08 0.04 0.00 0.25 0.04 0.04 0.25 0.00 0.16 0.00 0.05 0.06 f probability 0.79 0.85 0.99 0.62 0.85 0.85 0.63 1.00 0.69 1.00 0.83 0.81 lag 37 38 39 40 41 42 43 44 45 46 47 48 aic 78.39 83.30 88.62 94.39 100.82 108.04 116.14 125.56 137.00 151.89 171.75 206.71 f ratio 0.04 0.00 0.00 0.01 0.00 0.00 0.02 0.02 0.02 0.00 0.04 0.00 f probability 0.85 0.96 0.98 0.93 1.00 1.00 0.90 0.89 0.90 0.99 0.86 0.96 lag 49 aic f ratio f probability This value cannot be computed because LAG = N-1. order autoregressive process selected = 2 one step prediction variance of process selected = .883569 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 coefficient value 0.7819-0.3634 starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.9474 - c - i i i i 0.8000 - - i i i i i i 0.6000 - - i i i i i i i i i i 0.4000 - - i i i b * w i i i i i i i i i i i i + i i +++++ i 0.2000 - 2...322222. - i 2+222....2...2....2...2....2...2 i i 2+++ i i i +2++ i i +2++++ i i 2+++2++++ i i 2+++2 i i i i i i i i i 0.1000 - - i i i i 0.0800 - - i i i i i i 0.0600 - - i i i i i i i i i i 0.0400 - - i i i i 0.0337 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.4137 0.4260 0.4382 0.4505 0.4627 0.4750 0.4873 0.4995 0.5118 0.5240 0.5363 Return value of IERR is 0 0.44999999E+00 0.20268981E+00 0.22800922E+00 0.45099998E+00 0.20225269E+00 0.22490859E+00 0.45199996E+00 0.20182547E+00 0.22182643E+00 0.45299995E+00 0.20140807E+00 0.21876681E+00 0.45399994E+00 0.20100047E+00 0.21573329E+00 0.45499992E+00 0.20060256E+00 0.21272957E+00 0.45599991E+00 0.20021422E+00 0.20975888E+00 0.45699990E+00 0.19983549E+00 0.20682466E+00 0.45799989E+00 0.19946632E+00 0.20393038E+00 0.45899987E+00 0.19910659E+00 0.20107877E+00 0.45999986E+00 0.19875622E+00 0.19827247E+00 0.46099985E+00 0.19841523E+00 0.19551492E+00 0.46199983E+00 0.19808351E+00 0.19280827E+00 0.46299982E+00 0.19776109E+00 0.19015503E+00 0.46399981E+00 0.19744773E+00 0.18755710E+00 0.46499979E+00 0.19714361E+00 0.18501735E+00 0.46599978E+00 0.19684854E+00 0.18253720E+00 0.46699977E+00 0.19656254E+00 0.18011868E+00 0.46799976E+00 0.19628556E+00 0.17776322E+00 0.46899974E+00 0.19601752E+00 0.17547286E+00 0.46999973E+00 0.19575837E+00 0.17324853E+00 0.47099972E+00 0.19550814E+00 0.17109168E+00 0.47199970E+00 0.19526668E+00 0.16900361E+00 0.47299969E+00 0.19503404E+00 0.16698503E+00 0.47399968E+00 0.19481021E+00 0.16503692E+00 0.47499967E+00 0.19459510E+00 0.16316020E+00 0.47599965E+00 0.19438866E+00 0.16135561E+00 0.47699964E+00 0.19419089E+00 0.15962350E+00 0.47799963E+00 0.19400181E+00 0.15796435E+00 0.47899961E+00 0.19382125E+00 0.15637863E+00 0.47999960E+00 0.19364931E+00 0.15486658E+00 0.48099959E+00 0.19348592E+00 0.15342855E+00 0.48199958E+00 0.19333108E+00 0.15206480E+00 0.48299956E+00 0.19318475E+00 0.15077531E+00 0.48399955E+00 0.19304684E+00 0.14956009E+00 0.48499954E+00 0.19291742E+00 0.14841914E+00 0.48599952E+00 0.19279647E+00 0.14735258E+00 0.48699951E+00 0.19268391E+00 0.14636040E+00 0.48799950E+00 0.19257976E+00 0.14544225E+00 0.48899949E+00 0.19248401E+00 0.14459825E+00 0.48999947E+00 0.19239664E+00 0.14382792E+00 0.49099946E+00 0.19231759E+00 0.14313209E+00 0.49199945E+00 0.19224690E+00 0.14250946E+00 0.49299943E+00 0.19218463E+00 0.14196050E+00 0.49399942E+00 0.19213058E+00 0.14148510E+00 0.49499941E+00 0.19208489E+00 0.14108324E+00 0.49599940E+00 0.19204752E+00 0.14075434E+00 0.49699938E+00 0.19201851E+00 0.14049864E+00 0.49799937E+00 0.19199774E+00 0.14031601E+00 0.49899936E+00 0.19198529E+00 0.14020658E+00 0.50000000E+00 0.19198115E+00 0.14016998E+00 iar = 2 lag = 16 phi = 0.78192770E+00 -0.36338660E+00 white noise spectrum Test 8 of UASS: starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) The plot has been supressed because fewer than four valid (positive) spectral estimates could be computed. Return value of IERR is 0 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.99999998E-02 0.00000000E+00 0.00000000E+00 0.20000000E-01 0.00000000E+00 0.00000000E+00 0.29999999E-01 0.00000000E+00 0.00000000E+00 0.39999999E-01 0.00000000E+00 0.00000000E+00 0.49999997E-01 0.00000000E+00 0.00000000E+00 0.59999995E-01 0.00000000E+00 0.00000000E+00 0.69999993E-01 0.00000000E+00 0.00000000E+00 0.79999991E-01 0.00000000E+00 0.00000000E+00 0.89999989E-01 0.00000000E+00 0.00000000E+00 0.99999987E-01 0.00000000E+00 0.00000000E+00 0.10999998E+00 0.00000000E+00 0.00000000E+00 0.11999998E+00 0.00000000E+00 0.00000000E+00 0.12999998E+00 0.00000000E+00 0.00000000E+00 0.13999999E+00 0.00000000E+00 0.00000000E+00 0.14999999E+00 0.00000000E+00 0.00000000E+00 0.16000000E+00 0.00000000E+00 0.00000000E+00 0.17000000E+00 0.00000000E+00 0.00000000E+00 0.18000001E+00 0.00000000E+00 0.00000000E+00 0.19000001E+00 0.00000000E+00 0.00000000E+00 0.20000002E+00 0.00000000E+00 0.00000000E+00 0.21000002E+00 0.00000000E+00 0.00000000E+00 0.22000003E+00 0.00000000E+00 0.00000000E+00 0.23000003E+00 0.00000000E+00 0.00000000E+00 0.24000004E+00 0.00000000E+00 0.00000000E+00 0.25000003E+00 0.00000000E+00 0.00000000E+00 0.26000002E+00 0.00000000E+00 0.00000000E+00 0.27000001E+00 0.00000000E+00 0.00000000E+00 0.28000000E+00 0.00000000E+00 0.00000000E+00 0.28999999E+00 0.00000000E+00 0.00000000E+00 0.29999998E+00 0.00000000E+00 0.00000000E+00 0.30999997E+00 0.00000000E+00 0.00000000E+00 0.31999996E+00 0.00000000E+00 0.00000000E+00 0.32999995E+00 0.00000000E+00 0.00000000E+00 0.33999994E+00 0.00000000E+00 0.00000000E+00 0.34999993E+00 0.00000000E+00 0.00000000E+00 0.35999992E+00 0.00000000E+00 0.00000000E+00 0.36999992E+00 0.00000000E+00 0.00000000E+00 0.37999991E+00 0.00000000E+00 0.00000000E+00 0.38999990E+00 0.00000000E+00 0.00000000E+00 0.39999989E+00 0.00000000E+00 0.00000000E+00 0.40999988E+00 0.00000000E+00 0.00000000E+00 0.41999987E+00 0.00000000E+00 0.00000000E+00 0.42999986E+00 0.00000000E+00 0.00000000E+00 0.43999985E+00 0.00000000E+00 0.00000000E+00 0.44999984E+00 0.00000000E+00 0.00000000E+00 0.45999983E+00 0.00000000E+00 0.00000000E+00 0.46999982E+00 0.00000000E+00 0.00000000E+00 0.47999981E+00 0.00000000E+00 0.00000000E+00 0.48999980E+00 0.00000000E+00 0.00000000E+00 0.50000000E+00 0.00000000E+00 0.00000000E+00 iar = 2 lag = 16 phi = 0.10000000E+01 -0.50000000E+00 suppress output, lag and iar less than zero Test 9 of UASS: Return value of IERR is 0 0.44999999E+00 0.20268981E+00 0.22800922E+00 0.45099998E+00 0.20225269E+00 0.22490859E+00 0.45199996E+00 0.20182547E+00 0.22182643E+00 0.45299995E+00 0.20140807E+00 0.21876681E+00 0.45399994E+00 0.20100047E+00 0.21573329E+00 0.45499992E+00 0.20060256E+00 0.21272957E+00 0.45599991E+00 0.20021422E+00 0.20975888E+00 0.45699990E+00 0.19983549E+00 0.20682466E+00 0.45799989E+00 0.19946632E+00 0.20393038E+00 0.45899987E+00 0.19910659E+00 0.20107877E+00 0.45999986E+00 0.19875622E+00 0.19827247E+00 0.46099985E+00 0.19841523E+00 0.19551492E+00 0.46199983E+00 0.19808351E+00 0.19280827E+00 0.46299982E+00 0.19776109E+00 0.19015503E+00 0.46399981E+00 0.19744773E+00 0.18755710E+00 0.46499979E+00 0.19714361E+00 0.18501735E+00 0.46599978E+00 0.19684854E+00 0.18253720E+00 0.46699977E+00 0.19656254E+00 0.18011868E+00 0.46799976E+00 0.19628556E+00 0.17776322E+00 0.46899974E+00 0.19601752E+00 0.17547286E+00 0.46999973E+00 0.19575837E+00 0.17324853E+00 0.47099972E+00 0.19550814E+00 0.17109168E+00 0.47199970E+00 0.19526668E+00 0.16900361E+00 0.47299969E+00 0.19503404E+00 0.16698503E+00 0.47399968E+00 0.19481021E+00 0.16503692E+00 0.47499967E+00 0.19459510E+00 0.16316020E+00 0.47599965E+00 0.19438866E+00 0.16135561E+00 0.47699964E+00 0.19419089E+00 0.15962350E+00 0.47799963E+00 0.19400181E+00 0.15796435E+00 0.47899961E+00 0.19382125E+00 0.15637863E+00 0.47999960E+00 0.19364931E+00 0.15486658E+00 0.48099959E+00 0.19348592E+00 0.15342855E+00 0.48199958E+00 0.19333108E+00 0.15206480E+00 0.48299956E+00 0.19318475E+00 0.15077531E+00 0.48399955E+00 0.19304684E+00 0.14956009E+00 0.48499954E+00 0.19291742E+00 0.14841914E+00 0.48599952E+00 0.19279647E+00 0.14735258E+00 0.48699951E+00 0.19268391E+00 0.14636040E+00 0.48799950E+00 0.19257976E+00 0.14544225E+00 0.48899949E+00 0.19248401E+00 0.14459825E+00 0.48999947E+00 0.19239664E+00 0.14382792E+00 0.49099946E+00 0.19231759E+00 0.14313209E+00 0.49199945E+00 0.19224690E+00 0.14250946E+00 0.49299943E+00 0.19218463E+00 0.14196050E+00 0.49399942E+00 0.19213058E+00 0.14148510E+00 0.49499941E+00 0.19208489E+00 0.14108324E+00 0.49599940E+00 0.19204752E+00 0.14075434E+00 0.49699938E+00 0.19201851E+00 0.14049864E+00 0.49799937E+00 0.19199774E+00 0.14031601E+00 0.49899936E+00 0.19198529E+00 0.14020658E+00 0.50000000E+00 0.19198115E+00 0.14016998E+00 iar = 2 lag = 16 phi = 0.78192770E+00 -0.36338660E+00 1check error handling - test 1 test of ufs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ufs (y, n) ierr is 1 test of ufss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufss ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the correct form of the call statement is call ufss (y, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsf ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ufsf (yfft, n, lyfft, ldstak) ierr is 1 test of ufsfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsfs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the correct form of the call statement is call ufsfs (yfft, n, lyfft, ldstak, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq) ierr is 1 test of ufsm starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsm ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ufsm (y, ymiss, n) ierr is 1 test of ufsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsms ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the correct form of the call statement is call ufsms (y, ymiss, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsv ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ufsv (acov, lagmax, n) ierr is 1 test of ufsvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsvs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the correct form of the call statement is call ufsvs (acov, lagmax, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsmv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsmv ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the correct form of the call statement is call ufsmv (acov, nlppa, lagmax, n) ierr is 1 test of ufsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsmvs ------------------------------------- the input value of n is -10. the value of the argument n must be greater than or equal to 17. the input value of nf is -5. the value of the argument nf must be greater than or equal to 1. the input value of nw is -1. the value of the argument nw must be greater than or equal to 1. the correct form of the call statement is call ufsmvs (acov, nlppa, lagmax, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 1check error handling - test 2 test of ufss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufss ------------------------------------- the input value of ispcf is 20. the first dimension of spcf , as indicated by the argument ispcf , must be greater than or equal to nf . the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 148. the correct form of the call statement is call ufss (y, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsf ------------------------------------- the input value of lyfft is -11. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 86. the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 50. the correct form of the call statement is call ufsf (yfft, n, lyfft, ldstak) ierr is 1 test of ufsfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsfs ------------------------------------- the input value of ispcf is 20. the first dimension of spcf , as indicated by the argument ispcf , must be greater than or equal to nf . the input value of lyfft is -11. the length of yfft , as indicated by the argument lyfft , must be greater than or equal to 102. the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 174. the correct form of the call statement is call ufsfs (yfft, n, lyfft, ldstak, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq) ierr is 1 test of ufsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsms ------------------------------------- the input value of ispcf is 20. the first dimension of spcf , as indicated by the argument ispcf , must be greater than or equal to nf . the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 175. the correct form of the call statement is call ufsms (y, ymiss, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsvs ------------------------------------- the input value of ispcf is 20. the first dimension of spcf , as indicated by the argument ispcf , must be greater than or equal to nf . the input value of lagmax is 55. the value of the argument lagmax must be between 1 and n-1 , inclusive. the correct form of the call statement is call ufsvs (acov, lagmax, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsmvs ------------------------------------- the input value of ispcf is 20. the first dimension of spcf , as indicated by the argument ispcf , must be greater than or equal to nf . the input value of lagmax is 55. the value of the argument lagmax must be between 1 and n-1 , inclusive. the correct form of the call statement is call ufsmvs (acov, nlppa, lagmax, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 1lds too small test of ufss starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufss ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 148. the correct form of the call statement is call ufss (y, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsf starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsf ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 50. the correct form of the call statement is call ufsf (yfft, n, lyfft, ldstak) ierr is 1 test of ufsfs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsfs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 174. the correct form of the call statement is call ufsfs (yfft, n, lyfft, ldstak, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq) ierr is 1 test of ufsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsms ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 175. the correct form of the call statement is call ufsms (y, ymiss, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsvs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 120. the correct form of the call statement is call ufsvs (acov, lagmax, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsmvs ------------------------------------- the input value of ldstak is 0. the length of dstak , as indicated by the argument ldstak, must be greater than or equal to 120. the correct form of the call statement is call ufsmvs (acov, nlppa, lagmax, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 1all data and covariances missing test of ufsm starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsm ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call ufsm (y, ymiss, n) ierr is 1 test of ufsms starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsms ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call ufsms (y, ymiss, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 test of ufsmv starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsmv ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call ufsmv (acov, nlppa, lagmax, n) ierr is 1 test of ufsmvs starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsmvs ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call ufsmvs (acov, nlppa, lagmax, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 1every other data value missing test of ufsm LACOV = 101 LAGMAX = 32 N = 50 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsm ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call ufsm (y, ymiss, n) ierr is 1 test of ufsms LACOV = 17 LAGMAX = 16 N = 50 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine ufsms ------------------------------------- the covariances at lags zero and/or one could not be computed because of missing data. no further analysis is possible. the correct form of the call statement is call ufsms (y, ymiss, n, + nw, lags, nf, fmin, fmax, nprt, + spcf, ispcf, freq, ldstak) ierr is 1 1valid problem test of ufs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4685 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ++++++++++ c - i +++++++ i i ++++ i i ++++ i i +++ i -0.9096 - +++ - i ++ i i +++ i i ++ i i ++ i -1.8193 - ++ - i b ++ * w i i + i i ++ i i ++ i -2.7289 - + - i ++ i i + i i + i i ++ i -3.6385 - i + - i + i i ++ i i + i i + i -4.5482 - + - i + i i ++ i i + i i + i -5.4578 - + - i + i i + i i ++ i i + i -6.3674 - + - i + i i + i i + i i ++ i -7.2771 - + - i + i i + i i ++ i i + i -8.1867 - ++ - i + i i ++ i i ++ i i +++ i -9.0963 - +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++++++ c - i ++++++ i i +++ i i ++ i i ++ i -1.1087 - ++ - i + i i ++ i i + i i + i -2.2173 - + - i + i i + i i + b * w i i + i -3.3260 - + - i i i + i i + i i + i -4.4346 - + - i i i + i i + i i i + i -5.5433 - - i + i i + i i + i i i -6.6519 - + - i + i i + i i + i i + i -7.7606 - + - i + i i + i i + i i + i -8.8692 - ++ - i ++ i i + i i +++ i i ++ i -9.9779 - +++ - i ++ i i +++ i i ++++ i i ++++ i -11.0865 - ++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.2541 - c - i +++++ i i +++ ++ ++ i i ++++ ++ + i i +++ + + i -1.1943 - ++ ++ + - i +++ ++ i i ++++ + i i + i i i -2.6428 - + - i i i + i i + i i i -4.0912 - + b * w - i i i + i i i i + i -5.5397 - + - i + i i i i + i i ++ i -6.9881 - + i - i + i i ++ i i + i i + i -8.4366 - + - i + i i + i i ++ i i + i -9.8850 - + - i + i i ++ +++++++ i i ++ +++ ++ i i ++++++ + i -11.3335 - + - i + i i + i i + i i + i -12.7819 - + - i + i i + i i ++ i i +++ i -14.2304 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 32 / bw=0.0650 / edf= 7) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.5084 - c - i i i i i i i ++++ i -0.4448 - + + - i ++ i i + + i i +++++++ + i i ++ + i -2.3981 - ++ + - i +++ + + i i + i i + + i i + + + i -4.3514 - ++ + b * w - i + i i i i + i i i -6.3046 - - i + i i i i +++ i i + ++ ++ i -8.2579 - +++ + i - i + i i + i i ++ i i + i -10.2111 - + - i +++ i i + + ++ i i + + + i i ++ + + i -12.1644 - +++++ + - i + + + i i + i i + + + i i + + i -14.1176 - +++ - i + i i + i i + + i i + + i -16.0709 - + - i +++ i i i i i i i -18.0242 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of ufss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 2.8336 - + + + + + + + + + + + + c - i + + + i i + i i + i i + i i + i i + i 2.0000 - + - i i i + i i i i + i i i i + b * w i i i i + i i i i i i + i i i 1.0000 - + - i i i i i + i i i i 0.8000 - - i + i i i i + i i i 0.6000 - - i + i i i i + i i i i + i i i i + i 0.4000 - - i + i i + i i + i i i i + + i i + i i + i i + i i + + i i + + i i + + i 0.2206 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.7127 - c - i + + + i i + + + + i i + + + i i + + i i + + i i + + + i i + + + i i i 2.0000 - - i + i i i i i i + i i i i b * w i i i i + i i i i i 1.0000 - + - i i i i 0.8000 - + - i + i i i i i + i 0.6000 - + - i i i + i i i i + i i i 0.4000 - + - i + i i i i + i i + + + + + i i + + + i i + + + i i + i i i i + i i i 0.2000 - + - i i i + i i i i + i i + + i 0.1322 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.28336103E+01 0.31765008E+01 0.99999998E-02 0.28331456E+01 0.31546259E+01 0.20000000E-01 0.28318422E+01 0.30898814E+01 0.29999999E-01 0.28299389E+01 0.29855804E+01 0.39999999E-01 0.28277245E+01 0.28492470E+01 0.49999997E-01 0.28253970E+01 0.26947193E+01 0.59999995E-01 0.28229017E+01 0.25434000E+01 0.69999993E-01 0.28197818E+01 0.24232385E+01 0.79999991E-01 0.28150654E+01 0.23644519E+01 0.89999989E-01 0.28072248E+01 0.23920674E+01 0.99999987E-01 0.27942095E+01 0.25169067E+01 0.10999998E+00 0.27735667E+01 0.27280111E+01 0.11999998E+00 0.27426386E+01 0.29899344E+01 0.12999998E+00 0.26988132E+01 0.32472711E+01 0.13999999E+00 0.26398010E+01 0.34364262E+01 0.14999999E+00 0.25639052E+01 0.35017123E+01 0.16000000E+00 0.24702525E+01 0.34107442E+01 0.17000000E+00 0.23589497E+01 0.31637948E+01 0.18000001E+00 0.22311523E+01 0.27936499E+01 0.19000001E+00 0.20890326E+01 0.23558958E+01 0.20000002E+00 0.19356500E+01 0.19130828E+01 0.21000002E+00 0.17747395E+01 0.15183274E+01 0.22000003E+00 0.16104401E+01 0.12037824E+01 0.23000003E+00 0.14469945E+01 0.97712886E+00 0.24000004E+00 0.12884519E+01 0.82595944E+00 0.25000003E+00 0.11384019E+01 0.72707671E+00 0.26000002E+00 0.99976534E+00 0.65647447E+00 0.27000001E+00 0.87465709E+00 0.59642541E+00 0.28000000E+00 0.76432866E+00 0.53805006E+00 0.28999999E+00 0.66918719E+00 0.47992694E+00 0.29999998E+00 0.58888102E+00 0.42470348E+00 0.30999997E+00 0.52243447E+00 0.37583613E+00 0.31999996E+00 0.46841407E+00 0.33575833E+00 0.32999995E+00 0.42510474E+00 0.30558991E+00 0.33999994E+00 0.39067769E+00 0.28565729E+00 0.34999993E+00 0.36333764E+00 0.27592516E+00 0.35999992E+00 0.34143651E+00 0.27586174E+00 0.36999992E+00 0.32355165E+00 0.28386891E+00 0.37999991E+00 0.30852950E+00 0.29681349E+00 0.38999990E+00 0.29549527E+00 0.31017292E+00 0.39999989E+00 0.28383875E+00 0.31893790E+00 0.40999988E+00 0.27318168E+00 0.31896615E+00 0.41999987E+00 0.26333535E+00 0.30820858E+00 0.42999986E+00 0.25425446E+00 0.28727424E+00 0.43999985E+00 0.24599242E+00 0.25911653E+00 0.44999984E+00 0.23866272E+00 0.22800970E+00 0.45999983E+00 0.23240566E+00 0.19827282E+00 0.46999982E+00 0.22736382E+00 0.17324841E+00 0.47999981E+00 0.22366321E+00 0.15486634E+00 0.48999980E+00 0.22140181E+00 0.14382780E+00 0.50000000E+00 0.22064102E+00 0.14016998E+00 1valid problem test of ufsf starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4685 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ++++++++++ c - i +++++++ i i ++++ i i ++++ i i +++ i -0.9096 - +++ - i ++ i i +++ i i ++ i i ++ i -1.8193 - ++ - i b ++ * w i i + i i ++ i i ++ i -2.7289 - + - i ++ i i + i i + i i ++ i -3.6385 - i + - i + i i ++ i i + i i + i -4.5482 - + - i + i i ++ i i + i i + i -5.4578 - + - i + i i + i i ++ i i + i -6.3674 - + - i + i i + i i + i i ++ i -7.2771 - + - i + i i + i i ++ i i + i -8.1867 - ++ - i + i i ++ i i ++ i i +++ i -9.0963 - +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++++++ c - i ++++++ i i +++ i i ++ i i ++ i -1.1087 - ++ - i + i i ++ i i + i i + i -2.2173 - + - i + i i + i i + b * w i i + i -3.3260 - + - i i i + i i + i i + i -4.4346 - + - i i i + i i + i i i + i -5.5433 - - i + i i + i i + i i i -6.6519 - + - i + i i + i i + i i + i -7.7606 - + - i + i i + i i + i i + i -8.8692 - ++ - i ++ i i + i i +++ i i ++ i -9.9779 - +++ - i ++ i i +++ i i ++++ i i ++++ i -11.0865 - ++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.2542 - c - i +++++ i i +++ ++ ++ i i ++++ ++ + i i +++ + + i -1.1943 - ++ ++ + - i +++ ++ i i ++++ + i i + i i i -2.6428 - + - i i i + i i + i i i -4.0912 - + b * w - i i i + i i i i + i -5.5397 - + - i + i i i i + i i ++ i -6.9881 - + i - i + i i ++ i i + i i + i -8.4366 - + - i + i i + i i ++ i i + i -9.8850 - + - i + i i ++ +++++++ i i ++ +++ ++ i i ++++++ + i -11.3335 - + - i + i i + i i + i i + i -12.7819 - + - i + i i + i i ++ i i +++ i -14.2304 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 32 / bw=0.0650 / edf= 7) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.5084 - c - i i i i i i i ++++ i -0.4448 - + + - i ++ i i + + i i +++++++ + i i ++ + i -2.3981 - ++ + - i +++ + + i i + i i + + i i + + + i -4.3513 - ++ + b * w - i + i i i i + i i i -6.3046 - - i + i i i i +++ i i + ++ ++ i -8.2579 - +++ + i - i + i i + i i ++ i i + i -10.2111 - + - i +++ i i + + ++ i i + + + i i ++ + + i -12.1644 - +++++ + - i + + + i i + i i + + + i i + + i -14.1176 - +++ - i + i i + i i + + i i + + i -16.0709 - + - i +++ i i i i i i i -18.0241 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of ufsfs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 2.8336 - + + + + + + + + + + + + c - i + + + i i + i i + i i + i i + i i + i 2.0000 - + - i i i + i i i i + i i i i + b * w i i i i + i i i i i i + i i i 1.0000 - + - i i i i i + i i i i 0.8000 - - i + i i i i + i i i 0.6000 - - i + i i i i + i i i i + i i i i + i 0.4000 - - i + i i + i i + i i i i + + i i + i i + i i + i i + + i i + + i i + + i 0.2206 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.7127 - c - i + + + i i + + + + i i + + + i i + + i i + + i i + + + i i + + + i i i 2.0000 - - i + i i i i i i + i i i i b * w i i i i + i i i i i 1.0000 - + - i i i i 0.8000 - + - i + i i i i i + i 0.6000 - + - i i i + i i i i + i i i 0.4000 - + - i + i i i i + i i + + + + + i i + + + i i + + + i i + i i i i + i i i 0.2000 - + - i i i + i i i i + i i + + i 0.1322 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.28336105E+01 0.31765013E+01 0.99999998E-02 0.28331456E+01 0.31546266E+01 0.20000000E-01 0.28318424E+01 0.30898819E+01 0.29999999E-01 0.28299389E+01 0.29855812E+01 0.39999999E-01 0.28277245E+01 0.28492472E+01 0.49999997E-01 0.28253970E+01 0.26947196E+01 0.59999995E-01 0.28229020E+01 0.25434003E+01 0.69999993E-01 0.28197818E+01 0.24232388E+01 0.79999991E-01 0.28150654E+01 0.23644519E+01 0.89999989E-01 0.28072248E+01 0.23920674E+01 0.99999987E-01 0.27942095E+01 0.25169067E+01 0.10999998E+00 0.27735667E+01 0.27280116E+01 0.11999998E+00 0.27426386E+01 0.29899344E+01 0.12999998E+00 0.26988134E+01 0.32472711E+01 0.13999999E+00 0.26398010E+01 0.34364262E+01 0.14999999E+00 0.25639055E+01 0.35017123E+01 0.16000000E+00 0.24702525E+01 0.34107442E+01 0.17000000E+00 0.23589497E+01 0.31637950E+01 0.18000001E+00 0.22311523E+01 0.27936499E+01 0.19000001E+00 0.20890324E+01 0.23558958E+01 0.20000002E+00 0.19356499E+01 0.19130827E+01 0.21000002E+00 0.17747395E+01 0.15183272E+01 0.22000003E+00 0.16104400E+01 0.12037823E+01 0.23000003E+00 0.14469944E+01 0.97712874E+00 0.24000004E+00 0.12884518E+01 0.82595938E+00 0.25000003E+00 0.11384017E+01 0.72707653E+00 0.26000002E+00 0.99976516E+00 0.65647435E+00 0.27000001E+00 0.87465686E+00 0.59642524E+00 0.28000000E+00 0.76432848E+00 0.53804988E+00 0.28999999E+00 0.66918695E+00 0.47992671E+00 0.29999998E+00 0.58888084E+00 0.42470324E+00 0.30999997E+00 0.52243429E+00 0.37583578E+00 0.31999996E+00 0.46841383E+00 0.33575809E+00 0.32999995E+00 0.42510450E+00 0.30558968E+00 0.33999994E+00 0.39067757E+00 0.28565693E+00 0.34999993E+00 0.36333740E+00 0.27592480E+00 0.35999992E+00 0.34143627E+00 0.27586138E+00 0.36999992E+00 0.32355130E+00 0.28386855E+00 0.37999991E+00 0.30852926E+00 0.29681313E+00 0.38999990E+00 0.29549491E+00 0.31017256E+00 0.39999989E+00 0.28383839E+00 0.31893754E+00 0.40999988E+00 0.27318132E+00 0.31896579E+00 0.41999987E+00 0.26333511E+00 0.30820811E+00 0.42999986E+00 0.25425410E+00 0.28727376E+00 0.43999985E+00 0.24599218E+00 0.25911605E+00 0.44999984E+00 0.23866236E+00 0.22800910E+00 0.45999983E+00 0.23240530E+00 0.19827223E+00 0.46999982E+00 0.22736347E+00 0.17324781E+00 0.47999981E+00 0.22366297E+00 0.15486586E+00 0.48999980E+00 0.22140157E+00 0.14382744E+00 0.50000000E+00 0.22064078E+00 0.14016950E+00 1valid problem test of ufsm LACOV = 101 LAGMAX = 32 N = 50 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4685 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ++++++++++ c - i +++++++ i i ++++ i i ++++ i i +++ i -0.9096 - +++ - i ++ i i +++ i i ++ i i ++ i -1.8193 - ++ - i b ++ * w i i + i i ++ i i ++ i -2.7289 - + - i ++ i i + i i + i i ++ i -3.6385 - i + - i + i i ++ i i + i i + i -4.5482 - + - i + i i ++ i i + i i + i -5.4578 - + - i + i i + i i ++ i i + i -6.3674 - + - i + i i + i i + i i ++ i -7.2771 - + - i + i i + i i ++ i i + i -8.1867 - ++ - i + i i ++ i i ++ i i +++ i -9.0963 - +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++++++ c - i ++++++ i i +++ i i ++ i i ++ i -1.1087 - ++ - i + i i ++ i i + i i + i -2.2173 - + - i + i i + i i + b * w i i + i -3.3260 - + - i i i + i i + i i + i -4.4346 - + - i i i + i i + i i i + i -5.5433 - - i + i i + i i + i i i -6.6519 - + - i + i i + i i + i i + i -7.7606 - + - i + i i + i i + i i + i -8.8692 - ++ - i ++ i i + i i +++ i i ++ i -9.9779 - +++ - i ++ i i +++ i i ++++ i i ++++ i -11.0865 - ++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.2541 - c - i +++++ i i +++ ++ ++ i i ++++ ++ + i i +++ + + i -1.1943 - ++ ++ + - i +++ ++ i i ++++ + i i + i i i -2.6428 - + - i i i + i i + i i i -4.0912 - + b * w - i i i + i i i i + i -5.5397 - + - i + i i i i + i i ++ i -6.9881 - + i - i + i i ++ i i + i i + i -8.4366 - + - i + i i + i i ++ i i + i -9.8850 - + - i + i i ++ +++++++ i i ++ +++ ++ i i ++++++ + i -11.3335 - + - i + i i + i i + i i + i -12.7819 - + - i + i i + i i ++ i i +++ i -14.2304 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 32 / bw=0.0650 / edf= 7) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.5084 - c - i i i i i i i ++++ i -0.4448 - + + - i ++ i i + + i i +++++++ + i i ++ + i -2.3981 - ++ + - i +++ + + i i + i i + + i i + + + i -4.3514 - ++ + b * w - i + i i i i + i i i -6.3046 - - i + i i i i +++ i i + ++ ++ i -8.2579 - +++ + i - i + i i + i i ++ i i + i -10.2111 - + - i +++ i i + + ++ i i + + + i i ++ + + i -12.1644 - +++++ + - i + + + i i + i i + + + i i + + i -14.1176 - +++ - i + i i + i i + + i i + + i -16.0709 - + - i +++ i i i i i i i -18.0242 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of ufsms LACOV = 17 LAGMAX = 16 N = 50 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 2.8336 - + + + + + + + + + + + + c - i + + + i i + i i + i i + i i + i i + i 2.0000 - + - i i i + i i i i + i i i i + b * w i i i i + i i i i i i + i i i 1.0000 - + - i i i i i + i i i i 0.8000 - - i + i i i i + i i i 0.6000 - - i + i i i i + i i i i + i i i i + i 0.4000 - - i + i i + i i + i i i i + + i i + i i + i i + i i + + i i + + i i + + i 0.2206 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.7127 - c - i + + + i i + + + + i i + + + i i + + i i + + i i + + + i i + + + i i i 2.0000 - - i + i i i i i i + i i i i b * w i i i i + i i i i i 1.0000 - + - i i i i 0.8000 - + - i + i i i i i + i 0.6000 - + - i i i + i i i i + i i i 0.4000 - + - i + i i i i + i i + + + + + i i + + + i i + + + i i + i i i i + i i i 0.2000 - + - i i i + i i i i + i i + + i 0.1322 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.28336103E+01 0.31765008E+01 0.99999998E-02 0.28331456E+01 0.31546259E+01 0.20000000E-01 0.28318422E+01 0.30898814E+01 0.29999999E-01 0.28299389E+01 0.29855807E+01 0.39999999E-01 0.28277245E+01 0.28492470E+01 0.49999997E-01 0.28253970E+01 0.26947193E+01 0.59999995E-01 0.28229017E+01 0.25433998E+01 0.69999993E-01 0.28197818E+01 0.24232388E+01 0.79999991E-01 0.28150654E+01 0.23644519E+01 0.89999989E-01 0.28072248E+01 0.23920674E+01 0.99999987E-01 0.27942095E+01 0.25169065E+01 0.10999998E+00 0.27735667E+01 0.27280111E+01 0.11999998E+00 0.27426386E+01 0.29899344E+01 0.12999998E+00 0.26988132E+01 0.32472711E+01 0.13999999E+00 0.26398010E+01 0.34364262E+01 0.14999999E+00 0.25639052E+01 0.35017123E+01 0.16000000E+00 0.24702525E+01 0.34107442E+01 0.17000000E+00 0.23589497E+01 0.31637950E+01 0.18000001E+00 0.22311523E+01 0.27936499E+01 0.19000001E+00 0.20890326E+01 0.23558958E+01 0.20000002E+00 0.19356500E+01 0.19130828E+01 0.21000002E+00 0.17747395E+01 0.15183274E+01 0.22000003E+00 0.16104401E+01 0.12037824E+01 0.23000003E+00 0.14469945E+01 0.97712886E+00 0.24000004E+00 0.12884519E+01 0.82595956E+00 0.25000003E+00 0.11384019E+01 0.72707671E+00 0.26000002E+00 0.99976534E+00 0.65647447E+00 0.27000001E+00 0.87465709E+00 0.59642541E+00 0.28000000E+00 0.76432866E+00 0.53805006E+00 0.28999999E+00 0.66918719E+00 0.47992706E+00 0.29999998E+00 0.58888108E+00 0.42470348E+00 0.30999997E+00 0.52243453E+00 0.37583613E+00 0.31999996E+00 0.46841407E+00 0.33575845E+00 0.32999995E+00 0.42510474E+00 0.30559015E+00 0.33999994E+00 0.39067769E+00 0.28565741E+00 0.34999993E+00 0.36333764E+00 0.27592528E+00 0.35999992E+00 0.34143639E+00 0.27586186E+00 0.36999992E+00 0.32355165E+00 0.28386879E+00 0.37999991E+00 0.30852950E+00 0.29681349E+00 0.38999990E+00 0.29549527E+00 0.31017303E+00 0.39999989E+00 0.28383875E+00 0.31893790E+00 0.40999988E+00 0.27318168E+00 0.31896615E+00 0.41999987E+00 0.26333547E+00 0.30820847E+00 0.42999986E+00 0.25425434E+00 0.28727424E+00 0.43999985E+00 0.24599242E+00 0.25911653E+00 0.44999984E+00 0.23866272E+00 0.22800970E+00 0.45999983E+00 0.23240566E+00 0.19827271E+00 0.46999982E+00 0.22736371E+00 0.17324841E+00 0.47999981E+00 0.22366321E+00 0.15486622E+00 0.48999980E+00 0.22140169E+00 0.14382780E+00 0.50000000E+00 0.22064102E+00 0.14016998E+00 1valid problem test of ufsv starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4685 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ++++++++++ c - i +++++++ i i ++++ i i ++++ i i +++ i -0.9096 - +++ - i ++ i i +++ i i ++ i i ++ i -1.8193 - ++ - i b ++ * w i i + i i ++ i i ++ i -2.7289 - + - i ++ i i + i i + i i ++ i -3.6385 - i + - i + i i ++ i i + i i + i -4.5482 - + - i + i i ++ i i + i i + i -5.4578 - + - i + i i + i i ++ i i + i -6.3674 - + - i + i i + i i + i i ++ i -7.2771 - + - i + i i + i i ++ i i + i -8.1867 - ++ - i + i i ++ i i ++ i i +++ i -9.0963 - +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++++++ c - i ++++++ i i +++ i i ++ i i ++ i -1.1087 - ++ - i + i i ++ i i + i i + i -2.2173 - + - i + i i + i i + b * w i i + i -3.3260 - + - i i i + i i + i i + i -4.4346 - + - i i i + i i + i i i + i -5.5433 - - i + i i + i i + i i i -6.6519 - + - i + i i + i i + i i + i -7.7606 - + - i + i i + i i + i i + i -8.8692 - ++ - i ++ i i + i i +++ i i ++ i -9.9779 - +++ - i ++ i i +++ i i ++++ i i ++++ i -11.0865 - ++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.2541 - c - i +++++ i i +++ ++ ++ i i ++++ ++ + i i +++ + + i -1.1943 - ++ ++ + - i +++ ++ i i ++++ + i i + i i i -2.6428 - + - i i i + i i + i i i -4.0912 - + b * w - i i i + i i i i + i -5.5397 - + - i + i i i i + i i ++ i -6.9881 - + i - i + i i ++ i i + i i + i -8.4366 - + - i + i i + i i ++ i i + i -9.8850 - + - i + i i ++ +++++++ i i ++ +++ ++ i i ++++++ + i -11.3335 - + - i + i i + i i + i i + i -12.7819 - + - i + i i + i i ++ i i +++ i -14.2304 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 32 / bw=0.0650 / edf= 7) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.5084 - c - i i i i i i i ++++ i -0.4448 - + + - i ++ i i + + i i +++++++ + i i ++ + i -2.3981 - ++ + - i +++ + + i i + i i + + i i + + + i -4.3514 - ++ + b * w - i + i i i i + i i i -6.3046 - - i + i i i i +++ i i + ++ ++ i -8.2579 - +++ + i - i + i i + i i ++ i i + i -10.2111 - + - i +++ i i + + ++ i i + + + i i ++ + + i -12.1644 - +++++ + - i + + + i i + i i + + + i i + + i -14.1176 - +++ - i + i i + i i + + i i + + i -16.0709 - + - i +++ i i i i i i i -18.0242 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of ufsvs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 2.8336 - + + + + + + + + + + + + c - i + + + i i + i i + i i + i i + i i + i 2.0000 - + - i i i + i i i i + i i i i + b * w i i i i + i i i i i i + i i i 1.0000 - + - i i i i i + i i i i 0.8000 - - i + i i i i + i i i 0.6000 - - i + i i i i + i i i i + i i i i + i 0.4000 - - i + i i + i i + i i i i + + i i + i i + i i + i i + + i i + + i i + + i 0.2206 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.7127 - c - i + + + i i + + + + i i + + + i i + + i i + + i i + + + i i + + + i i i 2.0000 - - i + i i i i i i + i i i i b * w i i i i + i i i i i 1.0000 - + - i i i i 0.8000 - + - i + i i i i i + i 0.6000 - + - i i i + i i i i + i i i 0.4000 - + - i + i i i i + i i + + + + + i i + + + i i + + + i i + i i i i + i i i 0.2000 - + - i i i + i i i i + i i + + i 0.1322 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.28336103E+01 0.31765008E+01 0.99999998E-02 0.28331456E+01 0.31546259E+01 0.20000000E-01 0.28318422E+01 0.30898814E+01 0.29999999E-01 0.28299389E+01 0.29855804E+01 0.39999999E-01 0.28277245E+01 0.28492470E+01 0.49999997E-01 0.28253970E+01 0.26947193E+01 0.59999995E-01 0.28229017E+01 0.25434000E+01 0.69999993E-01 0.28197818E+01 0.24232385E+01 0.79999991E-01 0.28150654E+01 0.23644519E+01 0.89999989E-01 0.28072248E+01 0.23920674E+01 0.99999987E-01 0.27942095E+01 0.25169067E+01 0.10999998E+00 0.27735667E+01 0.27280111E+01 0.11999998E+00 0.27426386E+01 0.29899344E+01 0.12999998E+00 0.26988132E+01 0.32472711E+01 0.13999999E+00 0.26398010E+01 0.34364262E+01 0.14999999E+00 0.25639052E+01 0.35017123E+01 0.16000000E+00 0.24702525E+01 0.34107442E+01 0.17000000E+00 0.23589497E+01 0.31637948E+01 0.18000001E+00 0.22311523E+01 0.27936499E+01 0.19000001E+00 0.20890326E+01 0.23558958E+01 0.20000002E+00 0.19356500E+01 0.19130828E+01 0.21000002E+00 0.17747395E+01 0.15183274E+01 0.22000003E+00 0.16104401E+01 0.12037824E+01 0.23000003E+00 0.14469945E+01 0.97712886E+00 0.24000004E+00 0.12884519E+01 0.82595944E+00 0.25000003E+00 0.11384019E+01 0.72707671E+00 0.26000002E+00 0.99976534E+00 0.65647447E+00 0.27000001E+00 0.87465709E+00 0.59642541E+00 0.28000000E+00 0.76432866E+00 0.53805006E+00 0.28999999E+00 0.66918719E+00 0.47992694E+00 0.29999998E+00 0.58888102E+00 0.42470348E+00 0.30999997E+00 0.52243447E+00 0.37583613E+00 0.31999996E+00 0.46841407E+00 0.33575833E+00 0.32999995E+00 0.42510474E+00 0.30558991E+00 0.33999994E+00 0.39067769E+00 0.28565729E+00 0.34999993E+00 0.36333764E+00 0.27592516E+00 0.35999992E+00 0.34143651E+00 0.27586174E+00 0.36999992E+00 0.32355165E+00 0.28386891E+00 0.37999991E+00 0.30852950E+00 0.29681349E+00 0.38999990E+00 0.29549527E+00 0.31017292E+00 0.39999989E+00 0.28383875E+00 0.31893790E+00 0.40999988E+00 0.27318168E+00 0.31896615E+00 0.41999987E+00 0.26333535E+00 0.30820858E+00 0.42999986E+00 0.25425446E+00 0.28727424E+00 0.43999985E+00 0.24599242E+00 0.25911653E+00 0.44999984E+00 0.23866272E+00 0.22800970E+00 0.45999983E+00 0.23240566E+00 0.19827282E+00 0.46999982E+00 0.22736382E+00 0.17324841E+00 0.47999981E+00 0.22366321E+00 0.15486634E+00 0.48999980E+00 0.22140181E+00 0.14382780E+00 0.50000000E+00 0.22064102E+00 0.14016998E+00 1valid problem LACOV = 101 LAGMAX = 49 N = 50 test of ufsmv starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4685 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ++++++++++ c - i +++++++ i i ++++ i i ++++ i i +++ i -0.9096 - +++ - i ++ i i +++ i i ++ i i ++ i -1.8193 - ++ - i b ++ * w i i + i i ++ i i ++ i -2.7289 - + - i ++ i i + i i + i i ++ i -3.6385 - i + - i + i i ++ i i + i i + i -4.5482 - + - i + i i ++ i i + i i + i -5.4578 - + - i + i i + i i ++ i i + i -6.3674 - + - i + i i + i i + i i ++ i -7.2771 - + - i + i i + i i ++ i i + i -8.1867 - ++ - i + i i ++ i i ++ i i +++ i -9.0963 - +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++++++ c - i ++++++ i i +++ i i ++ i i ++ i -1.1087 - ++ - i + i i ++ i i + i i + i -2.2173 - + - i + i i + i i + b * w i i + i -3.3260 - + - i i i + i i + i i + i -4.4346 - + - i i i + i i + i i i + i -5.5433 - - i + i i + i i + i i i -6.6519 - + - i + i i + i i + i i + i -7.7606 - + - i + i i + i i + i i + i -8.8692 - ++ - i ++ i i + i i +++ i i ++ i -9.9779 - +++ - i ++ i i +++ i i ++++ i i ++++ i -11.0865 - ++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.2541 - c - i +++++ i i +++ ++ ++ i i ++++ ++ + i i +++ + + i -1.1943 - ++ ++ + - i +++ ++ i i ++++ + i i + i i i -2.6428 - + - i i i + i i + i i i -4.0912 - + b * w - i i i + i i i i + i -5.5397 - + - i + i i i i + i i ++ i -6.9881 - + i - i + i i ++ i i + i i + i -8.4366 - + - i + i i + i i ++ i i + i -9.8850 - + - i + i i ++ +++++++ i i ++ +++ ++ i i ++++++ + i -11.3335 - + - i + i i + i i + i i + i -12.7819 - + - i + i i + i i ++ i i +++ i -14.2304 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 32 / bw=0.0650 / edf= 7) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.5084 - c - i i i i i i i ++++ i -0.4448 - + + - i ++ i i + + i i +++++++ + i i ++ + i -2.3981 - ++ + - i +++ + + i i + i i + + i i + + + i -4.3514 - ++ + b * w - i + i i i i + i i i -6.3046 - - i + i i i i +++ i i + ++ ++ i -8.2579 - +++ + i - i + i i + i i ++ i i + i -10.2111 - + - i +++ i i + + ++ i i + + + i i ++ + + i -12.1644 - +++++ + - i + + + i i + i i + + + i i + + i -14.1176 - +++ - i + i i + i i + + i i + + i -16.0709 - + - i +++ i i i i i i i -18.0242 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1valid problem test of ufsmvs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 2.8336 - + + + + + + + + + + + + c - i + + + i i + i i + i i + i i + i i + i 2.0000 - + - i i i + i i i i + i i i i + b * w i i i i + i i i i i i + i i i 1.0000 - + - i i i i i + i i i i 0.8000 - - i + i i i i + i i i 0.6000 - - i + i i i i + i i i i + i i i i + i 0.4000 - - i + i i + i i + i i i i + + i i + i i + i i + i i + + i i + + i i + + i 0.2206 - + + + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.7127 - c - i + + + i i + + + + i i + + + i i + + i i + + i i + + + i i + + + i i i 2.0000 - - i + i i i i i i + i i i i b * w i i i i + i i i i i 1.0000 - + - i i i i 0.8000 - + - i + i i i i i + i 0.6000 - + - i i i + i i i i + i i i 0.4000 - + - i + i i i i + i i + + + + + i i + + + i i + + + i i + i i i i + i i i 0.2000 - + - i i i + i i i i + i i + + i 0.1322 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.28336103E+01 0.31765008E+01 0.99999998E-02 0.28331456E+01 0.31546259E+01 0.20000000E-01 0.28318422E+01 0.30898814E+01 0.29999999E-01 0.28299389E+01 0.29855807E+01 0.39999999E-01 0.28277245E+01 0.28492470E+01 0.49999997E-01 0.28253970E+01 0.26947193E+01 0.59999995E-01 0.28229017E+01 0.25433998E+01 0.69999993E-01 0.28197818E+01 0.24232388E+01 0.79999991E-01 0.28150654E+01 0.23644519E+01 0.89999989E-01 0.28072248E+01 0.23920674E+01 0.99999987E-01 0.27942095E+01 0.25169065E+01 0.10999998E+00 0.27735667E+01 0.27280111E+01 0.11999998E+00 0.27426386E+01 0.29899344E+01 0.12999998E+00 0.26988132E+01 0.32472711E+01 0.13999999E+00 0.26398010E+01 0.34364262E+01 0.14999999E+00 0.25639052E+01 0.35017123E+01 0.16000000E+00 0.24702525E+01 0.34107442E+01 0.17000000E+00 0.23589497E+01 0.31637950E+01 0.18000001E+00 0.22311523E+01 0.27936499E+01 0.19000001E+00 0.20890326E+01 0.23558958E+01 0.20000002E+00 0.19356500E+01 0.19130828E+01 0.21000002E+00 0.17747395E+01 0.15183274E+01 0.22000003E+00 0.16104401E+01 0.12037824E+01 0.23000003E+00 0.14469945E+01 0.97712886E+00 0.24000004E+00 0.12884519E+01 0.82595956E+00 0.25000003E+00 0.11384019E+01 0.72707671E+00 0.26000002E+00 0.99976534E+00 0.65647447E+00 0.27000001E+00 0.87465709E+00 0.59642541E+00 0.28000000E+00 0.76432866E+00 0.53805006E+00 0.28999999E+00 0.66918719E+00 0.47992706E+00 0.29999998E+00 0.58888108E+00 0.42470348E+00 0.30999997E+00 0.52243453E+00 0.37583613E+00 0.31999996E+00 0.46841407E+00 0.33575845E+00 0.32999995E+00 0.42510474E+00 0.30559015E+00 0.33999994E+00 0.39067769E+00 0.28565741E+00 0.34999993E+00 0.36333764E+00 0.27592528E+00 0.35999992E+00 0.34143639E+00 0.27586186E+00 0.36999992E+00 0.32355165E+00 0.28386879E+00 0.37999991E+00 0.30852950E+00 0.29681349E+00 0.38999990E+00 0.29549527E+00 0.31017303E+00 0.39999989E+00 0.28383875E+00 0.31893790E+00 0.40999988E+00 0.27318168E+00 0.31896615E+00 0.41999987E+00 0.26333547E+00 0.30820847E+00 0.42999986E+00 0.25425434E+00 0.28727424E+00 0.43999985E+00 0.24599242E+00 0.25911653E+00 0.44999984E+00 0.23866272E+00 0.22800970E+00 0.45999983E+00 0.23240566E+00 0.19827271E+00 0.46999982E+00 0.22736371E+00 0.17324841E+00 0.47999981E+00 0.22366321E+00 0.15486622E+00 0.48999980E+00 0.22140169E+00 0.14382780E+00 0.50000000E+00 0.22064102E+00 0.14016998E+00 1minimum problem size test of ufs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4798 / edf= 16) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.6174 - c - i i i i i i i i 0.3759 - - i i i +++++++++++++++++++++++++++++ i i ++++++ i i +++++ i -0.8656 - +++ - i +++ i i +++ i i ++ i i +++ i -2.1072 - b * ++ w - i ++ i i + i i ++ i i ++ i -3.3487 - + - i ++ i i + i i ++ i i + i -4.5902 - i + - i ++ i i + i i + i i + i -5.8317 - ++ - i + i i + i i + i i ++ i -7.0732 - + - i + i i ++ i i + i i + i -8.3147 - ++ - i ++ i i ++ i i ++++++ i i i -9.5563 - - i i i i i i i i -10.7978 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2514 / edf= 9) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.1223 - c - i i i i i ++++++ i i ++++ ++++ i -0.5734 - ++ ++ - i ++ ++ i i ++ ++ i i + + i i ++ ++ i -2.2691 - + + - i + + i i ++ + i i + ++ i i ++ + i -3.9648 - + b+ * w - i +++ + i i ++ + i i + i i + i -5.6606 - + - i + i i + i i + i i + i -7.3563 - + i - i + i i + i i + i i + i -9.0520 - ++ - i + i i + i i ++ i i + i -10.7477 - ++ - i ++ i i + i i ++ i i ++ i -12.4435 - + - i ++ i i ++ i i ++ i i ++ i -14.1392 - +++ - i ++++ i i +++ i i i i i -15.8349 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1379 / edf= 5) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.8316 - c - i i i i i i i +++++++ i -0.5457 - ++ ++ - i + ++ i i + + i i ++ + i i + + i -2.9229 - + + - i + i i + + i i + i i + + i -5.3001 - + + - i + b * w i i + + i i + i i + i -7.6774 - + + - i + ++ i i + ++ i i +++ i i + +++ i -10.0546 - + ++ i - i ++ i i + ++ i i + ++ i i + ++ i -12.4319 - ++ ++++ - i + ++++ i i ++ i i ++ i i ++ i -14.8091 - + - i ++ i i + i i ++ i i ++ i -17.1863 - + - i ++ i i + i i + i i ++ i -19.5636 - + - i +++ i i i i i i i -21.9408 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 1minimum problem size test of ufss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 1 / bw=1.0000 / edf= 34) the plot has been supressed because no positive spectrum values were computed. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1379 / edf= 5) the plot has been supressed because no positive spectrum values were computed. ierr is 0 0.00000000E+00 0.15017184E+01 0.30369794E+00 1check handling of fmin and fmax test of ufss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.7284 - c - i i i i i i 0.6000 - - i i i i i i i i i i i i i i i i 0.4000 - - i b * w i i i i i i i i i i i i i i i i i i i i +22+ i i 2223232242 i i 232322322+ i i i 0.2000 - - i i i i i i i i i i i i i i i i i i i i i i i i i i i i 0.1000 - - i i i i i i i i 0.0800 - - i i 0.0723 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.3560 0.3798 0.4036 0.4274 0.4512 0.4750 0.4988 0.5226 0.5464 0.5702 0.5940 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.9474 - c - i i i i 0.8000 - - i i i i i i 0.6000 - - i i i i i i i i i i 0.4000 - - i i i b * w i i i i i i i i i i i i + i i 22+ i 0.2000 - 22+ - i +3+ i i +3+ i i i +22 i i +222 i i +2322+ i i 222+ i i i i i i i i i 0.1000 - - i i i i 0.0800 - - i i i i i i 0.0600 - - i i i i i i i i i i 0.0400 - - i i i i 0.0337 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.3560 0.3798 0.4036 0.4274 0.4512 0.4750 0.4988 0.5226 0.5464 0.5702 0.5940 ierr is 0 0.44999999E+00 0.23866272E+00 0.22800922E+00 0.45099998E+00 0.23798633E+00 0.22490859E+00 0.45199996E+00 0.23732078E+00 0.22182643E+00 0.45299995E+00 0.23666632E+00 0.21876681E+00 0.45399994E+00 0.23602295E+00 0.21573329E+00 0.45499992E+00 0.23539090E+00 0.21272957E+00 0.45599991E+00 0.23477042E+00 0.20975888E+00 0.45699990E+00 0.23416138E+00 0.20682466E+00 0.45799989E+00 0.23356438E+00 0.20393038E+00 0.45899987E+00 0.23297894E+00 0.20107877E+00 0.45999986E+00 0.23240566E+00 0.19827247E+00 0.46099985E+00 0.23184443E+00 0.19551492E+00 0.46199983E+00 0.23129570E+00 0.19280827E+00 0.46299982E+00 0.23075938E+00 0.19015503E+00 0.46399981E+00 0.23023546E+00 0.18755710E+00 0.46499979E+00 0.22972441E+00 0.18501735E+00 0.46599978E+00 0.22922611E+00 0.18253720E+00 0.46699977E+00 0.22874069E+00 0.18011868E+00 0.46799976E+00 0.22826862E+00 0.17776322E+00 0.46899974E+00 0.22780943E+00 0.17547286E+00 0.46999973E+00 0.22736371E+00 0.17324853E+00 0.47099972E+00 0.22693145E+00 0.17109168E+00 0.47199970E+00 0.22651279E+00 0.16900361E+00 0.47299969E+00 0.22610760E+00 0.16698503E+00 0.47399968E+00 0.22571635E+00 0.16503692E+00 0.47499967E+00 0.22533894E+00 0.16316020E+00 0.47599965E+00 0.22497547E+00 0.16135561E+00 0.47699964E+00 0.22462606E+00 0.15962350E+00 0.47799963E+00 0.22429085E+00 0.15796435E+00 0.47899961E+00 0.22396982E+00 0.15637863E+00 0.47999960E+00 0.22366321E+00 0.15486658E+00 0.48099959E+00 0.22337103E+00 0.15342855E+00 0.48199958E+00 0.22309327E+00 0.15206480E+00 0.48299956E+00 0.22283018E+00 0.15077531E+00 0.48399955E+00 0.22258162E+00 0.14956009E+00 0.48499954E+00 0.22234786E+00 0.14841914E+00 0.48599952E+00 0.22212887E+00 0.14735258E+00 0.48699951E+00 0.22192478E+00 0.14636040E+00 0.48799950E+00 0.22173548E+00 0.14544225E+00 0.48899949E+00 0.22156119E+00 0.14459825E+00 0.48999947E+00 0.22140181E+00 0.14382792E+00 0.49099946E+00 0.22125757E+00 0.14313209E+00 0.49199945E+00 0.22112846E+00 0.14250946E+00 0.49299943E+00 0.22101426E+00 0.14196050E+00 0.49399942E+00 0.22091532E+00 0.14148510E+00 0.49499941E+00 0.22083163E+00 0.14108324E+00 0.49599940E+00 0.22076297E+00 0.14075434E+00 0.49699938E+00 0.22070968E+00 0.14049864E+00 0.49799937E+00 0.22067153E+00 0.14031601E+00 0.49899936E+00 0.22064877E+00 0.14020658E+00 0.50000000E+00 0.22064102E+00 0.14016998E+00 1white noise spectrum test of ufss starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 2.9948 - c - i i i i i i i i i i i i i i i i 2.0000 - - i i i i i i i i i b * w i i i i i i i i i i + + + + + + i i + + + + i i + + i i + + i i + + i 1.0000 - + + - i + i + i i + + + i i + + i i + + + + + + + i 0.8000 - + + + + + + + + - i + + + + + i i + + + + + + i i i i i i i 0.6000 - - i i i i i i i i i i i i i i i i 0.4000 - - i i i i i i i i i i 0.2972 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 5.2649 - c - i i i i i i 4.0000 - - i i i i i i i i i i i i i i i i i i i i 2.0000 - b * w - i i i + + + i i + + i i i i + + i i i i + + i i i i + + i 1.0000 - + i - i + + + + + + i i + + + + + + i 0.8000 - + + + + + + + + + + - i + + + + i i + + + i i + + + + i i + + i 0.6000 - + + + + - i i i i i i i i i i 0.4000 - - i i i i i i i i i i i i i i i i i i 0.2000 - - 0.1875 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. ierr is 0 0.00000000E+00 0.81244922E+00 0.88183784E+00 0.99999998E-02 0.81069386E+00 0.88204479E+00 0.20000000E-01 0.80554926E+00 0.88111001E+00 0.29999999E-01 0.79737055E+00 0.87513399E+00 0.39999999E-01 0.78672040E+00 0.85978889E+00 0.49999997E-01 0.77432811E+00 0.83243561E+00 0.59999995E-01 0.76103908E+00 0.79346544E+00 0.69999993E-01 0.74776030E+00 0.74635768E+00 0.79999991E-01 0.73540616E+00 0.69661921E+00 0.89999989E-01 0.72485119E+00 0.65024865E+00 0.99999987E-01 0.71689165E+00 0.61242902E+00 0.10999998E+00 0.71221930E+00 0.58684528E+00 0.11999998E+00 0.71140689E+00 0.57560802E+00 0.12999998E+00 0.71490324E+00 0.57949877E+00 0.13999999E+00 0.72303540E+00 0.59823370E+00 0.14999999E+00 0.73601377E+00 0.63059151E+00 0.16000000E+00 0.75393558E+00 0.67439568E+00 0.17000000E+00 0.77678382E+00 0.72641182E+00 0.18000001E+00 0.80442065E+00 0.78224880E+00 0.19000001E+00 0.83657223E+00 0.83643341E+00 0.20000002E+00 0.87281054E+00 0.88294631E+00 0.21000002E+00 0.91253257E+00 0.91652620E+00 0.22000003E+00 0.95494366E+00 0.93477529E+00 0.23000003E+00 0.99905044E+00 0.94051874E+00 0.24000004E+00 0.10436674E+01 0.94324207E+00 0.25000003E+00 0.10874429E+01 0.95822799E+00 0.26000002E+00 0.11289054E+01 0.10026124E+01 0.27000001E+00 0.11665306E+01 0.10889490E+01 0.28000000E+00 0.11988242E+01 0.12184353E+01 0.28999999E+00 0.12244163E+01 0.13768330E+01 0.29999998E+00 0.12421572E+01 0.15356171E+01 0.30999997E+00 0.12512076E+01 0.16589770E+01 0.31999996E+00 0.12511102E+01 0.17147088E+01 0.32999995E+00 0.12418368E+01 0.16850529E+01 0.33999994E+00 0.12238038E+01 0.15731776E+01 0.34999993E+00 0.11978519E+01 0.14026083E+01 0.35999992E+00 0.11651928E+01 0.12097499E+01 0.36999992E+00 0.11273248E+01 0.10324452E+01 0.37999991E+00 0.10859274E+01 0.89896607E+00 0.38999990E+00 0.10427434E+01 0.82131088E+00 0.39999989E+00 0.99946260E+00 0.79464620E+00 0.40999988E+00 0.95761722E+00 0.80214638E+00 0.41999987E+00 0.91850007E+00 0.82260108E+00 0.42999986E+00 0.88311207E+00 0.83766055E+00 0.43999985E+00 0.85214591E+00 0.83641165E+00 0.44999984E+00 0.82600105E+00 0.81650966E+00 0.45999983E+00 0.80482936E+00 0.78251314E+00 0.46999982E+00 0.78860223E+00 0.74282634E+00 0.47999981E+00 0.77718651E+00 0.70662385E+00 0.48999980E+00 0.77042133E+00 0.68159544E+00 0.50000000E+00 0.76818174E+00 0.67268950E+00 test of vp test number 0 n = 144 + / ns = 1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i+ i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i + i 180.00 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i + i 180.00 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i +i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of vpm test number 0 n = 144 + / ns = 1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i+ i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i i Missing 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i i Missing 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i +i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of svp test number 0 n = 144 + / ns = 1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 ik i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i b i 180.00 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i k i 180.00 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i gi 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of svpm test number 0 n = 144 + / ns = 1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 ik i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i i Missing 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i i Missing 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i gi 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of mvp test number 0 n = 12 + / m = 12 / iym = 12 + / ns = 1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i 2 c d ef g h i j k l i 2.0000 i ab c df e g h i j k l i 3.0000 i a b c d 2 g h ij k l i 4.0000 i ab c d fe g h 2 k l i 5.0000 i ab c d ef g h i j k l i 6.0000 i a b c d e f g h i j k l i 7.0000 i a b c d e f g h i j k li 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 ia b c d e f g h ij k l i 12.000 i a b c d e f g h 2 k l i ierr = 0 test of mvpm test number 0 n = 12 + / m = 12 / iym = 12 + / ns = 1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i 2 c d ef g h i j k l i 2.0000 i ab c f e g h i j k l i Missing 3.0000 i a b c d 2 g h ij k l i 4.0000 i ab c d fe g h 2 k l i 5.0000 i ab c d ef g h i j k l i 6.0000 i a b c d e f g h i j k l i 7.0000 i a b c d e f g h i j k li 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 ia b c d f g h ij k l i Missing 12.000 i a b c d e f g h 2 k l i ierr = 0 test of vpl test number 0 n = 144 + / ns = 1 + / ilog = -1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i+ i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i + i 180.00 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i + i 180.00 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i +i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of vpml test number 0 n = 144 + / ns = 1 + / ilog = -1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i+ i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i i Missing 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i i Missing 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i +i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of svpl test number 0 n = 144 + / ns = 1 + / ilog = -1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 ik i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i b i 180.00 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i k i 180.00 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i gi 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of svpml test number 0 n = 144 + / ns = 1 + / ilog = -1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 ik i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i i Missing 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i i Missing 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i gi 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of mvpl test number 0 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = -1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i 2 c d ef g h i j k l i 2.0000 i ab c df e g h i j k l i 3.0000 i a b c d 2 g h ij k l i 4.0000 i ab c d fe g h 2 k l i 5.0000 i ab c d ef g h i j k l i 6.0000 i a b c d e f g h i j k l i 7.0000 i a b c d e f g h i j k li 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 ia b c d e f g h ij k l i 12.000 i a b c d e f g h 2 k l i ierr = 0 test of mvpml test number 0 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = -1 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i 2 c d ef g h i j k l i 2.0000 i ab c f e g h i j k l i Missing 3.0000 i a b c d 2 g h ij k l i 4.0000 i ab c d fe g h 2 k l i 5.0000 i ab c d ef g h i j k l i 6.0000 i a b c d e f g h i j k l i 7.0000 i a b c d e f g h i j k li 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 ia b c d f g h ij k l i Missing 12.000 i a b c d e f g h 2 k l i ierr = 0 test of vpc test number 0 n = 144 + / ns = 1 + / ilog = -1 isize= -1 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- .00000 i + i 112.00 .00000 i + i 118.00 .00000 i + i 132.00 .00000 i + i 129.00 .00000 i + i 121.00 .00000 i + i 135.00 .00000 i + i 148.00 .00000 i + i 148.00 .00000 i + i 136.00 .00000 i + i 119.00 .00000 i+ i 104.00 .00000 i + i 118.00 .00000 i + i 115.00 .00000 i + i 126.00 .00000 i + i 141.00 .00000 i + i 135.00 .00000 i + i 125.00 .00000 i + i 149.00 .00000 i + i 170.00 .00000 i + i 170.00 .00000 i + i 158.00 .00000 i + i 133.00 .00000 i + i 114.00 .00000 i + i 140.00 .00000 i + i 145.00 .00000 i + i 150.00 .00000 i + i 178.00 .00000 i + i 163.00 .00000 i + i 172.00 .00000 i + i 178.00 .00000 i + i 199.00 .00000 i + i 199.00 .00000 i + i 184.00 .00000 i + i 162.00 .00000 i + i 146.00 .00000 i + i 166.00 .00000 i + i 171.00 .00000 i + i 180.00 .00000 i + i 193.00 .00000 i + i 181.00 .00000 i + i 183.00 .00000 i + i 218.00 .00000 i + i 230.00 .00000 i + i 242.00 .00000 i + i 209.00 .00000 i + i 191.00 .00000 i + i 172.00 .00000 i + i 194.00 .00000 i + i 196.00 .00000 i + i 196.00 .00000 i + i 236.00 .00000 i + i 235.00 .00000 i + i 229.00 .00000 i + i 243.00 .00000 i + i 264.00 .00000 i + i 272.00 .00000 i + i 237.00 .00000 i + i 211.00 .00000 i + i 180.00 .00000 i + i 201.00 .00000 i + i 204.00 .00000 i + i 188.00 .00000 i + i 235.00 .00000 i + i 227.00 .00000 i + i 234.00 .00000 i + i 264.00 .00000 i + i 302.00 .00000 i + i 293.00 .00000 i + i 259.00 .00000 i + i 229.00 .00000 i + i 203.00 .00000 i + i 229.00 .00000 i + i 242.00 .00000 i + i 233.00 .00000 i + i 267.00 .00000 i + i 269.00 .00000 i + i 270.00 .00000 i + i 315.00 .00000 i + i 364.00 .00000 i + i 347.00 .00000 i + i 312.00 .00000 i + i 274.00 .00000 i + i 237.00 .00000 i + i 278.00 .00000 i + i 284.00 .00000 i + i 277.00 .00000 i + i 317.00 .00000 i + i 313.00 .00000 i + i 318.00 .00000 i + i 374.00 .00000 i + i 413.00 .00000 i + i 405.00 .00000 i + i 355.00 .00000 i + i 306.00 .00000 i + i 271.00 .00000 i + i 306.00 .00000 i + i 315.00 .00000 i + i 301.00 .00000 i + i 356.00 .00000 i + i 348.00 .00000 i + i 355.00 .00000 i + i 422.00 .00000 i + i 465.00 .00000 i + i 467.00 .00000 i + i 404.00 .00000 i + i 347.00 .00000 i + i 305.00 .00000 i + i 336.00 .00000 i + i 340.00 .00000 i + i 318.00 .00000 i + i 362.00 .00000 i + i 348.00 .00000 i + i 363.00 .00000 i + i 435.00 .00000 i + i 491.00 .00000 i + i 505.00 .00000 i + i 404.00 .00000 i + i 359.00 .00000 i + i 310.00 .00000 i + i 337.00 .00000 i + i 360.00 .00000 i + i 342.00 .00000 i + i 406.00 .00000 i + i 396.00 .00000 i + i 420.00 .00000 i + i 472.00 .00000 i + i 548.00 .00000 i + i 559.00 .00000 i + i 463.00 .00000 i + i 407.00 .00000 i + i 362.00 .00000 i + i 405.00 .00000 i + i 417.00 .00000 i + i 391.00 .00000 i + i 419.00 .00000 i + i 461.00 .00000 i + i 472.00 .00000 i + i 535.00 .00000 i +i 622.00 .00000 i + i 606.00 .00000 i + i 508.00 .00000 i + i 461.00 .00000 i + i 390.00 .00000 i + i 432.00 ierr = 0 test of vpmc test number 0 n = 144 + / ns = 1 + / ilog = -1 isize= -1 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- .00000 i + i 112.00 .00000 i + i 118.00 .00000 i + i 132.00 .00000 i + i 129.00 .00000 i + i 121.00 .00000 i + i 135.00 .00000 i + i 148.00 .00000 i + i 148.00 .00000 i + i 136.00 .00000 i + i 119.00 .00000 i+ i 104.00 .00000 i + i 118.00 .00000 i + i 115.00 .00000 i + i 126.00 .00000 i + i 141.00 .00000 i + i 135.00 .00000 i + i 125.00 .00000 i + i 149.00 .00000 i + i 170.00 .00000 i + i 170.00 .00000 i + i 158.00 .00000 i + i 133.00 .00000 i + i 114.00 .00000 i + i 140.00 .00000 i + i 145.00 .00000 i + i 150.00 .00000 i + i 178.00 .00000 i + i 163.00 .00000 i + i 172.00 .00000 i + i 178.00 .00000 i + i 199.00 .00000 i + i 199.00 .00000 i + i 184.00 .00000 i + i 162.00 .00000 i + i 146.00 .00000 i + i 166.00 .00000 i + i 171.00 .00000 i i Missing .00000 i + i 193.00 .00000 i + i 181.00 .00000 i + i 183.00 .00000 i + i 218.00 .00000 i + i 230.00 .00000 i + i 242.00 .00000 i + i 209.00 .00000 i + i 191.00 .00000 i + i 172.00 .00000 i + i 194.00 .00000 i + i 196.00 .00000 i + i 196.00 .00000 i + i 236.00 .00000 i + i 235.00 .00000 i + i 229.00 .00000 i + i 243.00 .00000 i + i 264.00 .00000 i + i 272.00 .00000 i + i 237.00 .00000 i + i 211.00 .00000 i i Missing .00000 i + i 201.00 .00000 i + i 204.00 .00000 i + i 188.00 .00000 i + i 235.00 .00000 i + i 227.00 .00000 i + i 234.00 .00000 i + i 264.00 .00000 i + i 302.00 .00000 i + i 293.00 .00000 i + i 259.00 .00000 i + i 229.00 .00000 i + i 203.00 .00000 i + i 229.00 .00000 i + i 242.00 .00000 i + i 233.00 .00000 i + i 267.00 .00000 i + i 269.00 .00000 i + i 270.00 .00000 i + i 315.00 .00000 i + i 364.00 .00000 i + i 347.00 .00000 i + i 312.00 .00000 i + i 274.00 .00000 i + i 237.00 .00000 i + i 278.00 .00000 i + i 284.00 .00000 i + i 277.00 .00000 i + i 317.00 .00000 i + i 313.00 .00000 i + i 318.00 .00000 i + i 374.00 .00000 i + i 413.00 .00000 i + i 405.00 .00000 i + i 355.00 .00000 i + i 306.00 .00000 i + i 271.00 .00000 i + i 306.00 .00000 i + i 315.00 .00000 i + i 301.00 .00000 i + i 356.00 .00000 i + i 348.00 .00000 i + i 355.00 .00000 i + i 422.00 .00000 i + i 465.00 .00000 i + i 467.00 .00000 i + i 404.00 .00000 i + i 347.00 .00000 i + i 305.00 .00000 i + i 336.00 .00000 i + i 340.00 .00000 i + i 318.00 .00000 i + i 362.00 .00000 i + i 348.00 .00000 i + i 363.00 .00000 i + i 435.00 .00000 i + i 491.00 .00000 i + i 505.00 .00000 i + i 404.00 .00000 i + i 359.00 .00000 i + i 310.00 .00000 i + i 337.00 .00000 i + i 360.00 .00000 i + i 342.00 .00000 i + i 406.00 .00000 i + i 396.00 .00000 i + i 420.00 .00000 i + i 472.00 .00000 i + i 548.00 .00000 i + i 559.00 .00000 i + i 463.00 .00000 i + i 407.00 .00000 i + i 362.00 .00000 i + i 405.00 .00000 i + i 417.00 .00000 i + i 391.00 .00000 i + i 419.00 .00000 i + i 461.00 .00000 i + i 472.00 .00000 i + i 535.00 .00000 i +i 622.00 .00000 i + i 606.00 .00000 i + i 508.00 .00000 i + i 461.00 .00000 i + i 390.00 .00000 i + i 432.00 ierr = 0 test of svpc test number 0 n = 144 + / ns = 1 + / ilog = -1 isize= -1 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- .00000 i + i 112.00 .00000 i z i 118.00 .00000 i c i 132.00 .00000 i d i 129.00 .00000 i e i 121.00 .00000 i f i 135.00 .00000 i g i 148.00 .00000 i h i 148.00 .00000 i i i 136.00 .00000 i j i 119.00 .00000 ik i 104.00 .00000 i l i 118.00 .00000 i a i 115.00 .00000 i b i 126.00 .00000 i c i 141.00 .00000 i d i 135.00 .00000 i e i 125.00 .00000 i f i 149.00 .00000 i g i 170.00 .00000 i h i 170.00 .00000 i i i 158.00 .00000 i j i 133.00 .00000 i k i 114.00 .00000 i l i 140.00 .00000 i a i 145.00 .00000 i b i 150.00 .00000 i c i 178.00 .00000 i d i 163.00 .00000 i e i 172.00 .00000 i f i 178.00 .00000 i g i 199.00 .00000 i h i 199.00 .00000 i i i 184.00 .00000 i j i 162.00 .00000 i k i 146.00 .00000 i l i 166.00 .00000 i a i 171.00 .00000 i b i 180.00 .00000 i c i 193.00 .00000 i d i 181.00 .00000 i e i 183.00 .00000 i f i 218.00 .00000 i g i 230.00 .00000 i h i 242.00 .00000 i i i 209.00 .00000 i j i 191.00 .00000 i k i 172.00 .00000 i l i 194.00 .00000 i a i 196.00 .00000 i b i 196.00 .00000 i c i 236.00 .00000 i d i 235.00 .00000 i e i 229.00 .00000 i f i 243.00 .00000 i g i 264.00 .00000 i h i 272.00 .00000 i i i 237.00 .00000 i j i 211.00 .00000 i k i 180.00 .00000 i l i 201.00 .00000 i a i 204.00 .00000 i b i 188.00 .00000 i c i 235.00 .00000 i d i 227.00 .00000 i e i 234.00 .00000 i f i 264.00 .00000 i g i 302.00 .00000 i h i 293.00 .00000 i i i 259.00 .00000 i j i 229.00 .00000 i k i 203.00 .00000 i l i 229.00 .00000 i a i 242.00 .00000 i b i 233.00 .00000 i c i 267.00 .00000 i d i 269.00 .00000 i e i 270.00 .00000 i f i 315.00 .00000 i g i 364.00 .00000 i h i 347.00 .00000 i i i 312.00 .00000 i j i 274.00 .00000 i k i 237.00 .00000 i l i 278.00 .00000 i a i 284.00 .00000 i b i 277.00 .00000 i c i 317.00 .00000 i d i 313.00 .00000 i e i 318.00 .00000 i f i 374.00 .00000 i g i 413.00 .00000 i h i 405.00 .00000 i i i 355.00 .00000 i j i 306.00 .00000 i k i 271.00 .00000 i l i 306.00 .00000 i a i 315.00 .00000 i b i 301.00 .00000 i c i 356.00 .00000 i d i 348.00 .00000 i e i 355.00 .00000 i f i 422.00 .00000 i g i 465.00 .00000 i h i 467.00 .00000 i i i 404.00 .00000 i j i 347.00 .00000 i k i 305.00 .00000 i l i 336.00 .00000 i a i 340.00 .00000 i b i 318.00 .00000 i c i 362.00 .00000 i d i 348.00 .00000 i e i 363.00 .00000 i f i 435.00 .00000 i g i 491.00 .00000 i h i 505.00 .00000 i i i 404.00 .00000 i j i 359.00 .00000 i k i 310.00 .00000 i l i 337.00 .00000 i a i 360.00 .00000 i b i 342.00 .00000 i c i 406.00 .00000 i d i 396.00 .00000 i e i 420.00 .00000 i f i 472.00 .00000 i g i 548.00 .00000 i h i 559.00 .00000 i i i 463.00 .00000 i j i 407.00 .00000 i k i 362.00 .00000 i l i 405.00 .00000 i a i 417.00 .00000 i b i 391.00 .00000 i c i 419.00 .00000 i d i 461.00 .00000 i e i 472.00 .00000 i f i 535.00 .00000 i gi 622.00 .00000 i h i 606.00 .00000 i i i 508.00 .00000 i j i 461.00 .00000 i k i 390.00 .00000 i l i 432.00 ierr = 0 test of svpmc test number 0 n = 144 + / ns = 1 + / ilog = -1 isize= -1 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- .00000 i + i 112.00 .00000 i z i 118.00 .00000 i c i 132.00 .00000 i d i 129.00 .00000 i e i 121.00 .00000 i f i 135.00 .00000 i g i 148.00 .00000 i h i 148.00 .00000 i i i 136.00 .00000 i j i 119.00 .00000 ik i 104.00 .00000 i l i 118.00 .00000 i a i 115.00 .00000 i b i 126.00 .00000 i c i 141.00 .00000 i d i 135.00 .00000 i e i 125.00 .00000 i f i 149.00 .00000 i g i 170.00 .00000 i h i 170.00 .00000 i i i 158.00 .00000 i j i 133.00 .00000 i k i 114.00 .00000 i l i 140.00 .00000 i a i 145.00 .00000 i b i 150.00 .00000 i c i 178.00 .00000 i d i 163.00 .00000 i e i 172.00 .00000 i f i 178.00 .00000 i g i 199.00 .00000 i h i 199.00 .00000 i i i 184.00 .00000 i j i 162.00 .00000 i k i 146.00 .00000 i l i 166.00 .00000 i a i 171.00 .00000 i i Missing .00000 i c i 193.00 .00000 i d i 181.00 .00000 i e i 183.00 .00000 i f i 218.00 .00000 i g i 230.00 .00000 i h i 242.00 .00000 i i i 209.00 .00000 i j i 191.00 .00000 i k i 172.00 .00000 i l i 194.00 .00000 i a i 196.00 .00000 i b i 196.00 .00000 i c i 236.00 .00000 i d i 235.00 .00000 i e i 229.00 .00000 i f i 243.00 .00000 i g i 264.00 .00000 i h i 272.00 .00000 i i i 237.00 .00000 i j i 211.00 .00000 i i Missing .00000 i l i 201.00 .00000 i a i 204.00 .00000 i b i 188.00 .00000 i c i 235.00 .00000 i d i 227.00 .00000 i e i 234.00 .00000 i f i 264.00 .00000 i g i 302.00 .00000 i h i 293.00 .00000 i i i 259.00 .00000 i j i 229.00 .00000 i k i 203.00 .00000 i l i 229.00 .00000 i a i 242.00 .00000 i b i 233.00 .00000 i c i 267.00 .00000 i d i 269.00 .00000 i e i 270.00 .00000 i f i 315.00 .00000 i g i 364.00 .00000 i h i 347.00 .00000 i i i 312.00 .00000 i j i 274.00 .00000 i k i 237.00 .00000 i l i 278.00 .00000 i a i 284.00 .00000 i b i 277.00 .00000 i c i 317.00 .00000 i d i 313.00 .00000 i e i 318.00 .00000 i f i 374.00 .00000 i g i 413.00 .00000 i h i 405.00 .00000 i i i 355.00 .00000 i j i 306.00 .00000 i k i 271.00 .00000 i l i 306.00 .00000 i a i 315.00 .00000 i b i 301.00 .00000 i c i 356.00 .00000 i d i 348.00 .00000 i e i 355.00 .00000 i f i 422.00 .00000 i g i 465.00 .00000 i h i 467.00 .00000 i i i 404.00 .00000 i j i 347.00 .00000 i k i 305.00 .00000 i l i 336.00 .00000 i a i 340.00 .00000 i b i 318.00 .00000 i c i 362.00 .00000 i d i 348.00 .00000 i e i 363.00 .00000 i f i 435.00 .00000 i g i 491.00 .00000 i h i 505.00 .00000 i i i 404.00 .00000 i j i 359.00 .00000 i k i 310.00 .00000 i l i 337.00 .00000 i a i 360.00 .00000 i b i 342.00 .00000 i c i 406.00 .00000 i d i 396.00 .00000 i e i 420.00 .00000 i f i 472.00 .00000 i g i 548.00 .00000 i h i 559.00 .00000 i i i 463.00 .00000 i j i 407.00 .00000 i k i 362.00 .00000 i l i 405.00 .00000 i a i 417.00 .00000 i b i 391.00 .00000 i c i 419.00 .00000 i d i 461.00 .00000 i e i 472.00 .00000 i f i 535.00 .00000 i gi 622.00 .00000 i h i 606.00 .00000 i i i 508.00 .00000 i j i 461.00 .00000 i k i 390.00 .00000 i l i 432.00 ierr = 0 test of mvpc test number 0 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = -1 isize= -1 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- .00000 i 2 c d ef g h i j k l i .00000 i ab c df e g h i j k l i .00000 i a b c d 2 g h ij k l i .00000 i ab c d fe g h 2 k l i .00000 i ab c d ef g h i j k l i .00000 i a b c d e f g h i j k l i .00000 i a b c d e f g h i j k li .00000 i a b c d e f g h i j k l i .00000 i a b c d e f g h 2 k l i .00000 i a b c d e f g h i j k l i .00000 ia b c d e f g h ij k l i .00000 i a b c d e f g h 2 k l i ierr = 0 test of mvpmc test number 0 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = -1 isize= -1 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- .00000 i 2 c d ef g h i j k l i .00000 i ab c f e g h i j k l i Missing .00000 i a b c d 2 g h ij k l i .00000 i ab c d fe g h 2 k l i .00000 i ab c d ef g h i j k l i .00000 i a b c d e f g h i j k l i .00000 i a b c d e f g h i j k li .00000 i a b c d e f g h i j k l i .00000 i a b c d e f g h 2 k l i .00000 i a b c d e f g h i j k l i .00000 ia b c d f g h ij k l i Missing .00000 i a b c d e f g h 2 k l i ierr = 0 test of vpl test number 1 n = 144 + / ns = 1 + / ilog = 0 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i+ i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i + i 180.00 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i + i 180.00 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i +i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of vpml test number 1 n = 144 + / ns = 1 + / ilog = 0 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i+ i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i i Missing 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i i Missing 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i +i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of svpl test number 1 n = 144 + / ns = 1 + / ilog = 0 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 ik i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i b i 180.00 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i k i 180.00 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i gi 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of svpml test number 1 n = 144 + / ns = 1 + / ilog = 0 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 ik i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i i Missing 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i i Missing 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i gi 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of mvpl test number 1 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 0 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i 2 c d ef g h i j k l i 2.0000 i ab c df e g h i j k l i 3.0000 i a b c d 2 g h ij k l i 4.0000 i ab c d fe g h 2 k l i 5.0000 i ab c d ef g h i j k l i 6.0000 i a b c d e f g h i j k l i 7.0000 i a b c d e f g h i j k li 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 ia b c d e f g h ij k l i 12.000 i a b c d e f g h 2 k l i ierr = 0 test of mvpml test number 1 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 0 starpac 2.08s (03/15/90) 104.0000 155.8000 207.6000 259.4000 311.2000 363.0000 414.8000 466.6000 518.4000 570.2000 622.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i 2 c d ef g h i j k l i 2.0000 i ab c f e g h i j k l i Missing 3.0000 i a b c d 2 g h ij k l i 4.0000 i ab c d fe g h 2 k l i 5.0000 i ab c d ef g h i j k l i 6.0000 i a b c d e f g h i j k l i 7.0000 i a b c d e f g h i j k li 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 ia b c d f g h ij k l i Missing 12.000 i a b c d e f g h 2 k l i ierr = 0 test of vpc test number 1 n = 144 + / ns = 1 + / ilog = 0 isize= 0 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = 16.0000 starpac 2.08s (03/15/90) 100.0000 160.0000 220.0000 280.0000 340.0000 400.0000 460.0000 520.0000 580.0000 640.0000 700.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 20.000 i + i 118.00 36.000 i + i 132.00 52.000 i + i 129.00 68.000 i + i 121.00 84.000 i + i 135.00 100.00 i + i 148.00 116.00 i + i 148.00 132.00 i + i 136.00 148.00 i + i 119.00 164.00 i + i 104.00 180.00 i + i 118.00 196.00 i + i 115.00 212.00 i + i 126.00 228.00 i + i 141.00 244.00 i + i 135.00 260.00 i + i 125.00 276.00 i + i 149.00 292.00 i + i 170.00 308.00 i + i 170.00 324.00 i + i 158.00 340.00 i + i 133.00 356.00 i + i 114.00 372.00 i + i 140.00 388.00 i + i 145.00 404.00 i + i 150.00 420.00 i + i 178.00 436.00 i + i 163.00 452.00 i + i 172.00 468.00 i + i 178.00 484.00 i + i 199.00 500.00 i + i 199.00 516.00 i + i 184.00 532.00 i + i 162.00 548.00 i + i 146.00 564.00 i + i 166.00 580.00 i + i 171.00 596.00 i + i 180.00 612.00 i + i 193.00 628.00 i + i 181.00 644.00 i + i 183.00 660.00 i + i 218.00 676.00 i + i 230.00 692.00 i + i 242.00 708.00 i + i 209.00 724.00 i + i 191.00 740.00 i + i 172.00 756.00 i + i 194.00 772.00 i + i 196.00 788.00 i + i 196.00 804.00 i + i 236.00 820.00 i + i 235.00 836.00 i + i 229.00 852.00 i + i 243.00 868.00 i + i 264.00 884.00 i + i 272.00 900.00 i + i 237.00 916.00 i + i 211.00 932.00 i + i 180.00 948.00 i + i 201.00 964.00 i + i 204.00 980.00 i + i 188.00 996.00 i + i 235.00 1012.0 i + i 227.00 1028.0 i + i 234.00 1044.0 i + i 264.00 1060.0 i + i 302.00 1076.0 i + i 293.00 1092.0 i + i 259.00 1108.0 i + i 229.00 1124.0 i + i 203.00 1140.0 i + i 229.00 1156.0 i + i 242.00 1172.0 i + i 233.00 1188.0 i + i 267.00 1204.0 i + i 269.00 1220.0 i + i 270.00 1236.0 i + i 315.00 1252.0 i + i 364.00 1268.0 i + i 347.00 1284.0 i + i 312.00 1300.0 i + i 274.00 1316.0 i + i 237.00 1332.0 i + i 278.00 1348.0 i + i 284.00 1364.0 i + i 277.00 1380.0 i + i 317.00 1396.0 i + i 313.00 1412.0 i + i 318.00 1428.0 i + i 374.00 1444.0 i + i 413.00 1460.0 i + i 405.00 1476.0 i + i 355.00 1492.0 i + i 306.00 1508.0 i + i 271.00 1524.0 i + i 306.00 1540.0 i + i 315.00 1556.0 i + i 301.00 1572.0 i + i 356.00 1588.0 i + i 348.00 1604.0 i + i 355.00 1620.0 i + i 422.00 1636.0 i + i 465.00 1652.0 i + i 467.00 1668.0 i + i 404.00 1684.0 i + i 347.00 1700.0 i + i 305.00 1716.0 i + i 336.00 1732.0 i + i 340.00 1748.0 i + i 318.00 1764.0 i + i 362.00 1780.0 i + i 348.00 1796.0 i + i 363.00 1812.0 i + i 435.00 1828.0 i + i 491.00 1844.0 i + i 505.00 1860.0 i + i 404.00 1876.0 i + i 359.00 1892.0 i + i 310.00 1908.0 i + i 337.00 1924.0 i + i 360.00 1940.0 i + i 342.00 1956.0 i + i 406.00 1972.0 i + i 396.00 1988.0 i + i 420.00 2004.0 i + i 472.00 2020.0 i + i 548.00 2036.0 i + i 559.00 2052.0 i + i 463.00 2068.0 i + i 407.00 2084.0 i + i 362.00 2100.0 i + i 405.00 2116.0 i + i 417.00 2132.0 i + i 391.00 2148.0 i + i 419.00 2164.0 i + i 461.00 2180.0 i + i 472.00 2196.0 i + i 535.00 2212.0 i + i 622.00 2228.0 i + i 606.00 2244.0 i + i 508.00 2260.0 i + i 461.00 2276.0 i + i 390.00 2292.0 i + i 432.00 ierr = 0 test of vpmc test number 1 n = 144 + / ns = 1 + / ilog = 0 isize= 0 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = 16.0000 starpac 2.08s (03/15/90) 100.0000 160.0000 220.0000 280.0000 340.0000 400.0000 460.0000 520.0000 580.0000 640.0000 700.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 20.000 i + i 118.00 36.000 i + i 132.00 52.000 i + i 129.00 68.000 i + i 121.00 84.000 i + i 135.00 100.00 i + i 148.00 116.00 i + i 148.00 132.00 i + i 136.00 148.00 i + i 119.00 164.00 i + i 104.00 180.00 i + i 118.00 196.00 i + i 115.00 212.00 i + i 126.00 228.00 i + i 141.00 244.00 i + i 135.00 260.00 i + i 125.00 276.00 i + i 149.00 292.00 i + i 170.00 308.00 i + i 170.00 324.00 i + i 158.00 340.00 i + i 133.00 356.00 i + i 114.00 372.00 i + i 140.00 388.00 i + i 145.00 404.00 i + i 150.00 420.00 i + i 178.00 436.00 i + i 163.00 452.00 i + i 172.00 468.00 i + i 178.00 484.00 i + i 199.00 500.00 i + i 199.00 516.00 i + i 184.00 532.00 i + i 162.00 548.00 i + i 146.00 564.00 i + i 166.00 580.00 i + i 171.00 596.00 i i Missing 612.00 i + i 193.00 628.00 i + i 181.00 644.00 i + i 183.00 660.00 i + i 218.00 676.00 i + i 230.00 692.00 i + i 242.00 708.00 i + i 209.00 724.00 i + i 191.00 740.00 i + i 172.00 756.00 i + i 194.00 772.00 i + i 196.00 788.00 i + i 196.00 804.00 i + i 236.00 820.00 i + i 235.00 836.00 i + i 229.00 852.00 i + i 243.00 868.00 i + i 264.00 884.00 i + i 272.00 900.00 i + i 237.00 916.00 i + i 211.00 932.00 i i Missing 948.00 i + i 201.00 964.00 i + i 204.00 980.00 i + i 188.00 996.00 i + i 235.00 1012.0 i + i 227.00 1028.0 i + i 234.00 1044.0 i + i 264.00 1060.0 i + i 302.00 1076.0 i + i 293.00 1092.0 i + i 259.00 1108.0 i + i 229.00 1124.0 i + i 203.00 1140.0 i + i 229.00 1156.0 i + i 242.00 1172.0 i + i 233.00 1188.0 i + i 267.00 1204.0 i + i 269.00 1220.0 i + i 270.00 1236.0 i + i 315.00 1252.0 i + i 364.00 1268.0 i + i 347.00 1284.0 i + i 312.00 1300.0 i + i 274.00 1316.0 i + i 237.00 1332.0 i + i 278.00 1348.0 i + i 284.00 1364.0 i + i 277.00 1380.0 i + i 317.00 1396.0 i + i 313.00 1412.0 i + i 318.00 1428.0 i + i 374.00 1444.0 i + i 413.00 1460.0 i + i 405.00 1476.0 i + i 355.00 1492.0 i + i 306.00 1508.0 i + i 271.00 1524.0 i + i 306.00 1540.0 i + i 315.00 1556.0 i + i 301.00 1572.0 i + i 356.00 1588.0 i + i 348.00 1604.0 i + i 355.00 1620.0 i + i 422.00 1636.0 i + i 465.00 1652.0 i + i 467.00 1668.0 i + i 404.00 1684.0 i + i 347.00 1700.0 i + i 305.00 1716.0 i + i 336.00 1732.0 i + i 340.00 1748.0 i + i 318.00 1764.0 i + i 362.00 1780.0 i + i 348.00 1796.0 i + i 363.00 1812.0 i + i 435.00 1828.0 i + i 491.00 1844.0 i + i 505.00 1860.0 i + i 404.00 1876.0 i + i 359.00 1892.0 i + i 310.00 1908.0 i + i 337.00 1924.0 i + i 360.00 1940.0 i + i 342.00 1956.0 i + i 406.00 1972.0 i + i 396.00 1988.0 i + i 420.00 2004.0 i + i 472.00 2020.0 i + i 548.00 2036.0 i + i 559.00 2052.0 i + i 463.00 2068.0 i + i 407.00 2084.0 i + i 362.00 2100.0 i + i 405.00 2116.0 i + i 417.00 2132.0 i + i 391.00 2148.0 i + i 419.00 2164.0 i + i 461.00 2180.0 i + i 472.00 2196.0 i + i 535.00 2212.0 i + i 622.00 2228.0 i + i 606.00 2244.0 i + i 508.00 2260.0 i + i 461.00 2276.0 i + i 390.00 2292.0 i + i 432.00 ierr = 0 test of svpc test number 1 n = 144 + / ns = 1 + / ilog = 0 isize= 0 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = 16.0000 starpac 2.08s (03/15/90) 100.0000 160.0000 220.0000 280.0000 340.0000 400.0000 460.0000 520.0000 580.0000 640.0000 700.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 20.000 i z i 118.00 36.000 i c i 132.00 52.000 i d i 129.00 68.000 i e i 121.00 84.000 i f i 135.00 100.00 i g i 148.00 116.00 i h i 148.00 132.00 i i i 136.00 148.00 i j i 119.00 164.00 i k i 104.00 180.00 i l i 118.00 196.00 i a i 115.00 212.00 i b i 126.00 228.00 i c i 141.00 244.00 i d i 135.00 260.00 i e i 125.00 276.00 i f i 149.00 292.00 i g i 170.00 308.00 i h i 170.00 324.00 i i i 158.00 340.00 i j i 133.00 356.00 i k i 114.00 372.00 i l i 140.00 388.00 i a i 145.00 404.00 i b i 150.00 420.00 i c i 178.00 436.00 i d i 163.00 452.00 i e i 172.00 468.00 i f i 178.00 484.00 i g i 199.00 500.00 i h i 199.00 516.00 i i i 184.00 532.00 i j i 162.00 548.00 i k i 146.00 564.00 i l i 166.00 580.00 i a i 171.00 596.00 i b i 180.00 612.00 i c i 193.00 628.00 i d i 181.00 644.00 i e i 183.00 660.00 i f i 218.00 676.00 i g i 230.00 692.00 i h i 242.00 708.00 i i i 209.00 724.00 i j i 191.00 740.00 i k i 172.00 756.00 i l i 194.00 772.00 i a i 196.00 788.00 i b i 196.00 804.00 i c i 236.00 820.00 i d i 235.00 836.00 i e i 229.00 852.00 i f i 243.00 868.00 i g i 264.00 884.00 i h i 272.00 900.00 i i i 237.00 916.00 i j i 211.00 932.00 i k i 180.00 948.00 i l i 201.00 964.00 i a i 204.00 980.00 i b i 188.00 996.00 i c i 235.00 1012.0 i d i 227.00 1028.0 i e i 234.00 1044.0 i f i 264.00 1060.0 i g i 302.00 1076.0 i h i 293.00 1092.0 i i i 259.00 1108.0 i j i 229.00 1124.0 i k i 203.00 1140.0 i l i 229.00 1156.0 i a i 242.00 1172.0 i b i 233.00 1188.0 i c i 267.00 1204.0 i d i 269.00 1220.0 i e i 270.00 1236.0 i f i 315.00 1252.0 i g i 364.00 1268.0 i h i 347.00 1284.0 i i i 312.00 1300.0 i j i 274.00 1316.0 i k i 237.00 1332.0 i l i 278.00 1348.0 i a i 284.00 1364.0 i b i 277.00 1380.0 i c i 317.00 1396.0 i d i 313.00 1412.0 i e i 318.00 1428.0 i f i 374.00 1444.0 i g i 413.00 1460.0 i h i 405.00 1476.0 i i i 355.00 1492.0 i j i 306.00 1508.0 i k i 271.00 1524.0 i l i 306.00 1540.0 i a i 315.00 1556.0 i b i 301.00 1572.0 i c i 356.00 1588.0 i d i 348.00 1604.0 i e i 355.00 1620.0 i f i 422.00 1636.0 i g i 465.00 1652.0 i h i 467.00 1668.0 i i i 404.00 1684.0 i j i 347.00 1700.0 i k i 305.00 1716.0 i l i 336.00 1732.0 i a i 340.00 1748.0 i b i 318.00 1764.0 i c i 362.00 1780.0 i d i 348.00 1796.0 i e i 363.00 1812.0 i f i 435.00 1828.0 i g i 491.00 1844.0 i h i 505.00 1860.0 i i i 404.00 1876.0 i j i 359.00 1892.0 i k i 310.00 1908.0 i l i 337.00 1924.0 i a i 360.00 1940.0 i b i 342.00 1956.0 i c i 406.00 1972.0 i d i 396.00 1988.0 i e i 420.00 2004.0 i f i 472.00 2020.0 i g i 548.00 2036.0 i h i 559.00 2052.0 i i i 463.00 2068.0 i j i 407.00 2084.0 i k i 362.00 2100.0 i l i 405.00 2116.0 i a i 417.00 2132.0 i b i 391.00 2148.0 i c i 419.00 2164.0 i d i 461.00 2180.0 i e i 472.00 2196.0 i f i 535.00 2212.0 i g i 622.00 2228.0 i h i 606.00 2244.0 i i i 508.00 2260.0 i j i 461.00 2276.0 i k i 390.00 2292.0 i l i 432.00 ierr = 0 test of svpmc test number 1 n = 144 + / ns = 1 + / ilog = 0 isize= 0 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = 16.0000 starpac 2.08s (03/15/90) 100.0000 160.0000 220.0000 280.0000 340.0000 400.0000 460.0000 520.0000 580.0000 640.0000 700.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 20.000 i z i 118.00 36.000 i c i 132.00 52.000 i d i 129.00 68.000 i e i 121.00 84.000 i f i 135.00 100.00 i g i 148.00 116.00 i h i 148.00 132.00 i i i 136.00 148.00 i j i 119.00 164.00 i k i 104.00 180.00 i l i 118.00 196.00 i a i 115.00 212.00 i b i 126.00 228.00 i c i 141.00 244.00 i d i 135.00 260.00 i e i 125.00 276.00 i f i 149.00 292.00 i g i 170.00 308.00 i h i 170.00 324.00 i i i 158.00 340.00 i j i 133.00 356.00 i k i 114.00 372.00 i l i 140.00 388.00 i a i 145.00 404.00 i b i 150.00 420.00 i c i 178.00 436.00 i d i 163.00 452.00 i e i 172.00 468.00 i f i 178.00 484.00 i g i 199.00 500.00 i h i 199.00 516.00 i i i 184.00 532.00 i j i 162.00 548.00 i k i 146.00 564.00 i l i 166.00 580.00 i a i 171.00 596.00 i i Missing 612.00 i c i 193.00 628.00 i d i 181.00 644.00 i e i 183.00 660.00 i f i 218.00 676.00 i g i 230.00 692.00 i h i 242.00 708.00 i i i 209.00 724.00 i j i 191.00 740.00 i k i 172.00 756.00 i l i 194.00 772.00 i a i 196.00 788.00 i b i 196.00 804.00 i c i 236.00 820.00 i d i 235.00 836.00 i e i 229.00 852.00 i f i 243.00 868.00 i g i 264.00 884.00 i h i 272.00 900.00 i i i 237.00 916.00 i j i 211.00 932.00 i i Missing 948.00 i l i 201.00 964.00 i a i 204.00 980.00 i b i 188.00 996.00 i c i 235.00 1012.0 i d i 227.00 1028.0 i e i 234.00 1044.0 i f i 264.00 1060.0 i g i 302.00 1076.0 i h i 293.00 1092.0 i i i 259.00 1108.0 i j i 229.00 1124.0 i k i 203.00 1140.0 i l i 229.00 1156.0 i a i 242.00 1172.0 i b i 233.00 1188.0 i c i 267.00 1204.0 i d i 269.00 1220.0 i e i 270.00 1236.0 i f i 315.00 1252.0 i g i 364.00 1268.0 i h i 347.00 1284.0 i i i 312.00 1300.0 i j i 274.00 1316.0 i k i 237.00 1332.0 i l i 278.00 1348.0 i a i 284.00 1364.0 i b i 277.00 1380.0 i c i 317.00 1396.0 i d i 313.00 1412.0 i e i 318.00 1428.0 i f i 374.00 1444.0 i g i 413.00 1460.0 i h i 405.00 1476.0 i i i 355.00 1492.0 i j i 306.00 1508.0 i k i 271.00 1524.0 i l i 306.00 1540.0 i a i 315.00 1556.0 i b i 301.00 1572.0 i c i 356.00 1588.0 i d i 348.00 1604.0 i e i 355.00 1620.0 i f i 422.00 1636.0 i g i 465.00 1652.0 i h i 467.00 1668.0 i i i 404.00 1684.0 i j i 347.00 1700.0 i k i 305.00 1716.0 i l i 336.00 1732.0 i a i 340.00 1748.0 i b i 318.00 1764.0 i c i 362.00 1780.0 i d i 348.00 1796.0 i e i 363.00 1812.0 i f i 435.00 1828.0 i g i 491.00 1844.0 i h i 505.00 1860.0 i i i 404.00 1876.0 i j i 359.00 1892.0 i k i 310.00 1908.0 i l i 337.00 1924.0 i a i 360.00 1940.0 i b i 342.00 1956.0 i c i 406.00 1972.0 i d i 396.00 1988.0 i e i 420.00 2004.0 i f i 472.00 2020.0 i g i 548.00 2036.0 i h i 559.00 2052.0 i i i 463.00 2068.0 i j i 407.00 2084.0 i k i 362.00 2100.0 i l i 405.00 2116.0 i a i 417.00 2132.0 i b i 391.00 2148.0 i c i 419.00 2164.0 i d i 461.00 2180.0 i e i 472.00 2196.0 i f i 535.00 2212.0 i g i 622.00 2228.0 i h i 606.00 2244.0 i i i 508.00 2260.0 i j i 461.00 2276.0 i k i 390.00 2292.0 i l i 432.00 ierr = 0 test of mvpc test number 1 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 0 isize= 0 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = 16.0000 starpac 2.08s (03/15/90) 100.0000 160.0000 220.0000 280.0000 340.0000 400.0000 460.0000 520.0000 580.0000 640.0000 700.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i ab c d ef g h i j k l i 20.000 i ab c d fe g h i j k l i 36.000 i a b c d 2 g h ij k l i 52.000 i ab c d f e g h 2 k l i 68.000 i 2 c d 2 g h ij k l i 84.000 i a b c d e f g h i j k l i 100.00 i a b c d e f g h i j k l i 116.00 i a b c d e f g h i j k l i 132.00 i a b c d e f g h 2 k l i 148.00 i a b c d e f g h i j k l i 164.00 i ab c de f g h ij k l i 180.00 i a b c de f g h ij k l i ierr = 0 test of mvpmc test number 1 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 0 isize= 0 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = 16.0000 starpac 2.08s (03/15/90) 100.0000 160.0000 220.0000 280.0000 340.0000 400.0000 460.0000 520.0000 580.0000 640.0000 700.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i ab c d ef g h i j k l i 20.000 i ab c fe g h i j k l i Missing 36.000 i a b c d 2 g h ij k l i 52.000 i ab c d f e g h 2 k l i 68.000 i 2 c d 2 g h ij k l i 84.000 i a b c d e f g h i j k l i 100.00 i a b c d e f g h i j k l i 116.00 i a b c d e f g h i j k l i 132.00 i a b c d e f g h 2 k l i 148.00 i a b c d e f g h i j k l i 164.00 i ab c d f g h ij k l i Missing 180.00 i a b c de f g h ij k l i ierr = 0 test of vpl test number 2 n = 144 + / ns = 1 + / ilog = 2 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 600.0000 800.0000 1000.0000 -i-----------------------------i-----------------------------i-----------------i-----------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i + i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i + i 180.00 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i + i 180.00 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i + i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of vpml test number 2 n = 144 + / ns = 1 + / ilog = 2 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 600.0000 800.0000 1000.0000 -i-----------------------------i-----------------------------i-----------------i-----------i---------i- 1.0000 i + i 112.00 2.0000 i + i 118.00 3.0000 i + i 132.00 4.0000 i + i 129.00 5.0000 i + i 121.00 6.0000 i + i 135.00 7.0000 i + i 148.00 8.0000 i + i 148.00 9.0000 i + i 136.00 10.000 i + i 119.00 11.000 i + i 104.00 12.000 i + i 118.00 13.000 i + i 115.00 14.000 i + i 126.00 15.000 i + i 141.00 16.000 i + i 135.00 17.000 i + i 125.00 18.000 i + i 149.00 19.000 i + i 170.00 20.000 i + i 170.00 21.000 i + i 158.00 22.000 i + i 133.00 23.000 i + i 114.00 24.000 i + i 140.00 25.000 i + i 145.00 26.000 i + i 150.00 27.000 i + i 178.00 28.000 i + i 163.00 29.000 i + i 172.00 30.000 i + i 178.00 31.000 i + i 199.00 32.000 i + i 199.00 33.000 i + i 184.00 34.000 i + i 162.00 35.000 i + i 146.00 36.000 i + i 166.00 37.000 i + i 171.00 38.000 i i Missing 39.000 i + i 193.00 40.000 i + i 181.00 41.000 i + i 183.00 42.000 i + i 218.00 43.000 i + i 230.00 44.000 i + i 242.00 45.000 i + i 209.00 46.000 i + i 191.00 47.000 i + i 172.00 48.000 i + i 194.00 49.000 i + i 196.00 50.000 i + i 196.00 51.000 i + i 236.00 52.000 i + i 235.00 53.000 i + i 229.00 54.000 i + i 243.00 55.000 i + i 264.00 56.000 i + i 272.00 57.000 i + i 237.00 58.000 i + i 211.00 59.000 i i Missing 60.000 i + i 201.00 61.000 i + i 204.00 62.000 i + i 188.00 63.000 i + i 235.00 64.000 i + i 227.00 65.000 i + i 234.00 66.000 i + i 264.00 67.000 i + i 302.00 68.000 i + i 293.00 69.000 i + i 259.00 70.000 i + i 229.00 71.000 i + i 203.00 72.000 i + i 229.00 73.000 i + i 242.00 74.000 i + i 233.00 75.000 i + i 267.00 76.000 i + i 269.00 77.000 i + i 270.00 78.000 i + i 315.00 79.000 i + i 364.00 80.000 i + i 347.00 81.000 i + i 312.00 82.000 i + i 274.00 83.000 i + i 237.00 84.000 i + i 278.00 85.000 i + i 284.00 86.000 i + i 277.00 87.000 i + i 317.00 88.000 i + i 313.00 89.000 i + i 318.00 90.000 i + i 374.00 91.000 i + i 413.00 92.000 i + i 405.00 93.000 i + i 355.00 94.000 i + i 306.00 95.000 i + i 271.00 96.000 i + i 306.00 97.000 i + i 315.00 98.000 i + i 301.00 99.000 i + i 356.00 100.00 i + i 348.00 101.00 i + i 355.00 102.00 i + i 422.00 103.00 i + i 465.00 104.00 i + i 467.00 105.00 i + i 404.00 106.00 i + i 347.00 107.00 i + i 305.00 108.00 i + i 336.00 109.00 i + i 340.00 110.00 i + i 318.00 111.00 i + i 362.00 112.00 i + i 348.00 113.00 i + i 363.00 114.00 i + i 435.00 115.00 i + i 491.00 116.00 i + i 505.00 117.00 i + i 404.00 118.00 i + i 359.00 119.00 i + i 310.00 120.00 i + i 337.00 121.00 i + i 360.00 122.00 i + i 342.00 123.00 i + i 406.00 124.00 i + i 396.00 125.00 i + i 420.00 126.00 i + i 472.00 127.00 i + i 548.00 128.00 i + i 559.00 129.00 i + i 463.00 130.00 i + i 407.00 131.00 i + i 362.00 132.00 i + i 405.00 133.00 i + i 417.00 134.00 i + i 391.00 135.00 i + i 419.00 136.00 i + i 461.00 137.00 i + i 472.00 138.00 i + i 535.00 139.00 i + i 622.00 140.00 i + i 606.00 141.00 i + i 508.00 142.00 i + i 461.00 143.00 i + i 390.00 144.00 i + i 432.00 ierr = 0 test of svpl test number 2 n = 144 + / ns = 1 + / ilog = 2 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 600.0000 800.0000 1000.0000 -i-----------------------------i-----------------------------i-----------------i-----------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 i k i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i b i 180.00 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i k i 180.00 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i g i 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of svpml test number 2 n = 144 + / ns = 1 + / ilog = 2 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 600.0000 800.0000 1000.0000 -i-----------------------------i-----------------------------i-----------------i-----------i---------i- 1.0000 i + i 112.00 2.0000 i z i 118.00 3.0000 i c i 132.00 4.0000 i d i 129.00 5.0000 i e i 121.00 6.0000 i f i 135.00 7.0000 i g i 148.00 8.0000 i h i 148.00 9.0000 i i i 136.00 10.000 i j i 119.00 11.000 i k i 104.00 12.000 i l i 118.00 13.000 i a i 115.00 14.000 i b i 126.00 15.000 i c i 141.00 16.000 i d i 135.00 17.000 i e i 125.00 18.000 i f i 149.00 19.000 i g i 170.00 20.000 i h i 170.00 21.000 i i i 158.00 22.000 i j i 133.00 23.000 i k i 114.00 24.000 i l i 140.00 25.000 i a i 145.00 26.000 i b i 150.00 27.000 i c i 178.00 28.000 i d i 163.00 29.000 i e i 172.00 30.000 i f i 178.00 31.000 i g i 199.00 32.000 i h i 199.00 33.000 i i i 184.00 34.000 i j i 162.00 35.000 i k i 146.00 36.000 i l i 166.00 37.000 i a i 171.00 38.000 i i Missing 39.000 i c i 193.00 40.000 i d i 181.00 41.000 i e i 183.00 42.000 i f i 218.00 43.000 i g i 230.00 44.000 i h i 242.00 45.000 i i i 209.00 46.000 i j i 191.00 47.000 i k i 172.00 48.000 i l i 194.00 49.000 i a i 196.00 50.000 i b i 196.00 51.000 i c i 236.00 52.000 i d i 235.00 53.000 i e i 229.00 54.000 i f i 243.00 55.000 i g i 264.00 56.000 i h i 272.00 57.000 i i i 237.00 58.000 i j i 211.00 59.000 i i Missing 60.000 i l i 201.00 61.000 i a i 204.00 62.000 i b i 188.00 63.000 i c i 235.00 64.000 i d i 227.00 65.000 i e i 234.00 66.000 i f i 264.00 67.000 i g i 302.00 68.000 i h i 293.00 69.000 i i i 259.00 70.000 i j i 229.00 71.000 i k i 203.00 72.000 i l i 229.00 73.000 i a i 242.00 74.000 i b i 233.00 75.000 i c i 267.00 76.000 i d i 269.00 77.000 i e i 270.00 78.000 i f i 315.00 79.000 i g i 364.00 80.000 i h i 347.00 81.000 i i i 312.00 82.000 i j i 274.00 83.000 i k i 237.00 84.000 i l i 278.00 85.000 i a i 284.00 86.000 i b i 277.00 87.000 i c i 317.00 88.000 i d i 313.00 89.000 i e i 318.00 90.000 i f i 374.00 91.000 i g i 413.00 92.000 i h i 405.00 93.000 i i i 355.00 94.000 i j i 306.00 95.000 i k i 271.00 96.000 i l i 306.00 97.000 i a i 315.00 98.000 i b i 301.00 99.000 i c i 356.00 100.00 i d i 348.00 101.00 i e i 355.00 102.00 i f i 422.00 103.00 i g i 465.00 104.00 i h i 467.00 105.00 i i i 404.00 106.00 i j i 347.00 107.00 i k i 305.00 108.00 i l i 336.00 109.00 i a i 340.00 110.00 i b i 318.00 111.00 i c i 362.00 112.00 i d i 348.00 113.00 i e i 363.00 114.00 i f i 435.00 115.00 i g i 491.00 116.00 i h i 505.00 117.00 i i i 404.00 118.00 i j i 359.00 119.00 i k i 310.00 120.00 i l i 337.00 121.00 i a i 360.00 122.00 i b i 342.00 123.00 i c i 406.00 124.00 i d i 396.00 125.00 i e i 420.00 126.00 i f i 472.00 127.00 i g i 548.00 128.00 i h i 559.00 129.00 i i i 463.00 130.00 i j i 407.00 131.00 i k i 362.00 132.00 i l i 405.00 133.00 i a i 417.00 134.00 i b i 391.00 135.00 i c i 419.00 136.00 i d i 461.00 137.00 i e i 472.00 138.00 i f i 535.00 139.00 i g i 622.00 140.00 i h i 606.00 141.00 i i i 508.00 142.00 i j i 461.00 143.00 i k i 390.00 144.00 i l i 432.00 ierr = 0 test of mvpl test number 2 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 2 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 600.0000 800.0000 1000.0000 -i-----------------------------i-----------------------------i-----------------i-----------i---------i- 1.0000 i ab c d e f g h i j k l i 2.0000 i a b c df e g h i j k l i 3.0000 i a b c d 2 g h ij kl i 4.0000 i a b c d fe g h 2 k l i 5.0000 i a b c d ef g h ij k l i 6.0000 i a b c d e f g h ij k l i 7.0000 i a b c d e f g h i j k l i 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 i a b c d e f g h ij k l i 12.000 i a b c de f g h 2 k l i ierr = 0 test of mvpml test number 2 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 2 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 600.0000 800.0000 1000.0000 -i-----------------------------i-----------------------------i-----------------i-----------i---------i- 1.0000 i ab c d e f g h i j k l i 2.0000 i a b c f e g h i j k l i Missing 3.0000 i a b c d 2 g h ij kl i 4.0000 i a b c d fe g h 2 k l i 5.0000 i a b c d ef g h ij k l i 6.0000 i a b c d e f g h ij k l i 7.0000 i a b c d e f g h i j k l i 8.0000 i a b c d e f g h i j k l i 9.0000 i a b c d e f g h 2 k l i 10.000 i a b c d e f g h i j k l i 11.000 i a b c d f g h ij k l i Missing 12.000 i a b c de f g h 2 k l i ierr = 0 test of vpc test number 2 n = 144 + / ns = 1 + / ilog = 2 isize= 2 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i + i 112.00 3.0000 i + i 118.00 2.0000 i + i 132.00 1.0000 i + i 129.00 .00000 i + i 121.00 -1.0000 i + i 135.00 -2.0000 i + i 148.00 -3.0000 i + i 148.00 -4.0000 i + i 136.00 -5.0000 i + i 119.00 -6.0000 i + i 104.00 -7.0000 i + i 118.00 -8.0000 i + i 115.00 -9.0000 i + i 126.00 -10.000 i + i 141.00 -11.000 i + i 135.00 -12.000 i + i 125.00 -13.000 i + i 149.00 -14.000 i + i 170.00 -15.000 i + i 170.00 -16.000 i + i 158.00 -17.000 i + i 133.00 -18.000 i + i 114.00 -19.000 i + i 140.00 -20.000 i + i 145.00 -21.000 i + i 150.00 -22.000 i + i 178.00 -23.000 i + i 163.00 -24.000 i + i 172.00 -25.000 i + i 178.00 -26.000 i + i 199.00 -27.000 i + i 199.00 -28.000 i + i 184.00 -29.000 i + i 162.00 -30.000 i + i 146.00 -31.000 i + i 166.00 -32.000 i + i 171.00 -33.000 i + i 180.00 -34.000 i + i 193.00 -35.000 i + i 181.00 -36.000 i + i 183.00 -37.000 i + i 218.00 -38.000 i + i 230.00 -39.000 i + i 242.00 -40.000 i + i 209.00 -41.000 i + i 191.00 -42.000 i + i 172.00 -43.000 i + i 194.00 -44.000 i + i 196.00 -45.000 i + i 196.00 -46.000 i + i 236.00 -47.000 i + i 235.00 -48.000 i + i 229.00 -49.000 i + i 243.00 -50.000 i + i 264.00 -51.000 i + i 272.00 -52.000 i + i 237.00 -53.000 i + i 211.00 -54.000 i + i 180.00 -55.000 i + i 201.00 -56.000 i + i 204.00 -57.000 i + i 188.00 -58.000 i + i 235.00 -59.000 i + i 227.00 -60.000 i + i 234.00 -61.000 i + i 264.00 -62.000 i + i 302.00 -63.000 i + i 293.00 -64.000 i + i 259.00 -65.000 i + i 229.00 -66.000 i + i 203.00 -67.000 i + i 229.00 -68.000 i + i 242.00 -69.000 i + i 233.00 -70.000 i + i 267.00 -71.000 i + i 269.00 -72.000 i + i 270.00 -73.000 i + i 315.00 -74.000 i + i 364.00 -75.000 i + i 347.00 -76.000 i + i 312.00 -77.000 i + i 274.00 -78.000 i + i 237.00 -79.000 i + i 278.00 -80.000 i + i 284.00 -81.000 i + i 277.00 -82.000 i + i 317.00 -83.000 i + i 313.00 -84.000 i + i 318.00 -85.000 i + i 374.00 -86.000 i + i 413.00 -87.000 i + i 405.00 -88.000 i + i 355.00 -89.000 i + i 306.00 -90.000 i + i 271.00 -91.000 i + i 306.00 -92.000 i + i 315.00 -93.000 i + i 301.00 -94.000 i + i 356.00 -95.000 i + i 348.00 -96.000 i + i 355.00 -97.000 i + i 422.00 -98.000 i + i 465.00 -99.000 i + i 467.00 -100.00 i + i 404.00 -101.00 i + i 347.00 -102.00 i + i 305.00 -103.00 i + i 336.00 -104.00 i + i 340.00 -105.00 i + i 318.00 -106.00 i + i 362.00 -107.00 i + i 348.00 -108.00 i + i 363.00 -109.00 i + i 435.00 -110.00 i + i 491.00 -111.00 i + i 505.00 -112.00 i + i 404.00 -113.00 i + i 359.00 -114.00 i + i 310.00 -115.00 i + i 337.00 -116.00 i + i 360.00 -117.00 i + i 342.00 -118.00 i + i 406.00 -119.00 i + i 396.00 -120.00 i + i 420.00 -121.00 i + i 472.00 -122.00 i + i 548.00 -123.00 i + i 559.00 -124.00 i + i 463.00 -125.00 i + i 407.00 -126.00 i + i 362.00 -127.00 i + i 405.00 -128.00 i + i 417.00 -129.00 i + i 391.00 -130.00 i + i 419.00 -131.00 i + i 461.00 -132.00 i + i 472.00 -133.00 i + i 535.00 -134.00 i + i 622.00 -135.00 i + i 606.00 -136.00 i + i 508.00 -137.00 i + i 461.00 -138.00 i + i 390.00 -139.00 i + i 432.00 ierr = 0 test of vpmc test number 2 n = 144 + / ns = 1 + / ilog = 2 isize= 2 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i + i 112.00 3.0000 i + i 118.00 2.0000 i + i 132.00 1.0000 i + i 129.00 .00000 i + i 121.00 -1.0000 i + i 135.00 -2.0000 i + i 148.00 -3.0000 i + i 148.00 -4.0000 i + i 136.00 -5.0000 i + i 119.00 -6.0000 i + i 104.00 -7.0000 i + i 118.00 -8.0000 i + i 115.00 -9.0000 i + i 126.00 -10.000 i + i 141.00 -11.000 i + i 135.00 -12.000 i + i 125.00 -13.000 i + i 149.00 -14.000 i + i 170.00 -15.000 i + i 170.00 -16.000 i + i 158.00 -17.000 i + i 133.00 -18.000 i + i 114.00 -19.000 i + i 140.00 -20.000 i + i 145.00 -21.000 i + i 150.00 -22.000 i + i 178.00 -23.000 i + i 163.00 -24.000 i + i 172.00 -25.000 i + i 178.00 -26.000 i + i 199.00 -27.000 i + i 199.00 -28.000 i + i 184.00 -29.000 i + i 162.00 -30.000 i + i 146.00 -31.000 i + i 166.00 -32.000 i + i 171.00 -33.000 i i Missing -34.000 i + i 193.00 -35.000 i + i 181.00 -36.000 i + i 183.00 -37.000 i + i 218.00 -38.000 i + i 230.00 -39.000 i + i 242.00 -40.000 i + i 209.00 -41.000 i + i 191.00 -42.000 i + i 172.00 -43.000 i + i 194.00 -44.000 i + i 196.00 -45.000 i + i 196.00 -46.000 i + i 236.00 -47.000 i + i 235.00 -48.000 i + i 229.00 -49.000 i + i 243.00 -50.000 i + i 264.00 -51.000 i + i 272.00 -52.000 i + i 237.00 -53.000 i + i 211.00 -54.000 i i Missing -55.000 i + i 201.00 -56.000 i + i 204.00 -57.000 i + i 188.00 -58.000 i + i 235.00 -59.000 i + i 227.00 -60.000 i + i 234.00 -61.000 i + i 264.00 -62.000 i + i 302.00 -63.000 i + i 293.00 -64.000 i + i 259.00 -65.000 i + i 229.00 -66.000 i + i 203.00 -67.000 i + i 229.00 -68.000 i + i 242.00 -69.000 i + i 233.00 -70.000 i + i 267.00 -71.000 i + i 269.00 -72.000 i + i 270.00 -73.000 i + i 315.00 -74.000 i + i 364.00 -75.000 i + i 347.00 -76.000 i + i 312.00 -77.000 i + i 274.00 -78.000 i + i 237.00 -79.000 i + i 278.00 -80.000 i + i 284.00 -81.000 i + i 277.00 -82.000 i + i 317.00 -83.000 i + i 313.00 -84.000 i + i 318.00 -85.000 i + i 374.00 -86.000 i + i 413.00 -87.000 i + i 405.00 -88.000 i + i 355.00 -89.000 i + i 306.00 -90.000 i + i 271.00 -91.000 i + i 306.00 -92.000 i + i 315.00 -93.000 i + i 301.00 -94.000 i + i 356.00 -95.000 i + i 348.00 -96.000 i + i 355.00 -97.000 i + i 422.00 -98.000 i + i 465.00 -99.000 i + i 467.00 -100.00 i + i 404.00 -101.00 i + i 347.00 -102.00 i + i 305.00 -103.00 i + i 336.00 -104.00 i + i 340.00 -105.00 i + i 318.00 -106.00 i + i 362.00 -107.00 i + i 348.00 -108.00 i + i 363.00 -109.00 i + i 435.00 -110.00 i + i 491.00 -111.00 i + i 505.00 -112.00 i + i 404.00 -113.00 i + i 359.00 -114.00 i + i 310.00 -115.00 i + i 337.00 -116.00 i + i 360.00 -117.00 i + i 342.00 -118.00 i + i 406.00 -119.00 i + i 396.00 -120.00 i + i 420.00 -121.00 i + i 472.00 -122.00 i + i 548.00 -123.00 i + i 559.00 -124.00 i + i 463.00 -125.00 i + i 407.00 -126.00 i + i 362.00 -127.00 i + i 405.00 -128.00 i + i 417.00 -129.00 i + i 391.00 -130.00 i + i 419.00 -131.00 i + i 461.00 -132.00 i + i 472.00 -133.00 i + i 535.00 -134.00 i + i 622.00 -135.00 i + i 606.00 -136.00 i + i 508.00 -137.00 i + i 461.00 -138.00 i + i 390.00 -139.00 i + i 432.00 ierr = 0 test of svpc test number 2 n = 144 + / ns = 1 + / ilog = 2 isize= 2 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i + i 112.00 3.0000 i z i 118.00 2.0000 i c i 132.00 1.0000 i d i 129.00 .00000 i e i 121.00 -1.0000 i f i 135.00 -2.0000 i g i 148.00 -3.0000 i h i 148.00 -4.0000 i i i 136.00 -5.0000 i j i 119.00 -6.0000 i k i 104.00 -7.0000 i l i 118.00 -8.0000 i a i 115.00 -9.0000 i b i 126.00 -10.000 i c i 141.00 -11.000 i d i 135.00 -12.000 i e i 125.00 -13.000 i f i 149.00 -14.000 i g i 170.00 -15.000 i h i 170.00 -16.000 i i i 158.00 -17.000 i j i 133.00 -18.000 i k i 114.00 -19.000 i l i 140.00 -20.000 i a i 145.00 -21.000 i b i 150.00 -22.000 i c i 178.00 -23.000 i d i 163.00 -24.000 i e i 172.00 -25.000 i f i 178.00 -26.000 i g i 199.00 -27.000 i h i 199.00 -28.000 i i i 184.00 -29.000 i j i 162.00 -30.000 i k i 146.00 -31.000 i l i 166.00 -32.000 i a i 171.00 -33.000 i b i 180.00 -34.000 i c i 193.00 -35.000 i d i 181.00 -36.000 i e i 183.00 -37.000 i f i 218.00 -38.000 i g i 230.00 -39.000 i h i 242.00 -40.000 i i i 209.00 -41.000 i j i 191.00 -42.000 i k i 172.00 -43.000 i l i 194.00 -44.000 i a i 196.00 -45.000 i b i 196.00 -46.000 i c i 236.00 -47.000 i d i 235.00 -48.000 i e i 229.00 -49.000 i f i 243.00 -50.000 i g i 264.00 -51.000 i h i 272.00 -52.000 i i i 237.00 -53.000 i j i 211.00 -54.000 i k i 180.00 -55.000 i l i 201.00 -56.000 i a i 204.00 -57.000 i b i 188.00 -58.000 i c i 235.00 -59.000 i d i 227.00 -60.000 i e i 234.00 -61.000 i f i 264.00 -62.000 i g i 302.00 -63.000 i h i 293.00 -64.000 i i i 259.00 -65.000 i j i 229.00 -66.000 i k i 203.00 -67.000 i l i 229.00 -68.000 i a i 242.00 -69.000 i b i 233.00 -70.000 i c i 267.00 -71.000 i d i 269.00 -72.000 i e i 270.00 -73.000 i f i 315.00 -74.000 i g i 364.00 -75.000 i h i 347.00 -76.000 i i i 312.00 -77.000 i j i 274.00 -78.000 i k i 237.00 -79.000 i l i 278.00 -80.000 i a i 284.00 -81.000 i b i 277.00 -82.000 i c i 317.00 -83.000 i d i 313.00 -84.000 i e i 318.00 -85.000 i f i 374.00 -86.000 i g i 413.00 -87.000 i h i 405.00 -88.000 i i i 355.00 -89.000 i j i 306.00 -90.000 i k i 271.00 -91.000 i l i 306.00 -92.000 i a i 315.00 -93.000 i b i 301.00 -94.000 i c i 356.00 -95.000 i d i 348.00 -96.000 i e i 355.00 -97.000 i f i 422.00 -98.000 i g i 465.00 -99.000 i h i 467.00 -100.00 i i i 404.00 -101.00 i j i 347.00 -102.00 i k i 305.00 -103.00 i l i 336.00 -104.00 i a i 340.00 -105.00 i b i 318.00 -106.00 i c i 362.00 -107.00 i d i 348.00 -108.00 i e i 363.00 -109.00 i f i 435.00 -110.00 i g i 491.00 -111.00 i h i 505.00 -112.00 i i i 404.00 -113.00 i j i 359.00 -114.00 i k i 310.00 -115.00 i l i 337.00 -116.00 i a i 360.00 -117.00 i b i 342.00 -118.00 i c i 406.00 -119.00 i d i 396.00 -120.00 i e i 420.00 -121.00 i f i 472.00 -122.00 i g i 548.00 -123.00 i h i 559.00 -124.00 i i i 463.00 -125.00 i j i 407.00 -126.00 i k i 362.00 -127.00 i l i 405.00 -128.00 i a i 417.00 -129.00 i b i 391.00 -130.00 i c i 419.00 -131.00 i d i 461.00 -132.00 i e i 472.00 -133.00 i f i 535.00 -134.00 i g i 622.00 -135.00 i h i 606.00 -136.00 i i i 508.00 -137.00 i j i 461.00 -138.00 i k i 390.00 -139.00 i l i 432.00 ierr = 0 test of svpmc test number 2 n = 144 + / ns = 1 + / ilog = 2 isize= 2 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i + i 112.00 3.0000 i z i 118.00 2.0000 i c i 132.00 1.0000 i d i 129.00 .00000 i e i 121.00 -1.0000 i f i 135.00 -2.0000 i g i 148.00 -3.0000 i h i 148.00 -4.0000 i i i 136.00 -5.0000 i j i 119.00 -6.0000 i k i 104.00 -7.0000 i l i 118.00 -8.0000 i a i 115.00 -9.0000 i b i 126.00 -10.000 i c i 141.00 -11.000 i d i 135.00 -12.000 i e i 125.00 -13.000 i f i 149.00 -14.000 i g i 170.00 -15.000 i h i 170.00 -16.000 i i i 158.00 -17.000 i j i 133.00 -18.000 i k i 114.00 -19.000 i l i 140.00 -20.000 i a i 145.00 -21.000 i b i 150.00 -22.000 i c i 178.00 -23.000 i d i 163.00 -24.000 i e i 172.00 -25.000 i f i 178.00 -26.000 i g i 199.00 -27.000 i h i 199.00 -28.000 i i i 184.00 -29.000 i j i 162.00 -30.000 i k i 146.00 -31.000 i l i 166.00 -32.000 i a i 171.00 -33.000 i i Missing -34.000 i c i 193.00 -35.000 i d i 181.00 -36.000 i e i 183.00 -37.000 i f i 218.00 -38.000 i g i 230.00 -39.000 i h i 242.00 -40.000 i i i 209.00 -41.000 i j i 191.00 -42.000 i k i 172.00 -43.000 i l i 194.00 -44.000 i a i 196.00 -45.000 i b i 196.00 -46.000 i c i 236.00 -47.000 i d i 235.00 -48.000 i e i 229.00 -49.000 i f i 243.00 -50.000 i g i 264.00 -51.000 i h i 272.00 -52.000 i i i 237.00 -53.000 i j i 211.00 -54.000 i i Missing -55.000 i l i 201.00 -56.000 i a i 204.00 -57.000 i b i 188.00 -58.000 i c i 235.00 -59.000 i d i 227.00 -60.000 i e i 234.00 -61.000 i f i 264.00 -62.000 i g i 302.00 -63.000 i h i 293.00 -64.000 i i i 259.00 -65.000 i j i 229.00 -66.000 i k i 203.00 -67.000 i l i 229.00 -68.000 i a i 242.00 -69.000 i b i 233.00 -70.000 i c i 267.00 -71.000 i d i 269.00 -72.000 i e i 270.00 -73.000 i f i 315.00 -74.000 i g i 364.00 -75.000 i h i 347.00 -76.000 i i i 312.00 -77.000 i j i 274.00 -78.000 i k i 237.00 -79.000 i l i 278.00 -80.000 i a i 284.00 -81.000 i b i 277.00 -82.000 i c i 317.00 -83.000 i d i 313.00 -84.000 i e i 318.00 -85.000 i f i 374.00 -86.000 i g i 413.00 -87.000 i h i 405.00 -88.000 i i i 355.00 -89.000 i j i 306.00 -90.000 i k i 271.00 -91.000 i l i 306.00 -92.000 i a i 315.00 -93.000 i b i 301.00 -94.000 i c i 356.00 -95.000 i d i 348.00 -96.000 i e i 355.00 -97.000 i f i 422.00 -98.000 i g i 465.00 -99.000 i h i 467.00 -100.00 i i i 404.00 -101.00 i j i 347.00 -102.00 i k i 305.00 -103.00 i l i 336.00 -104.00 i a i 340.00 -105.00 i b i 318.00 -106.00 i c i 362.00 -107.00 i d i 348.00 -108.00 i e i 363.00 -109.00 i f i 435.00 -110.00 i g i 491.00 -111.00 i h i 505.00 -112.00 i i i 404.00 -113.00 i j i 359.00 -114.00 i k i 310.00 -115.00 i l i 337.00 -116.00 i a i 360.00 -117.00 i b i 342.00 -118.00 i c i 406.00 -119.00 i d i 396.00 -120.00 i e i 420.00 -121.00 i f i 472.00 -122.00 i g i 548.00 -123.00 i h i 559.00 -124.00 i i i 463.00 -125.00 i j i 407.00 -126.00 i k i 362.00 -127.00 i l i 405.00 -128.00 i a i 417.00 -129.00 i b i 391.00 -130.00 i c i 419.00 -131.00 i d i 461.00 -132.00 i e i 472.00 -133.00 i f i 535.00 -134.00 i g i 622.00 -135.00 i h i 606.00 -136.00 i i i 508.00 -137.00 i j i 461.00 -138.00 i k i 390.00 -139.00 i l i 432.00 ierr = 0 test of mvpc test number 2 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 2 isize= 2 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i ab c d 2 g h i jk l i 3.0000 i ab c dfe g h ij k l i 2.0000 i ab cd 2 g h 2 kl i 1.0000 i ab c d fe g h 2 k l i .00000 i ab cd 2 g h 2 k l i -1.0000 i a b c d e f g h ij k l i -2.0000 i a b c d e f g h i j k l i -3.0000 i a b c d ef g h i j k l i -4.0000 i a b c d e f g h 2 k l i -5.0000 i a b c d e f g h ij k l i -6.0000 i a b c de f g h ij k l i -7.0000 i a b c de f g h 2 k l i ierr = 0 test of mvpmc test number 2 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 2 isize= 2 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 700.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i ab c d 2 g h i jk l i 3.0000 i ab c fe g h ij k l i Missing 2.0000 i ab cd 2 g h 2 kl i 1.0000 i ab c d fe g h 2 k l i .00000 i ab cd 2 g h 2 k l i -1.0000 i a b c d e f g h ij k l i -2.0000 i a b c d e f g h i j k l i -3.0000 i a b c d ef g h i j k l i -4.0000 i a b c d e f g h 2 k l i -5.0000 i a b c d e f g h ij k l i -6.0000 i a b c d f g h ij k l i Missing -7.0000 i a b c de f g h 2 k l i ierr = 0 test of vpc test number 3 n = 144 + / ns = 1 + / ilog = 20 isize= 20 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 120.0000 140.0000 160.0000 180.0000 200.0000 220.0000 240.0000 260.0000 280.0000 300.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 3.0000 i + i 118.00 2.0000 i + i 132.00 1.0000 i + i 129.00 .00000 i + i 121.00 -1.0000 i + i 135.00 -2.0000 i + i 148.00 -3.0000 i + i 148.00 -4.0000 i + i 136.00 -5.0000 i + i 119.00 -6.0000 i + i 104.00 -7.0000 i + i 118.00 -8.0000 i + i 115.00 -9.0000 i + i 126.00 -10.000 i + i 141.00 -11.000 i + i 135.00 -12.000 i + i 125.00 -13.000 i + i 149.00 -14.000 i + i 170.00 -15.000 i + i 170.00 -16.000 i + i 158.00 -17.000 i + i 133.00 -18.000 i + i 114.00 -19.000 i + i 140.00 -20.000 i + i 145.00 -21.000 i + i 150.00 -22.000 i + i 178.00 -23.000 i + i 163.00 -24.000 i + i 172.00 -25.000 i + i 178.00 -26.000 i + i 199.00 -27.000 i + i 199.00 -28.000 i + i 184.00 -29.000 i + i 162.00 -30.000 i + i 146.00 -31.000 i + i 166.00 -32.000 i + i 171.00 -33.000 i + i 180.00 -34.000 i + i 193.00 -35.000 i + i 181.00 -36.000 i + i 183.00 -37.000 i + i 218.00 -38.000 i + i 230.00 -39.000 i + i 242.00 -40.000 i + i 209.00 -41.000 i + i 191.00 -42.000 i + i 172.00 -43.000 i + i 194.00 -44.000 i + i 196.00 -45.000 i + i 196.00 -46.000 i + i 236.00 -47.000 i + i 235.00 -48.000 i + i 229.00 -49.000 i + i 243.00 -50.000 i + i 264.00 -51.000 i + i 272.00 -52.000 i + i 237.00 -53.000 i + i 211.00 -54.000 i + i 180.00 -55.000 i + i 201.00 -56.000 i + i 204.00 -57.000 i + i 188.00 -58.000 i + i 235.00 -59.000 i + i 227.00 -60.000 i + i 234.00 -61.000 i + i 264.00 -62.000 i i 302.00 -63.000 i + i 293.00 -64.000 i + i 259.00 -65.000 i + i 229.00 -66.000 i + i 203.00 -67.000 i + i 229.00 -68.000 i + i 242.00 -69.000 i + i 233.00 -70.000 i + i 267.00 -71.000 i + i 269.00 -72.000 i + i 270.00 -73.000 i i 315.00 -74.000 i i 364.00 -75.000 i i 347.00 -76.000 i i 312.00 -77.000 i + i 274.00 -78.000 i + i 237.00 -79.000 i + i 278.00 -80.000 i + i 284.00 -81.000 i + i 277.00 -82.000 i i 317.00 -83.000 i i 313.00 -84.000 i i 318.00 -85.000 i i 374.00 -86.000 i i 413.00 -87.000 i i 405.00 -88.000 i i 355.00 -89.000 i i 306.00 -90.000 i + i 271.00 -91.000 i i 306.00 -92.000 i i 315.00 -93.000 i i 301.00 -94.000 i i 356.00 -95.000 i i 348.00 -96.000 i i 355.00 -97.000 i i 422.00 -98.000 i i 465.00 -99.000 i i 467.00 -100.00 i i 404.00 -101.00 i i 347.00 -102.00 i i 305.00 -103.00 i i 336.00 -104.00 i i 340.00 -105.00 i i 318.00 -106.00 i i 362.00 -107.00 i i 348.00 -108.00 i i 363.00 -109.00 i i 435.00 -110.00 i i 491.00 -111.00 i i 505.00 -112.00 i i 404.00 -113.00 i i 359.00 -114.00 i i 310.00 -115.00 i i 337.00 -116.00 i i 360.00 -117.00 i i 342.00 -118.00 i i 406.00 -119.00 i i 396.00 -120.00 i i 420.00 -121.00 i i 472.00 -122.00 i i 548.00 -123.00 i i 559.00 -124.00 i i 463.00 -125.00 i i 407.00 -126.00 i i 362.00 -127.00 i i 405.00 -128.00 i i 417.00 -129.00 i i 391.00 -130.00 i i 419.00 -131.00 i i 461.00 -132.00 i i 472.00 -133.00 i i 535.00 -134.00 i i 622.00 -135.00 i i 606.00 -136.00 i i 508.00 -137.00 i i 461.00 -138.00 i i 390.00 -139.00 i i 432.00 ierr = 0 test of vpmc test number 3 n = 144 + / ns = 1 + / ilog = 20 isize= 20 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 120.0000 140.0000 160.0000 180.0000 200.0000 220.0000 240.0000 260.0000 280.0000 300.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 3.0000 i + i 118.00 2.0000 i + i 132.00 1.0000 i + i 129.00 .00000 i + i 121.00 -1.0000 i + i 135.00 -2.0000 i + i 148.00 -3.0000 i + i 148.00 -4.0000 i + i 136.00 -5.0000 i + i 119.00 -6.0000 i + i 104.00 -7.0000 i + i 118.00 -8.0000 i + i 115.00 -9.0000 i + i 126.00 -10.000 i + i 141.00 -11.000 i + i 135.00 -12.000 i + i 125.00 -13.000 i + i 149.00 -14.000 i + i 170.00 -15.000 i + i 170.00 -16.000 i + i 158.00 -17.000 i + i 133.00 -18.000 i + i 114.00 -19.000 i + i 140.00 -20.000 i + i 145.00 -21.000 i + i 150.00 -22.000 i + i 178.00 -23.000 i + i 163.00 -24.000 i + i 172.00 -25.000 i + i 178.00 -26.000 i + i 199.00 -27.000 i + i 199.00 -28.000 i + i 184.00 -29.000 i + i 162.00 -30.000 i + i 146.00 -31.000 i + i 166.00 -32.000 i + i 171.00 -33.000 i i Missing -34.000 i + i 193.00 -35.000 i + i 181.00 -36.000 i + i 183.00 -37.000 i + i 218.00 -38.000 i + i 230.00 -39.000 i + i 242.00 -40.000 i + i 209.00 -41.000 i + i 191.00 -42.000 i + i 172.00 -43.000 i + i 194.00 -44.000 i + i 196.00 -45.000 i + i 196.00 -46.000 i + i 236.00 -47.000 i + i 235.00 -48.000 i + i 229.00 -49.000 i + i 243.00 -50.000 i + i 264.00 -51.000 i + i 272.00 -52.000 i + i 237.00 -53.000 i + i 211.00 -54.000 i i Missing -55.000 i + i 201.00 -56.000 i + i 204.00 -57.000 i + i 188.00 -58.000 i + i 235.00 -59.000 i + i 227.00 -60.000 i + i 234.00 -61.000 i + i 264.00 -62.000 i i 302.00 -63.000 i + i 293.00 -64.000 i + i 259.00 -65.000 i + i 229.00 -66.000 i + i 203.00 -67.000 i + i 229.00 -68.000 i + i 242.00 -69.000 i + i 233.00 -70.000 i + i 267.00 -71.000 i + i 269.00 -72.000 i + i 270.00 -73.000 i i 315.00 -74.000 i i 364.00 -75.000 i i 347.00 -76.000 i i 312.00 -77.000 i + i 274.00 -78.000 i + i 237.00 -79.000 i + i 278.00 -80.000 i + i 284.00 -81.000 i + i 277.00 -82.000 i i 317.00 -83.000 i i 313.00 -84.000 i i 318.00 -85.000 i i 374.00 -86.000 i i 413.00 -87.000 i i 405.00 -88.000 i i 355.00 -89.000 i i 306.00 -90.000 i + i 271.00 -91.000 i i 306.00 -92.000 i i 315.00 -93.000 i i 301.00 -94.000 i i 356.00 -95.000 i i 348.00 -96.000 i i 355.00 -97.000 i i 422.00 -98.000 i i 465.00 -99.000 i i 467.00 -100.00 i i 404.00 -101.00 i i 347.00 -102.00 i i 305.00 -103.00 i i 336.00 -104.00 i i 340.00 -105.00 i i 318.00 -106.00 i i 362.00 -107.00 i i 348.00 -108.00 i i 363.00 -109.00 i i 435.00 -110.00 i i 491.00 -111.00 i i 505.00 -112.00 i i 404.00 -113.00 i i 359.00 -114.00 i i 310.00 -115.00 i i 337.00 -116.00 i i 360.00 -117.00 i i 342.00 -118.00 i i 406.00 -119.00 i i 396.00 -120.00 i i 420.00 -121.00 i i 472.00 -122.00 i i 548.00 -123.00 i i 559.00 -124.00 i i 463.00 -125.00 i i 407.00 -126.00 i i 362.00 -127.00 i i 405.00 -128.00 i i 417.00 -129.00 i i 391.00 -130.00 i i 419.00 -131.00 i i 461.00 -132.00 i i 472.00 -133.00 i i 535.00 -134.00 i i 622.00 -135.00 i i 606.00 -136.00 i i 508.00 -137.00 i i 461.00 -138.00 i i 390.00 -139.00 i i 432.00 ierr = 0 test of svpc test number 3 n = 144 + / ns = 1 + / ilog = 20 isize= 20 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 120.0000 140.0000 160.0000 180.0000 200.0000 220.0000 240.0000 260.0000 280.0000 300.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 3.0000 i z i 118.00 2.0000 i c i 132.00 1.0000 i d i 129.00 .00000 i e i 121.00 -1.0000 i f i 135.00 -2.0000 i g i 148.00 -3.0000 i h i 148.00 -4.0000 i i i 136.00 -5.0000 i j i 119.00 -6.0000 i k i 104.00 -7.0000 i l i 118.00 -8.0000 i a i 115.00 -9.0000 i b i 126.00 -10.000 i c i 141.00 -11.000 i d i 135.00 -12.000 i e i 125.00 -13.000 i f i 149.00 -14.000 i g i 170.00 -15.000 i h i 170.00 -16.000 i i i 158.00 -17.000 i j i 133.00 -18.000 i k i 114.00 -19.000 i l i 140.00 -20.000 i a i 145.00 -21.000 i b i 150.00 -22.000 i c i 178.00 -23.000 i d i 163.00 -24.000 i e i 172.00 -25.000 i f i 178.00 -26.000 i g i 199.00 -27.000 i h i 199.00 -28.000 i i i 184.00 -29.000 i j i 162.00 -30.000 i k i 146.00 -31.000 i l i 166.00 -32.000 i a i 171.00 -33.000 i b i 180.00 -34.000 i c i 193.00 -35.000 i d i 181.00 -36.000 i e i 183.00 -37.000 i f i 218.00 -38.000 i g i 230.00 -39.000 i h i 242.00 -40.000 i i i 209.00 -41.000 i j i 191.00 -42.000 i k i 172.00 -43.000 i l i 194.00 -44.000 i a i 196.00 -45.000 i b i 196.00 -46.000 i c i 236.00 -47.000 i d i 235.00 -48.000 i e i 229.00 -49.000 i f i 243.00 -50.000 i g i 264.00 -51.000 i h i 272.00 -52.000 i i i 237.00 -53.000 i j i 211.00 -54.000 i k i 180.00 -55.000 i l i 201.00 -56.000 i a i 204.00 -57.000 i b i 188.00 -58.000 i c i 235.00 -59.000 i d i 227.00 -60.000 i e i 234.00 -61.000 i f i 264.00 -62.000 i i 302.00 -63.000 i h i 293.00 -64.000 i i i 259.00 -65.000 i j i 229.00 -66.000 i k i 203.00 -67.000 i l i 229.00 -68.000 i a i 242.00 -69.000 i b i 233.00 -70.000 i c i 267.00 -71.000 i d i 269.00 -72.000 i e i 270.00 -73.000 i i 315.00 -74.000 i i 364.00 -75.000 i i 347.00 -76.000 i i 312.00 -77.000 i j i 274.00 -78.000 i k i 237.00 -79.000 i l i 278.00 -80.000 i a i 284.00 -81.000 i b i 277.00 -82.000 i i 317.00 -83.000 i i 313.00 -84.000 i i 318.00 -85.000 i i 374.00 -86.000 i i 413.00 -87.000 i i 405.00 -88.000 i i 355.00 -89.000 i i 306.00 -90.000 i k i 271.00 -91.000 i i 306.00 -92.000 i i 315.00 -93.000 i i 301.00 -94.000 i i 356.00 -95.000 i i 348.00 -96.000 i i 355.00 -97.000 i i 422.00 -98.000 i i 465.00 -99.000 i i 467.00 -100.00 i i 404.00 -101.00 i i 347.00 -102.00 i i 305.00 -103.00 i i 336.00 -104.00 i i 340.00 -105.00 i i 318.00 -106.00 i i 362.00 -107.00 i i 348.00 -108.00 i i 363.00 -109.00 i i 435.00 -110.00 i i 491.00 -111.00 i i 505.00 -112.00 i i 404.00 -113.00 i i 359.00 -114.00 i i 310.00 -115.00 i i 337.00 -116.00 i i 360.00 -117.00 i i 342.00 -118.00 i i 406.00 -119.00 i i 396.00 -120.00 i i 420.00 -121.00 i i 472.00 -122.00 i i 548.00 -123.00 i i 559.00 -124.00 i i 463.00 -125.00 i i 407.00 -126.00 i i 362.00 -127.00 i i 405.00 -128.00 i i 417.00 -129.00 i i 391.00 -130.00 i i 419.00 -131.00 i i 461.00 -132.00 i i 472.00 -133.00 i i 535.00 -134.00 i i 622.00 -135.00 i i 606.00 -136.00 i i 508.00 -137.00 i i 461.00 -138.00 i i 390.00 -139.00 i i 432.00 ierr = 0 test of svpmc test number 3 n = 144 + / ns = 1 + / ilog = 20 isize= 20 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 120.0000 140.0000 160.0000 180.0000 200.0000 220.0000 240.0000 260.0000 280.0000 300.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i + i 112.00 3.0000 i z i 118.00 2.0000 i c i 132.00 1.0000 i d i 129.00 .00000 i e i 121.00 -1.0000 i f i 135.00 -2.0000 i g i 148.00 -3.0000 i h i 148.00 -4.0000 i i i 136.00 -5.0000 i j i 119.00 -6.0000 i k i 104.00 -7.0000 i l i 118.00 -8.0000 i a i 115.00 -9.0000 i b i 126.00 -10.000 i c i 141.00 -11.000 i d i 135.00 -12.000 i e i 125.00 -13.000 i f i 149.00 -14.000 i g i 170.00 -15.000 i h i 170.00 -16.000 i i i 158.00 -17.000 i j i 133.00 -18.000 i k i 114.00 -19.000 i l i 140.00 -20.000 i a i 145.00 -21.000 i b i 150.00 -22.000 i c i 178.00 -23.000 i d i 163.00 -24.000 i e i 172.00 -25.000 i f i 178.00 -26.000 i g i 199.00 -27.000 i h i 199.00 -28.000 i i i 184.00 -29.000 i j i 162.00 -30.000 i k i 146.00 -31.000 i l i 166.00 -32.000 i a i 171.00 -33.000 i i Missing -34.000 i c i 193.00 -35.000 i d i 181.00 -36.000 i e i 183.00 -37.000 i f i 218.00 -38.000 i g i 230.00 -39.000 i h i 242.00 -40.000 i i i 209.00 -41.000 i j i 191.00 -42.000 i k i 172.00 -43.000 i l i 194.00 -44.000 i a i 196.00 -45.000 i b i 196.00 -46.000 i c i 236.00 -47.000 i d i 235.00 -48.000 i e i 229.00 -49.000 i f i 243.00 -50.000 i g i 264.00 -51.000 i h i 272.00 -52.000 i i i 237.00 -53.000 i j i 211.00 -54.000 i i Missing -55.000 i l i 201.00 -56.000 i a i 204.00 -57.000 i b i 188.00 -58.000 i c i 235.00 -59.000 i d i 227.00 -60.000 i e i 234.00 -61.000 i f i 264.00 -62.000 i i 302.00 -63.000 i h i 293.00 -64.000 i i i 259.00 -65.000 i j i 229.00 -66.000 i k i 203.00 -67.000 i l i 229.00 -68.000 i a i 242.00 -69.000 i b i 233.00 -70.000 i c i 267.00 -71.000 i d i 269.00 -72.000 i e i 270.00 -73.000 i i 315.00 -74.000 i i 364.00 -75.000 i i 347.00 -76.000 i i 312.00 -77.000 i j i 274.00 -78.000 i k i 237.00 -79.000 i l i 278.00 -80.000 i a i 284.00 -81.000 i b i 277.00 -82.000 i i 317.00 -83.000 i i 313.00 -84.000 i i 318.00 -85.000 i i 374.00 -86.000 i i 413.00 -87.000 i i 405.00 -88.000 i i 355.00 -89.000 i i 306.00 -90.000 i k i 271.00 -91.000 i i 306.00 -92.000 i i 315.00 -93.000 i i 301.00 -94.000 i i 356.00 -95.000 i i 348.00 -96.000 i i 355.00 -97.000 i i 422.00 -98.000 i i 465.00 -99.000 i i 467.00 -100.00 i i 404.00 -101.00 i i 347.00 -102.00 i i 305.00 -103.00 i i 336.00 -104.00 i i 340.00 -105.00 i i 318.00 -106.00 i i 362.00 -107.00 i i 348.00 -108.00 i i 363.00 -109.00 i i 435.00 -110.00 i i 491.00 -111.00 i i 505.00 -112.00 i i 404.00 -113.00 i i 359.00 -114.00 i i 310.00 -115.00 i i 337.00 -116.00 i i 360.00 -117.00 i i 342.00 -118.00 i i 406.00 -119.00 i i 396.00 -120.00 i i 420.00 -121.00 i i 472.00 -122.00 i i 548.00 -123.00 i i 559.00 -124.00 i i 463.00 -125.00 i i 407.00 -126.00 i i 362.00 -127.00 i i 405.00 -128.00 i i 417.00 -129.00 i i 391.00 -130.00 i i 419.00 -131.00 i i 461.00 -132.00 i i 472.00 -133.00 i i 535.00 -134.00 i i 622.00 -135.00 i i 606.00 -136.00 i i 508.00 -137.00 i i 461.00 -138.00 i i 390.00 -139.00 i i 432.00 ierr = 0 test of mvpc test number 3 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 20 isize= 20 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 120.0000 140.0000 160.0000 180.0000 200.0000 220.0000 240.0000 260.0000 280.0000 300.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i a b c d e f g h i 3.0000 i a b c d f e g h i 2.0000 i a b c d 2 g i 1.0000 i a b c d f e g i .00000 i a b c d e f g i -1.0000 i a b c d e f i -2.0000 i a b c d e i -3.0000 i a b c d e f i -4.0000 i a b c d e f i -5.0000 i a b c d e f g i -6.0000 i a b c d e f g h i -7.0000 i a b c d e f g i ierr = 0 test of mvpmc test number 3 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 20 isize= 20 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 120.0000 140.0000 160.0000 180.0000 200.0000 220.0000 240.0000 260.0000 280.0000 300.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 4.0000 i a b c d e f g h i 3.0000 i a b c f e g h i Missing 2.0000 i a b c d 2 g i 1.0000 i a b c d f e g i .00000 i a b c d e f g i -1.0000 i a b c d e f i -2.0000 i a b c d e i -3.0000 i a b c d e f i -4.0000 i a b c d e f i -5.0000 i a b c d e f g i -6.0000 i a b c d f g h i Missing -7.0000 i a b c d e f g i ierr = 0 test of mvpc test number 4 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i ab c d 2 g h i 3.0000 i ab c dfe g h i 2.0000 i ab cd 2 g i 1.0000 i ab c d fe g i .00000 i ab cd 2 g i -1.0000 i a b c d e f i -2.0000 i a b c d e i -3.0000 i a b c d ef i -4.0000 i a b c d e f i -5.0000 i a b c d e f g i -6.0000 i a b c de f g h i -7.0000 i a b c de f g i ierr = 0 test of mvpmc test number 4 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i ab c d 2 g h i 3.0000 i ab c fe g h i Missing 2.0000 i ab cd 2 g i 1.0000 i ab c d fe g i .00000 i ab cd 2 g i -1.0000 i a b c d e f i -2.0000 i a b c d e i -3.0000 i a b c d ef i -4.0000 i a b c d e f i -5.0000 i a b c d e f g i -6.0000 i a b c d f g h i Missing -7.0000 i a b c de f g i ierr = 0 test of mvpc test number 5 n = 1 + / m = 144 / iym = 1 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i ka24235242257472z78 562 i ierr = 0 test of mvpmc test number 5 n = 1 + / m = 144 / iym = 1 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 100.0000 / yub = 300.0000 / xlb = 4.0000 + / xinc = -1.0000 starpac 2.08s (03/15/90) 100.0000 200.0000 400.0000 800.0000 -i--------------i--------------i--------i-----i----i- 4.0000 i ka24235242255472z78 562 i Missing ierr = 0 test of vpc test number 6 n = 36 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 0.5000 0.8000 2.0000 4.0000 -i---i-----i----i--------------i--------------i----i- .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 ierr = 0 test of vpmc test number 6 n = 36 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 0.5000 0.8000 2.0000 4.0000 -i---i-----i----i--------------i--------------i----i- .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 .00000 i + i 1.0000 ierr = 0 test of svpc test number 6 n = 36 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 0.5000 0.8000 2.0000 4.0000 -i---i-----i----i--------------i--------------i----i- .00000 i + i 1.0000 .00000 i z i 1.0000 .00000 i c i 1.0000 .00000 i d i 1.0000 .00000 i e i 1.0000 .00000 i f i 1.0000 .00000 i g i 1.0000 .00000 i h i 1.0000 .00000 i i i 1.0000 .00000 i j i 1.0000 .00000 i k i 1.0000 .00000 i l i 1.0000 .00000 i a i 1.0000 .00000 i b i 1.0000 .00000 i c i 1.0000 .00000 i d i 1.0000 .00000 i e i 1.0000 .00000 i f i 1.0000 .00000 i g i 1.0000 .00000 i h i 1.0000 .00000 i i i 1.0000 .00000 i j i 1.0000 .00000 i k i 1.0000 .00000 i l i 1.0000 .00000 i a i 1.0000 .00000 i b i 1.0000 .00000 i c i 1.0000 .00000 i d i 1.0000 .00000 i e i 1.0000 .00000 i f i 1.0000 .00000 i g i 1.0000 .00000 i h i 1.0000 .00000 i i i 1.0000 .00000 i j i 1.0000 .00000 i k i 1.0000 .00000 i l i 1.0000 ierr = 0 test of svpmc test number 6 n = 36 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 0.5000 0.8000 2.0000 4.0000 -i---i-----i----i--------------i--------------i----i- .00000 i + i 1.0000 .00000 i z i 1.0000 .00000 i c i 1.0000 .00000 i d i 1.0000 .00000 i e i 1.0000 .00000 i f i 1.0000 .00000 i g i 1.0000 .00000 i h i 1.0000 .00000 i i i 1.0000 .00000 i j i 1.0000 .00000 i k i 1.0000 .00000 i l i 1.0000 .00000 i a i 1.0000 .00000 i b i 1.0000 .00000 i c i 1.0000 .00000 i d i 1.0000 .00000 i e i 1.0000 .00000 i f i 1.0000 .00000 i g i 1.0000 .00000 i h i 1.0000 .00000 i i i 1.0000 .00000 i j i 1.0000 .00000 i k i 1.0000 .00000 i l i 1.0000 .00000 i a i 1.0000 .00000 i b i 1.0000 .00000 i c i 1.0000 .00000 i d i 1.0000 .00000 i e i 1.0000 .00000 i f i 1.0000 .00000 i g i 1.0000 .00000 i h i 1.0000 .00000 i i i 1.0000 .00000 i j i 1.0000 .00000 i k i 1.0000 .00000 i l i 1.0000 ierr = 0 test of mvpc test number 6 n = 6 + / m = 6 / iym = 12 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 0.5000 0.8000 2.0000 4.0000 -i---i-----i----i--------------i--------------i----i- .00000 i 6 i .00000 i 6 i .00000 i 6 i .00000 i 6 i .00000 i 6 i .00000 i 6 i ierr = 0 test of mvpmc test number 6 n = 6 + / m = 6 / iym = 12 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) 0.5000 0.8000 2.0000 4.0000 -i---i-----i----i--------------i--------------i----i- .00000 i 6 i .00000 i 6 i .00000 i 6 i .00000 i 6 i .00000 i 6 i .00000 i 6 i ierr = 0 test of vp test number 7 n = 0 + / ns = 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine vp ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call vp (y, n, ns) ierr = 1 test of vpm test number 7 n = 0 + / ns = 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine vpm ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call vpm (y, ymiss, n, ns) ierr = 1 test of svp test number 7 n = 0 + / ns = 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine svp ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call svp (y, n, ns, isym) ierr = 1 test of svpm test number 7 n = 0 + / ns = 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine svpm ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call svpm (y, ymiss, n, ns, isym) ierr = 1 test of mvp test number 7 n = 0 + / m = 0 / iym = -1 + / ns = 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvp ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mvp (ym, n, m, iym, ns) ierr = 1 test of mvpm test number 7 n = 0 + / m = 0 / iym = -1 + / ns = 1 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpm ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mvpm (ym, ymmiss, n, m, iym, ns) ierr = 1 test of vpl test number 7 n = 0 + / ns = 1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine vpl ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call vpl (y, n, ns, ilog) ierr = 1 test of vpml test number 7 n = 0 + / ns = 1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine vpml ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call vpml (y, ymiss, n, ns, ilog) ierr = 1 test of svpl test number 7 n = 0 + / ns = 1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine svpl ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call svpl (y, n, ns, isym, ilog) ierr = 1 test of svpml test number 7 n = 0 + / ns = 1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine svpml ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call svpml (y, ymiss, n, ns, isym, ilog) ierr = 1 test of mvpl test number 7 n = 0 + / m = 0 / iym = -1 + / ns = 1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpl ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mvpl (ym, n, m, iym, ns, ilog) ierr = 1 test of mvpml test number 7 n = 0 + / m = 0 / iym = -1 + / ns = 1 + / ilog = 22 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpml ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mvpml (ym, ymmiss, n, m, iym, ns, ilog) ierr = 1 test of vpc test number 7 n = 0 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine vpc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call vpc (y, n, ns, ilog, + isize, irlin, ibar, ylb, yub, xlb, xinc) ierr = 1 test of vpmc test number 7 n = 0 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine vpmc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call vpmc (y, ymiss, n, ns, ilog, + isize, irlin, ibar, ylb, yub, xlb, xinc) ierr = 1 test of svpc test number 7 n = 0 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine svpc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call svpc (y, n, ns, isym, ilog, + isize, irlin, ibar, ylb, yub, xlb, xinc) ierr = 1 test of svpmc test number 7 n = 0 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine svpmc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the correct form of the call statement is call svpmc (y, ymiss, n, ns, isym, ilog, + isize, irlin, ibar, ylb, yub, xlb, xinc) ierr = 1 test of mvpc test number 7 n = 0 + / m = 0 / iym = -1 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mvpc (ym, n, m, iym, ns, ilog, + isize, ylb, yub, xlb, xinc) ierr = 1 test of mvpmc test number 7 n = 0 + / m = 0 / iym = -1 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpmc ------------------------------------- the input value of n is 0. the value of the argument n must be greater than or equal to one . the input value of m is 0. the value of the argument m must be greater than or equal to one . the correct form of the call statement is call mvpmc (ym, ymmiss, n, m, iym, ns, ilog, + isize, ylb, yub, xlb, xinc) ierr = 1 test of mvpc test number 8 n = 12 + / m = 12 / iym = -1 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = -1.0000 / yub = 0.0000 / xlb = -1.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpc ------------------------------------- the input value of iym is -1. the first dimension of ym , as indicated by the argument iym , must be greater than or equal to n . the input value of ylb is -1.0000000 . the value of the argument ylb must be greater than .00000000000000 . the input value of yub is .00000000 . the value of the argument yub must be greater than .00000000000000 . the correct form of the call statement is call mvpc (ym, n, m, iym, ns, ilog, + isize, ylb, yub, xlb, xinc) ierr = 1 test of mvpmc test number 8 n = 12 + / m = 12 / iym = -1 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = -1.0000 / yub = 0.0000 / xlb = -1.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpmc ------------------------------------- the input value of iym is -1. the first dimension of ym , as indicated by the argument iym , must be greater than or equal to n . the input value of ylb is -1.0000000 . the value of the argument ylb must be greater than .00000000000000 . the input value of yub is .00000000 . the value of the argument yub must be greater than .00000000000000 . the correct form of the call statement is call mvpmc (ym, ymmiss, n, m, iym, ns, ilog, + isize, ylb, yub, xlb, xinc) ierr = 1 test of mvpmc test number 9 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = -1.0000 / yub = 0.0000 / xlb = -1.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpmc ------------------------------------- the number of values in array ym less than or equal to 0.0000000E+00 is 1. the values in the array ym must all be greater than 0.0000000E+00. the input value of ylb is -1.0000000 . the value of the argument ylb must be greater than .00000000000000 . the input value of yub is .00000000 . the value of the argument yub must be greater than .00000000000000 . the correct form of the call statement is call mvpmc (ym, ymmiss, n, m, iym, ns, ilog, + isize, ylb, yub, xlb, xinc) ierr = 1 test of mvpmc test number 10 n = 12 + / m = 12 / iym = 12 + / ns = 1 + / ilog = 22 isize= 22 / irlin= -1 / ibar = -1 + / ylb = 0.0000 / yub = 0.0000 / xlb = 0.0000 + / xinc = 0.0000 starpac 2.08s (03/15/90) +****************** * error messages * ****************** error checking for subroutine mvpmc ------------------------------------- no non-missing plot coordinates were found. the plot has been suppressed. the correct form of the call statement is call mvpmc (ym, ymmiss, n, m, iym, ns, ilog, + isize, ylb, yub, xlb, xinc) ierr = 1 1*ch1 simple test of pp starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.7418 - + + - i i i i i i i i 0.7271 - + - i + i i + + i i i i i 0.7125 - - i i i + i i i i + + i 0.6978 - + - i i i i i i i i 0.6831 - - i i i i i i i i 0.6684 - - i i i + i i i i i 0.6538 - + - i i i i i ++ i i + + + + i 0.6391 - + + + - i + + + + + + i i + + + ++ + + i i + + ++ + ++ + + + i i + + + + + i 0.6244 - + + + + + + + - i + + + +++ + i i + ++ + + i i + + + i i + +++ + i 0.6098 - ++ + ++ - i + i i i i i i i 0.5951 - ++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 9.3000 17.6000 25.9000 34.2000 42.5000 50.8000 59.1000 67.4000 75.7000 84.0000 the value of ierr is 0 1simple test of stat starpac 2.08s (03/15/90) +statistical analysis n = 84 frequency distribution (1-6) 5 25 35 8 1 0 0 4 4 2 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 6.3734055E-01 wtd standard deviation = 3.2405213E-02 weighted mean = 6.3734055E-01 weighted s.d. of mean = 3.5356984E-03 median = 6.2915003E-01 range = 1.4670002E-01 mid-range = 6.6845000E-01 mean deviation = 2.1076450E-02 25 pct unwtd trimmed mean= 6.2885946E-01 variance = 1.0500979E-03 25 pct wtd trimmed mean = 6.2885946E-01 coef. of. var. (percent) = 5.0844421E+00 a two-sided 95 pct confidence interval for mean is 6.3030815E-01 to 6.4437294E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 2.8136952E-02 to 3.8211718E-02 (2-7) linear trend statistics (5-1) other statistics slope = -2.4737162E-04 minimum = 5.9509999E-01 s.d. of slope = 1.4414075E-04 maximum = 7.4180001E-01 slope/s.d. of slope = t = -1.7161810E+00 beta one = 3.7288008E+00 prob exceeding abs value of obs t = 0.090 beta two = 5.9283767E+00 wtd sum of values = 5.3536606E+01 wtd sum of squares = 3.4208202E+01 tests for non-randomness wtd sum of dev squared = 8.7158121E-02 students t = 1.8025874E+02 hno. of runs up and down = 47 wtd sum absolute values = 5.3536606E+01 expected no. of runs = 55.7 wtd ave absolute values = 6.3734055E-01 s.d. of no. of runs = 3.82 mean sq successive diff = 3.6382340E-04 mean sq succ diff/var = 0.346 deviations from wtd mean no. of + signs = 22 no. of - signs = 62 no. of runs = 14 expected no. of runs= 33.5 s.d. of runs = 3.51 diff./s.d. of runs = -5.550 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1*ch2 simple test of mpp starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 622.0000 - l - i i i l i i i i i 570.2000 - - i k i i k i i l i i i 518.4000 - - i j l i i i i j i i l k i 466.6000 - i i k - i l l i i i i j l i i k i i 414.8000 - l l h - i k h 2 k k i i l k l i i i i h i 363.0000 - k j j g j k - i i 2 i h i i j k g i i i 2 i i j h h i 311.2000 - i h g g j - i i f h i h i i f i i h h g i i g g g e g h i 259.4000 - f e f - i i i g 2 e f e d e g i i g f e d f f i i d i 207.6000 - f d e f - i e e d c c 2 i i f d c d i i d c d c c 2 i i d c b b c c i 155.8000 - b - i c c b b a a c i i a b a a b b i i b a 2 i i 2 a a b a i 104.0000 - a - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 2.1000 3.2000 4.3000 5.4000 6.5000 7.6000 8.7000 9.8000 10.9000 12.0000 the value of ierr is 0 1*ch3 simple test of nrand the value of ierr is 0 generated results = -0.43870595E+00 0.13753282E+01 0.15124143E+01-0.78124404E+00 0.28223589E+00 -0.10907031E+01 0.66842330E+00 0.21530523E+01 0.91798043E+00 0.18341726E+01 -0.97279960E+00-0.11837106E+00-0.49941701E+00 0.54132605E+00 0.98725259E+00 0.61203074E+00 0.28811760E+01-0.80256443E-02-0.12379875E+01 0.18274617E+01 -0.28058094E+00 0.32195526E+00-0.51617473E+00 0.12058322E+01-0.24248886E+01 -0.13785406E+01 0.90864003E+00 0.20606978E+01-0.12210104E+01 0.56404579E+00 0.41296285E+00 0.26889676E+00-0.34880674E+00 0.91630095E+00 0.39474002E+00 -0.11695906E+01 0.11614258E+00 0.10934627E+01-0.20253827E+00-0.63061959E+00 0.86215001E+00 0.23011737E+00-0.26915628E+00-0.11060549E+01-0.10562941E+01 0.58346617E+00-0.24894953E+00 0.10645722E+01-0.26259449E+00-0.10540226E+01 1simple test of nrandc the value of ierr is 0 generated results = 0.37806470E+01 0.46876640E+01 0.47562070E+01 0.36093779E+01 0.41411180E+01 0.34546485E+01 0.43342118E+01 0.50765262E+01 0.44589901E+01 0.49170861E+01 0.35136001E+01 0.39408145E+01 0.37502916E+01 0.42706633E+01 0.44936261E+01 0.43060155E+01 0.54405880E+01 0.39959872E+01 0.33810062E+01 0.49137306E+01 0.38597095E+01 0.41609778E+01 0.37419126E+01 0.46029162E+01 0.27875557E+01 0.33107297E+01 0.44543200E+01 0.50303488E+01 0.33894949E+01 0.42820230E+01 0.42064815E+01 0.41344485E+01 0.38255966E+01 0.44581504E+01 0.41973701E+01 0.34152048E+01 0.40580711E+01 0.45467315E+01 0.38987308E+01 0.36846902E+01 0.44310751E+01 0.41150589E+01 0.38654218E+01 0.34469726E+01 0.34718530E+01 0.42917333E+01 0.38755252E+01 0.45322862E+01 0.38687027E+01 0.34729886E+01 1mvp display of generated results starpac 2.08s (03/15/90) -2.4249 -1.6383 -0.8518 -0.0652 0.7213 1.5078 2.2944 3.0809 3.8675 4.6540 5.4406 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i a b i 2.0000 i a b i 3.0000 i a b i 4.0000 i a b i 5.0000 i a b i 6.0000 i a b i 7.0000 i a b i 8.0000 i a b i 9.0000 i a b i 10.000 i a b i 11.000 i a b i 12.000 i a b i 13.000 i a b i 14.000 i a b i 15.000 i a b i 16.000 i a b i 17.000 i a bi 18.000 i a b i 19.000 i a b i 20.000 i a b i 21.000 i a b i 22.000 i a b i 23.000 i a b i 24.000 i a b i 25.000 ia b i 26.000 i a b i 27.000 i a b i 28.000 i a b i 29.000 i a b i 30.000 i a b i 31.000 i a b i 32.000 i a b i 33.000 i a b i 34.000 i a b i 35.000 i a b i 36.000 i a b i 37.000 i a b i 38.000 i a b i 39.000 i a b i 40.000 i a b i 41.000 i a b i 42.000 i a b i 43.000 i a b i 44.000 i a b i 45.000 i a b i 46.000 i a b i 47.000 i a b i 48.000 i a b i 49.000 i a b i 50.000 i a b i 1*ch4 simple test of hist starpac 2.08s (03/15/90) histogram number of observations = 39 minimum observation = -5.00000000E-01 maximum observation = 1.89999998E+00 histogram lower bound = -5.00000000E-01 histogram upper bound = 1.89999998E+00 number of cells = 9 observations used = 39 25 pct trimmed mean = 4.23809499E-01 min. observation used = -5.00000000E-01 standard deviation = 5.06689370E-01 max. observation used = 1.89999998E+00 mean dev./std. dev. = 7.99040318E-01 mean value = 4.10256386E-01 sqrt(beta one) = 3.10646802E-01 median value = 5.00000000E-01 beta two = 3.33793283E+00 for a normal distribution, the values (mean deviation/standard deviation), sqrt(beta one), and beta two are approximately 0.8, 0.0 and 3.0, respectively. to test the null hypothesis of normality, see tables of critical values pp. 207-208, biometrika tables for statisticians, vol. 1. see pp. 67-68 for a discussion of these tests. hinterval cum. 1-cum. cell no. number of observations hmid point fract. fract. fract. obs. + 0 10 20 30 40 50 ------------------------------------------ +---------+---------+---------+---------+---------+ -3.666666E-01 0.128 1.000 0.128 5 +++++ -9.999996E-02 0.256 0.872 0.128 5 +++++ 1.666667E-01 0.436 0.744 0.179 7 +++++++ 4.333334E-01 0.564 0.564 0.128 5 +++++ 7.000000E-01 0.846 0.436 0.282 11 +++++++++++ 9.666667E-01 0.974 0.154 0.128 5 +++++ 1.233333E+00 0.974 0.026 0.000 0 1.500000E+00 0.974 0.026 0.000 0 1.766667E+00 1.000 0.026 0.026 1 + the value of ierr is 0 1*ch5 simple test of stat starpac 2.08s (03/15/90) +statistical analysis n = 39 frequency distribution (1-6) 5 3 8 3 11 3 5 0 0 1 measures of location (2-2) measures of dispersion (2-6) unweighted mean = 4.1025639E-01 wtd standard deviation = 5.0668937E-01 weighted mean = 4.1025639E-01 weighted s.d. of mean = 8.1135236E-02 median = 5.0000000E-01 range = 2.4000001E+00 mid-range = 6.9999999E-01 mean deviation = 4.0486524E-01 25 pct unwtd trimmed mean= 4.2380950E-01 variance = 2.5673413E-01 25 pct wtd trimmed mean = 4.2380950E-01 coef. of. var. (percent) = 1.2350555E+02 a two-sided 95 pct confidence interval for mean is 2.4600610E-01 to 5.7450664E-01 (2-2) a two-sided 95 pct confidence interval for s.d. is 4.1409200E-01 to 6.5301019E-01 (2-7) linear trend statistics (5-1) other statistics slope = -4.0080948E-03 minimum = -5.0000000E-01 s.d. of slope = 7.2760493E-03 maximum = 1.9000000E+00 slope/s.d. of slope = t = -5.5086142E-01 beta one = 9.6501440E-02 prob exceeding abs value of obs t = 0.585 beta two = 3.3379328E+00 wtd sum of values = 1.5999999E+01 wtd sum of squares = 1.6320000E+01 tests for non-randomness wtd sum of dev squared = 9.7558975E+00 students t = 5.0564518E+00 hno. of runs up and down = 23 wtd sum absolute values = 2.0600000E+01 expected no. of runs = 25.7 wtd ave absolute values = 5.2820516E-01 s.d. of no. of runs = 2.57 mean sq successive diff = 2.8289476E-01 mean sq succ diff/var = 1.102 deviations from wtd mean no. of + signs = 20 no. of - signs = 19 no. of runs = 8 expected no. of runs= 20.5 s.d. of runs = 3.08 diff./s.d. of runs = -4.056 note - items in parentheses refer to page number in nbs handbook 91 (natrella, 1966) the value of ierr is 0 1*ch6 simple test of aov1 starpac 2.08s (03/15/90) Analysis of Variance group numbers have been assigned according to tag values given, where the smallest tag greater than zero has been assigned group number 1, the next smallest, group number 2, etc. tags <= zero have not been included in analysis. number of values excluded from analysis is 0 source d.f. sum of squares mean squares f ratio f prob. between groups 2 5.651040E+02 2.825520E+02 0.261E+02 0.000 slope 1 6.450886E+01 6.450886E+01 0.141E+01 0.257 devs. about line 1 5.005952E+02 5.005952E+02 0.462E+02 0.000 within groups 13 1.408333E+02 1.083333E+01 total 15 7.059375E+02 kruskal-wallis rank test for difference between group means * h = 0.114E+02, f prob = 0.000 (approx.) estimates sum of tag no. mean within s.d. s.d. of mean minimum maximum ranks 95pct conf int for mean 1.000000E+00 6 7.81667E+01+ 3.86868E+00+ 1.57938E+00 7.20000E+01 8.30000E+01 76.0 7.41067E+01 to 8.22266E+01 2.000000E+00 5 6.40000E+01- 3.00000E+00 1.34164E+00 6.10000E+01 6.70000E+01 15.0 6.02750E+01 to 6.77250E+01 3.000000E+00 5 7.40000E+01 2.73861E+00- 1.22474E+00 7.10000E+01 7.80000E+01 45.0 7.05996E+01 to 7.74004E+01 total 16 7.24375E+01 6.10000E+01 8.30000E+01 fixed effects model 3.29140E+00 8.22851E-01 7.06594E+01 to 7.42156E+01 random effects model 7.29576E+00 4.21221E+00 5.43138E+01 to 9.05612E+01 ungrouped data 6.86021E+00 1.71505E+00 6.87815E+01 to 7.60935E+01 pairwise multiple comparison of means. the means hare put in increasing order in groups separated by *****. a mean is adjudged non-significantly different from any mean in the same group and significantly different at the .05 level from any mean in another group. ***** ***** indicates adjacent groups have no common mean. newman-keuls technique, hartley modification. (approximate if group numbers are unequal.) 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, scheffe technique. 6.40000E+01, ***** ***** 7.40000E+01, 7.81667E+01, tests for homogeneity of variances. cochrans c = max. variance/sum(variances) = 0.4756, p = 0.395 (approx.) bartlett-box f = 0.269, p = 0.764 maximum variance / minimum variance = 1.9956 model ii - components of variance. estimate of between component 5.114704E+01 the value of ierr is 0 1*ch7 simple test of corr starpac 2.08s (03/15/90) correlation analysis for 4 variables with 9 observations correlation matrix - standard deviations are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 4.1764550 2 .68374240 .74628705 3 -.61596984 -.17249313 7.9279222 4 .80175227 .76795024 -.62874597 12.645156 significance levels of simple correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 0.42269588E-01 .00000000 3 0.77358723E-01 .65719634 .00000000 4 0.93530416E-02 0.15660405E-01 0.69711268E-01 .00000000 partial correlation coefficients with 2 remaining variables fixed column 1 2 3 4 1 1.0000000 2 .43170992 1.0000000 3 -.45663619 .69716984 1.0000000 4 .10539015 .72681993 -.64778912 1.0000000 significance levels of partial correlation coefficients (assuming normality) column 1 2 3 4 1 .00000000 2 .33344835 .00000000 3 .30301750 0.81676781E-01 .00000000 4 .82207656 0.64249456E-01 .11565989 .00000000 spearman rank correlation coefficients (adjusted for ties) column 1 2 3 4 1 1.0000000 2 .61088401 1.0000000 3 -.56666666 -.12552410 1.0000000 4 .68333334 .60251576 -.71666664 1.0000000 significance level of quadratic fit over linear fit based on f ratio with 1 and 6 degrees of freedom (for example, 0.1704 is the significance level of the quadratic term when column 2 is fitted to column 1) column 1 2 3 4 1 1.0000000 .40443492 .94935948 .85222399 2 .17035985 1.0000000 .80987620 .93774122 3 .71654016 .56763339 1.0000000 .84988511 4 .15654355 .59975278 .36811483 1.0000000 confidence intervals for simple correlation coefficients (using fisher transformation) 95 per chent limits below diagonal, 99 per cent limits above diagonal column 1 2 3 4 1 99.000000 .95517081 .32129729 .97349304 95.000000 -.21219550 -.94361627 0.51874168E-01 2 .92694801 99.000000 .70508558 .96846092 0.35941135E-01 95.000000 -.84136051 -.36249600E-01 3 0.81486106E-01 .55523425 99.000000 .30247203 -.90845978 -.75062579 95.000000 -.94585729 4 .95654887 .94838423 0.60737621E-01 99.000000 .29437226 .21190052 -.91203487 95.000000 the value of ierr is 0 1*ch8 simple test of lls starpac 2.08s (03/15/90) +*************************************************************** * linear least squares estimation with user-specified model * *************************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 80.000000 27.000000 89.000000 42.000000 38.765354 1.7810575 3.2346458 1.19 2 80.000000 27.000000 88.000000 37.000000 38.917477 1.8285162 -1.9174767 -0.72 3 75.000000 25.000000 90.000000 37.000000 32.444466 1.3552970 4.5555344 1.55 4 62.000000 24.000000 87.000000 28.000000 22.302227 1.1626698 5.6977730 1.88 5 62.000000 22.000000 87.000000 18.000000 19.711655 .74118036 -1.7116547 -0.54 6 62.000000 23.000000 87.000000 18.000000 21.006939 .90285313 -3.0069389 -0.97 7 62.000000 24.000000 93.000000 19.000000 21.389492 1.5186298 -2.3894920 -0.83 8 62.000000 24.000000 93.000000 20.000000 21.389492 1.5186298 -1.3894920 -0.48 9 58.000000 23.000000 87.000000 15.000000 18.144381 1.2143582 -3.1443806 -1.05 10 58.000000 18.000000 80.000000 14.000000 12.732807 1.4506369 1.2671928 0.44 11 58.000000 18.000000 89.000000 14.000000 11.363705 1.2770505 2.6362953 0.88 12 58.000000 17.000000 88.000000 13.000000 10.220541 1.5114741 2.7794590 0.97 13 58.000000 18.000000 82.000000 11.000000 12.428562 1.2873038 -1.4285622 -0.48 14 58.000000 19.000000 93.000000 12.000000 12.050501 1.4714425 -0.50500870E-01 -0.02 15 50.000000 18.000000 89.000000 8.0000000 5.6385841 1.4154767 2.3614159 0.81 16 50.000000 18.000000 86.000000 7.0000000 6.0949516 1.1742305 .90504837 0.30 17 50.000000 19.000000 72.000000 8.0000000 9.5199537 2.0821376 -1.5199537 -0.61 18 50.000000 19.000000 79.000000 8.0000000 8.4550953 1.2997496 -.45509529 -0.15 19 50.000000 20.000000 80.000000 9.0000000 9.5982590 1.3549962 -.59825897 -0.20 20 56.000000 20.000000 82.000000 15.000000 13.587852 .91842782 1.4121475 0.45 21 70.000000 20.000000 91.000000 15.000000 22.237709 1.7300640 -7.2377090 -2.64 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - * - - * - - * - - * - -* - - * - 0.75+ * * * + 0.75+ * ** + - * - - * - - * * - - * * - - * - - * - - * * - - ** - -0.75+ * * * * * + -0.75+ * * * * * + - * * * - - * ** - - - - - - - - - - - - - -2.25+ + -2.25+ + - - - - - *- - * - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 11.0 21.0 2.819 30.60 58.38 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----***--+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - *** - - - - * - - - - * - - - - ******* - - - 6+ ******* + 2.25+ + - ** - - - - **** - - * - - *** - - * - - **** - - * - 11+ *** + 0.75+ ** * + - ****** - - * - - * - - ** - - ** - - * - - *** - - ** - 16+ ** + -0.75+ ** *** + - ***** - - * * * - - ****** - - - - *** - - - - **** - - - 21+ + -2.25+ + - - - - - - - * - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 6448.76172 6448.76172 1 103.462013 20 613.035 0.000 198.185 0.000 2 1750.12561 4099.44385 2 16.7955017 19 166.371 0.000 59.9023 0.000 3 130.318787 2776.40210 3 10.4886522 18 12.3884 0.003 6.66789 0.007 4 9.96581554 2084.79297 4 10.5194073 17 .947374 0.344 .947375 0.344 residual 178.8299 17 total 8518.000 21 1 starpac 2.08s (03/15/90) +linear least squares estimation with user-specified model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 4 1 141.51476 .28758714 -.65179449 -1.6763208 2 .17926329 0.18186722E-01 -.36510661E-01 -.71435180E-02 3 -.14887902 -.73564130 .13544181 0.10475095E-04 4 -.90160000 -.33891633 0.18211227E-03 0.24427824E-01 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 -39.9196548 11.8959970 -3.356 0.004 6.5 -50.3588181 5.13832760 -9.801 0.000 2 .715639889 .134858161 5.307 0.000 6.4 .671154141 .126691043 5.298 0.000 3 1.29528618 .368024200 3.520 0.003 6.9 1.29535139 .367485434 3.525 0.002 4 -.152122542 .156294033 -.9733 0.344 6.1 residual standard deviation 3.243364 3.238616 based on degrees of freedom 21 - 4 = 17 + 21 - 3 = 18 multiple correlation coefficient squared 0.9136 approximate condition number 1047.371 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. the value of ierr is 0 1simple test of llsp starpac 2.08s (03/15/90) +*********************************************************** * linear least squares estimation with polynomial model * *********************************************************** results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 .00000000 12.000000 12.184849 .41999990 -.18484879 -0.61 2 1.0000000 10.500000 10.521213 .27284431 -0.21212578E-01 -0.05 3 2.0000000 10.000000 9.2233772 .23159598 .77662277 1.68 4 3.0000000 8.0000000 8.2913427 .24891697 -.29134274 -0.64 5 4.0000000 7.0000000 7.7251091 .26115480 -.72510910 -1.63 6 5.0000000 8.0000000 7.5246763 .24891689 .47532368 1.05 7 6.0000000 7.5000000 7.6900444 .23159629 -.19004440 -0.41 8 7.0000000 8.5000000 8.2212124 .27284411 .27878761 0.64 9 8.0000000 9.0000000 9.1181822 .41999984 -.11818218 -0.39 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - * - - * - - - - - - - - - 0.75+ * + 0.75+ * + - * - - * - - - - - - * - - * - - * *- - * * - -0.75+* * + -0.75+ * * + - - - - - - - - - * - - * - - - - - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 5.0 9.0 3.762 11.02 18.28 autocorrelation function of residuals normal probability plot of std res 1++---------+------*********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ********* - - - - **** - - - - * - - - - *** - - - 6+ * + 2.25+ + - * - - - - * - - * - - - - - - - - - 11+ + 0.75+ * + - - - * - - - - - - - - * - - - - * * - 16+ + -0.75+ * * + - - - - - - - - - - - * - - - - - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued analysis of variance -dependent on order variables are entered unless vectors are orthogonal- par sum of squares ------ par=0 ------ ------ pars=0 ----- index red due to par cum ms red df(msred) cum res ms df(rms) f prob(f) f prob(f) 1 720.027832 720.027832 1 2.59027338 8 2696.46 0.000 922.686 0.000 2 8.81667042 364.422241 2 1.70078790 7 33.0178 0.001 35.8016 0.000 3 10.3033504 246.382614 3 .267027467 6 38.5854 0.001 38.5854 0.001 residual 1.602165 6 total 740.7500 9 1 starpac 2.08s (03/15/90) +linear least squares estimation with polynomial model, continued variance-covariance and correlation matrices of the estimated parameters ------------------------------------------------------------------------ - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 1 .17639992 -.82535736E-01 0.80917384E-02 2 -.80268759 0.59936672E-01 -.69357767E-02 3 .65431988 -.96215767 0.86697208E-03 ------------------------- estimates from fit ------------------------ + ---- estimates from fit omitting last predictor value ---- estimated parameter sd of par t(par=0) prob(t) acc dig* + estimated parameter sd of par t(par=0) prob(t) 1 12.1848488 .419999897 29.01 0.000 6.9 10.4777775 .801574826 13.07 0.000 2 -1.84653652 .244819671 -7.542 0.000 6.9 -.383333325 .168364391 -2.277 0.057 3 .182900399 0.294443890E-01 6.212 0.001 6.8 residual standard deviation .5167470 1.304145 based on degrees of freedom 9 - 3 = 6 + 9 - 2 = 7 multiple correlation coefficient squared 0.9227 approximate condition number 118.0340 * the number of correctly computed digits in each parameter usually differs by less than 1 from the value given here. the value of ierr is 0 1*ch9 simple test of nls starpac 2.08s (03/15/90) +********************************************************************************** * nonlinear least squares estimation with numerically approximated derivatives * ********************************************************************************** summary of initial conditions ------------------------------ step size for observations failing step size selection criteria approximating * parameter starting value scale derivative count notes row number index fixed (par) (scale) (stp) f c 1 no .72500002 default 0.10000015E-01 0 2 no 4.0000000 default 0.12264252E-02 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 1 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3381E-01 0.4572E-02 .6894 .7111 y 0.1787E-01 y current parameter values index 1 2 value .7679511 3.859573 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3285E-01 0.4317E-02 0.5565E-01 0.5565E-01 y 0.3357E-03 y current parameter values index 1 2 value .7688670 3.860392 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741233 0.22074621E-01 -0.36123276E-01 -1.48 2 1.4710000 3.4210000 3.4111581 0.16468925E-01 0.98419189E-02 0.35 3 1.4900000 3.5969999 3.5844142 0.15617380E-01 0.12585640E-01 0.44 4 1.5650001 4.3400002 4.3326435 0.14066310E-01 0.73566437E-02 0.25 5 1.6109999 4.8820000 4.8453059 0.16511230E-01 0.36694050E-01 1.29 6 1.6799999 5.6599998 5.6968317 0.26187044E-01 -0.36831856E-01 -1.86 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with numerically approximated derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 0.3335921E-03 -0.9340724E-03 2 -.9907541 0.2664487E-02 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76886702 0.18264502E-01 42.10 .71815658 .81957746 2 no 3.8603923 0.51618669E-01 74.79 3.7170758 4.0037088 residual sum of squares 0.4317312E-02 residual standard deviation 0.3285313E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.83128 the value of ierr is 0 1simple test of nlsd starpac 2.08s (03/15/90) +*********************************************************************** * nonlinear least squares estimation with user-supplied derivatives * *********************************************************************** summary of initial conditions ------------------------------ derivative parameter starting value scale assessment index fixed (par) (scale) 1 no .72500002 default ok 2 no 4.0000000 default ok number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value 1.309000 number of observations (n) 6 number of independent variables (m) 1 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values 0.1472E-01 residual standard deviation for input parameter values (rsd) 0.6067E-01 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3390E-01 0.4597E-02 .6877 .7109 y 0.1790E-01 y current parameter values index 1 2 value .7679852 3.859309 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3285E-01 0.4317E-02 0.6087E-01 0.6086E-01 y 0.3206E-03 y current parameter values index 1 2 value .7688590 3.860417 ***** parameter convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued results from least squares fit ------------------------------- dependent predicted std dev of std row predictor values variable value pred value residual res 1 1.3090000 2.1380000 2.1741149 0.22079054E-01 -0.36114931E-01 -1.48 2 1.4710000 3.4210000 3.4111543 0.16469598E-01 0.98457336E-02 0.35 3 1.4900000 3.5969999 3.5844114 0.15615326E-01 0.12588501E-01 0.44 4 1.5650001 4.3400002 4.3326454 0.14065785E-01 0.73547363E-02 0.25 5 1.6109999 4.8820000 4.8453116 0.16512049E-01 0.36688328E-01 1.29 6 1.6799999 5.6599998 5.6968441 0.26183691E-01 -0.36844254E-01 -1.86 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued std res vs row number std res vs predicted values 3.75++---------+---------+----+----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - - - - - - - - - - - - - - - - 2.25+ + 2.25+ + - - - - - - - - - - - - - * - - * - 0.75+ + 0.75+ + - - - - - * * * - - ** * - - - - - - - - - -0.75+ + -0.75+ + - - - - - - - - -* - - * - - *- - * - -2.25+ + -2.25+ + - - - - - - - - - - - - - - - - -3.75++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ 1.0 3.5 6.0 1.087 4.816 8.545 autocorrelation function of residuals normal probability plot of std res 1++---------+-------********----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - ** - - - - *** - - - - ********** - - - - ******** - - - 6+ + 2.25+ + - - - - - - - - - - - - - - - * - 11+ + 0.75+ + - - - - - - - * * * - - - - - - - - - 16+ + -0.75+ + - - - - - - - - - - - * - - - - * - 21+ + -2.25+ + - - - - - - - - - - - - - - - - 26++---------+---------+----+----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation with user-supplied derivatives, continued variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 1 0.3342289E-03 -0.9369369E-03 2 -.9907720 0.2675650E-02 estimates from least squares fit --------------------------------- approximate 95 percent confidence limits index fixed parameter sd of par ratio lower upper 1 no .76885897 0.18281927E-01 42.06 .71810019 .81961775 2 no 3.8604167 0.51726684E-01 74.63 3.7168002 4.0040331 residual sum of squares 0.4317321E-02 residual standard deviation 0.3285316E-01 based on degrees of freedom 6 - 2 = 4 approximate condition number 20.87503 the value of ierr is 0 1simple test of stpls starpac 2.08s (03/15/90) +********************************** * derivative step size selection * ********************************** step size for observations failing step size selection criteria parameter approximating * starting value scale derivative count notes row number(s) index (par) (scale) (stp) f c 1 .72500002 default 0.10000015E-01 0 2 4.0000000 default 0.12264252E-02 0 * notes. a plus (+) in the columns headed f or c has the following meaning. f - number of observations failing step size selection criteria exceeds number of exemptions allowed. c - high curvaturhe in the model is suspected as the cause of all failures noted. number of reliable digits in model results (neta) 6 proportion of observations exempted from selection criteria (exmpt) 0.1000 number of observations exempted from selection criteria 1 number of observations (n) 6 the value of ierr is 0 1simple test of dckls starpac 2.08s (03/15/90) +*********************** * derivative checking * *********************** * parameter derivative starting value scale assessment index (par) (scale) 1 .00000000 default incorrect 2 4.0000000 default questionable (1) * numbers in parentheses refer to the following notes. (1) user-supplied and approximated derivatives agree, but both are zero. recheck at another row. number of reliable digits in model results (neta) 6 number of digits in derivative checking agreement tolerance (ntau) 2 row number at which derivatives were checked (nrow) 1 -values of the independent variables at this row index 1 value 1.309000 number of observations (n) 6 the value of ierr is 3 1*ch10 simple test of dif (no output unless error found) the value of ierr is 0 1plot of original series starpac 2.08s (03/15/90) 4.6444 4.8232 5.0021 5.1810 5.3598 5.5387 5.7175 5.8964 6.0752 6.2541 6.4329 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 4.7185 2.0000 i + i 4.7707 3.0000 i + i 4.8828 4.0000 i + i 4.8598 5.0000 i + i 4.7958 6.0000 i + i 4.9053 7.0000 i + i 4.9972 8.0000 i + i 4.9972 9.0000 i + i 4.9127 10.000 i + i 4.7791 11.000 i+ i 4.6444 12.000 i + i 4.7707 13.000 i + i 4.7449 14.000 i + i 4.8363 15.000 i + i 4.9488 16.000 i + i 4.9053 17.000 i + i 4.8283 18.000 i + i 5.0039 19.000 i + i 5.1358 20.000 i + i 5.1358 21.000 i + i 5.0626 22.000 i + i 4.8903 23.000 i + i 4.7362 24.000 i + i 4.9416 25.000 i + i 4.9767 26.000 i + i 5.0106 27.000 i + i 5.1818 28.000 i + i 5.0938 29.000 i + i 5.1475 30.000 i + i 5.1818 31.000 i + i 5.2933 32.000 i + i 5.2933 33.000 i + i 5.2149 34.000 i + i 5.0876 35.000 i + i 4.9836 36.000 i + i 5.1120 37.000 i + i 5.1417 38.000 i + i 5.1930 39.000 i + i 5.2627 40.000 i + i 5.1985 41.000 i + i 5.2095 42.000 i + i 5.3845 43.000 i + i 5.4381 44.000 i + i 5.4889 45.000 i + i 5.3423 46.000 i + i 5.2523 47.000 i + i 5.1475 48.000 i + i 5.2679 49.000 i + i 5.2781 50.000 i + i 5.2781 51.000 i + i 5.4638 52.000 i + i 5.4596 53.000 i + i 5.4337 54.000 i + i 5.4931 55.000 i + i 5.5759 56.000 i + i 5.6058 57.000 i + i 5.4681 58.000 i + i 5.3519 59.000 i + i 5.1930 60.000 i + i 5.3033 61.000 i + i 5.3181 62.000 i + i 5.2364 63.000 i + i 5.4596 64.000 i + i 5.4250 65.000 i + i 5.4553 66.000 i + i 5.5759 67.000 i + i 5.7104 68.000 i + i 5.6802 69.000 i + i 5.5568 70.000 i + i 5.4337 71.000 i + i 5.3132 72.000 i + i 5.4337 73.000 i + i 5.4889 74.000 i + i 5.4510 75.000 i + i 5.5872 76.000 i + i 5.5947 77.000 i + i 5.5984 78.000 i + i 5.7526 79.000 i + i 5.8972 80.000 i + i 5.8493 81.000 i + i 5.7430 82.000 i + i 5.6131 83.000 i + i 5.4681 84.000 i + i 5.6276 85.000 i + i 5.6490 86.000 i + i 5.6240 87.000 i + i 5.7589 88.000 i + i 5.7462 89.000 i + i 5.7621 90.000 i + i 5.9243 91.000 i + i 6.0234 92.000 i + i 6.0039 93.000 i + i 5.8721 94.000 i + i 5.7236 95.000 i + i 5.6021 96.000 i + i 5.7236 97.000 i + i 5.7526 98.000 i + i 5.7071 99.000 i + i 5.8749 100.00 i + i 5.8522 101.00 i + i 5.8721 102.00 i + i 6.0450 103.00 i + i 6.1420 104.00 i + i 6.1463 105.00 i + i 6.0014 106.00 i + i 5.8493 107.00 i + i 5.7203 108.00 i + i 5.8171 109.00 i + i 5.8289 110.00 i + i 5.7621 111.00 i + i 5.8916 112.00 i + i 5.8522 113.00 i + i 5.8944 114.00 i + i 6.0753 115.00 i + i 6.1964 116.00 i + i 6.2246 117.00 i + i 6.0014 118.00 i + i 5.8833 119.00 i + i 5.7366 120.00 i + i 5.8201 121.00 i + i 5.8861 122.00 i + i 5.8348 123.00 i + i 6.0064 124.00 i + i 5.9814 125.00 i + i 6.0403 126.00 i + i 6.1570 127.00 i + i 6.3063 128.00 i + i 6.3261 129.00 i + i 6.1377 130.00 i + i 6.0088 131.00 i + i 5.8916 132.00 i + i 6.0039 133.00 i + i 6.0331 134.00 i + i 5.9687 135.00 i + i 6.0379 136.00 i + i 6.1334 137.00 i + i 6.1570 138.00 i + i 6.2823 139.00 i +i 6.4329 140.00 i + i 6.4069 141.00 i + i 6.2305 142.00 i + i 6.1334 143.00 i + i 5.9661 144.00 i + i 6.0684 the value of ierr is 0 1plot of differenced series starpac 2.08s (03/15/90) -0.2231 -0.1785 -0.1339 -0.0893 -0.0446 0.0000 0.0446 0.0893 0.1339 0.1785 0.2231 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + i 0.52186E-01 2.0000 i + i .11212 3.0000 i + i -0.22990E-01 4.0000 i + i -0.64022E-01 5.0000 i + i .10948 6.0000 i + i 0.91938E-01 7.0000 i + i .00000 8.0000 i + i -0.84558E-01 9.0000 i + i -.13353 10.000 i + i -.13473 11.000 i + i .12629 12.000 i + i -0.25753E-01 13.000 i + i 0.91350E-01 14.000 i + i .11248 15.000 i + i -0.43485E-01 16.000 i + i -0.76961E-01 17.000 i + i .17563 18.000 i + i .13185 19.000 i + i .00000 20.000 i + i -0.73204E-01 21.000 i + i -.17225 22.000 i + i -.15415 23.000 i + i .20544 24.000 i + i 0.35091E-01 25.000 i + i 0.33902E-01 26.000 i + i .17115 27.000 i + i -0.88034E-01 28.000 i + i 0.53744E-01 29.000 i + i 0.34289E-01 30.000 i + i .11152 31.000 i + i .00000 32.000 i + i -0.78369E-01 33.000 i + i -.12734 34.000 i + i -.10399 35.000 i + i .12838 36.000 i + i 0.29676E-01 37.000 i + i 0.51293E-01 38.000 i + i 0.69733E-01 39.000 i + i -0.64193E-01 40.000 i + i 0.10989E-01 41.000 i + i .17501 42.000 i + i 0.53584E-01 43.000 i + i 0.50858E-01 44.000 i + i -.14660 45.000 i + i -0.90061E-01 46.000 i + i -.10478 47.000 i + i .12036 48.000 i + i 0.10257E-01 49.000 i + i .00000 50.000 i + i .18572 51.000 i + i -0.42462E-02 52.000 i + i -0.25864E-01 53.000 i + i 0.59340E-01 54.000 i + i 0.82888E-01 55.000 i + i 0.29853E-01 56.000 i + i -.13774 57.000 i + i -.11620 58.000 i + i -.15890 59.000 i + i .11035 60.000 i + i 0.14815E-01 61.000 i + i -0.81678E-01 62.000 i +i .22314 63.000 i + i -0.34636E-01 64.000 i + i 0.30371E-01 65.000 i + i .12063 66.000 i + i .13448 67.000 i + i -0.30254E-01 68.000 i + i -.12334 69.000 i + i -.12311 70.000 i + i -.12052 71.000 i + i .12052 72.000 i + i 0.55216E-01 73.000 i + i -0.37899E-01 74.000 i + i .13621 75.000 i + i 0.74625E-02 76.000 i + i 0.37107E-02 77.000 i + i .15415 78.000 i + i .14458 79.000 i + i -0.47829E-01 80.000 i + i -.10632 81.000 i + i -.12988 82.000 i + i -.14507 83.000 i + i .15956 84.000 i + i 0.21353E-01 85.000 i + i -0.24957E-01 86.000 i + i .13488 87.000 i + i -0.12698E-01 88.000 i + i 0.15848E-01 89.000 i + i .16220 90.000 i + i 0.99192E-01 91.000 i + i -0.19560E-01 92.000 i + i -.13177 93.000 i + i -.14853 94.000 i + i -.12147 95.000 i + i .12147 96.000 i + i 0.28987E-01 97.000 i + i -0.45462E-01 98.000 i + i .16782 99.000 i + i -0.22728E-01 100.00 i + i 0.19916E-01 101.00 i + i .17289 102.00 i + i 0.97032E-01 103.00 i + i 0.42920E-02 104.00 i + i -.14491 105.00 i + i -.15209 106.00 i + i -.12901 107.00 i + i 0.96799E-01 108.00 i + i 0.11835E-01 109.00 i + i -0.66894E-01 110.00 i + i .12959 111.00 i + i -0.39442E-01 112.00 i + i 0.42201E-01 113.00 i + i .18094 114.00 i + i .12110 115.00 i + i 0.28114E-01 116.00 i+ i -.22314 117.00 i + i -.11809 118.00 i + i -.14675 119.00 i + i 0.83511E-01 120.00 i + i 0.66021E-01 121.00 i + i -0.51293E-01 122.00 i + i .17154 123.00 i + i -0.24939E-01 124.00 i + i 0.58840E-01 125.00 i + i .11672 126.00 i + i .14930 127.00 i + i 0.19874E-01 128.00 i + i -.18842 129.00 i + i -.12891 130.00 i + i -.11717 131.00 i + i .11224 132.00 i + i 0.29199E-01 133.00 i + i -0.64379E-01 134.00 i + i 0.69163E-01 135.00 i + i 0.95527E-01 136.00 i + i 0.23581E-01 137.00 i + i .12529 138.00 i + i .15067 139.00 i + i -0.26060E-01 140.00 i + i -.17640 141.00 i + i -0.97084E-01 142.00 i + i -.16725 143.00 i + i .10228 the value of ierr is 0 1simple test of gfarf starpac 2.08s (03/15/90) gain function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++ - i +++++++++++++ i i ++++++++ i i +++++++ i i +++++ i -1.8039 - +++++ - i ++++ i i +++ i i ++++ i i ++ i -3.6078 - +++ - i ++ i i ++ i i ++ i i ++ i -5.4117 - ++ - i + i i + i i ++ i i + i -7.2156 - + - i + i i + i i i i + i -9.0195 - + - i i i + i i + i i i -10.8234 - - i + i i i i + i i i -12.6273 - - i i i + i i i i i -14.4312 - - i i i + i i i i i -16.2351 - - i i i i i i i i -18.0390 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) phase function of 1 term autoregressive, or difference, filter -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 3.1416 - - i i i i i i i i 2.5133 - - i i i i i i i i 1.8850 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.6283 - - i i i i i i i i 0.0000 - +++ - i ++++++++ i i ++++++++ i i ++++++++ i i ++++++++ i -0.6283 - ++++++++ - i ++++++++ i i +++++++++ i i ++++++++ i i ++++++++ i -1.2566 - ++++++++ - i ++++++++ i i +++++++ i i + i i i -1.8850 - - i i i i i i i i -2.5133 - - i i i i i i i i -3.1416 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. the value of ierr is 0 1*ch11 simple test of demod starpac 2.08s (03/15/90) time series demodulation demodulation frequency is0.09090909 cutoff frequency is 0.04545455 the number of terms in the filter is 41 plot of amplitude of smoothed demodulated series location of mean is given by plot character m 19.7733 24.0727 28.3722 32.6717 36.9712 41.2706 45.5701 49.8696 54.1690 58.4685 62.7680 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i + m i 30.652 2.0000 i + m i 32.848 3.0000 i + m i 35.191 4.0000 i + m i 37.582 5.0000 i m + i 39.925 6.0000 i m + i 42.146 7.0000 i m + i 44.216 8.0000 i m + i 46.153 9.0000 i m + i 47.948 10.000 i m + i 49.529 11.000 i m + i 50.786 12.000 i m + i 51.633 13.000 i m + i 52.052 14.000 i m + i 52.071 15.000 i m + i 51.754 16.000 i m + i 51.178 17.000 i m + i 50.429 18.000 i m + i 49.599 19.000 i m + i 48.776 20.000 i m + i 48.011 21.000 i m + i 47.287 22.000 i m + i 46.515 23.000 i m + i 45.587 24.000 i m + i 44.441 25.000 i m + i 43.055 26.000 i m + i 41.462 27.000 i m+ i 39.721 28.000 i + m i 37.931 29.000 i + m i 36.245 30.000 i + m i 34.853 31.000 i + m i 33.877 32.000 i + m i 33.306 33.000 i + m i 33.020 34.000 i + m i 32.861 35.000 i + m i 32.690 36.000 i + m i 32.398 37.000 i + m i 31.913 38.000 i + m i 31.235 39.000 i + m i 30.500 40.000 i + m i 29.924 41.000 i + m i 29.721 42.000 i + m i 30.052 43.000 i + m i 30.936 44.000 i + m i 32.234 45.000 i + m i 33.719 46.000 i + m i 35.148 47.000 i + m i 36.375 48.000 i + m i 37.438 49.000 i +m i 38.520 50.000 i m + i 39.817 51.000 i m + i 41.445 52.000 i m + i 43.391 53.000 i m + i 45.493 54.000 i m + i 47.509 55.000 i m + i 49.231 56.000 i m + i 50.580 57.000 i m + i 51.625 58.000 i m + i 52.548 59.000 i m + i 53.573 60.000 i m + i 54.903 61.000 i m + i 56.621 62.000 i m + i 58.605 63.000 i m + i 60.549 64.000 i m + i 62.053 65.000 i m +i 62.768 66.000 i m + i 62.470 67.000 i m + i 61.053 68.000 i m + i 58.498 69.000 i m + i 54.856 70.000 i m + i 50.271 71.000 i m + i 44.992 72.000 i m+ i 39.363 73.000 i + m i 33.810 74.000 i + m i 28.877 75.000 i + m i 25.219 76.000 i + m i 23.400 77.000 i + m i 23.440 78.000 i + m i 24.701 79.000 i + m i 26.348 80.000 i + m i 27.725 81.000 i + m i 28.463 82.000 i + m i 28.457 83.000 i + m i 27.798 84.000 i + m i 26.694 85.000 i + m i 25.380 86.000 i + m i 24.053 87.000 i + m i 22.852 88.000 i + m i 21.861 89.000 i + m i 21.115 90.000 i + m i 20.585 91.000 i + m i 20.228 92.000 i + m i 19.997 93.000 i+ m i 19.856 94.000 i+ m i 19.780 95.000 i+ m i 19.773 96.000 i+ m i 19.881 97.000 i + m i 20.173 98.000 i + m i 20.663 99.000 i + m i 21.322 100.00 i + m i 22.121 101.00 i + m i 23.043 102.00 i + m i 24.074 103.00 i + m i 25.170 104.00 i + m i 26.225 105.00 i + m i 27.089 106.00 i + m i 27.651 107.00 i + m i 27.926 108.00 i + m i 28.094 109.00 i + m i 28.456 110.00 i + m i 29.302 111.00 i + m i 30.831 112.00 i + m i 33.091 113.00 i + m i 35.983 114.00 i m i 39.318 115.00 i m + i 42.865 116.00 i m + i 46.383 117.00 i m + i 49.656 118.00 i m + i 52.532 119.00 i m + i 54.905 120.00 i m + i 56.646 121.00 i m + i 57.612 122.00 i m + i 57.717 123.00 i m + i 56.979 124.00 i m + i 55.517 125.00 i m + i 53.529 126.00 i m + i 51.257 127.00 i m + i 48.940 128.00 i m + i 46.789 129.00 i m + i 44.991 130.00 i m + i 43.671 131.00 i m + i 42.836 132.00 i m + i 42.376 133.00 i m + i 42.143 134.00 i m + i 41.978 135.00 i m + i 41.754 136.00 i m + i 41.392 137.00 i m + i 40.857 138.00 i m + i 40.153 139.00 i m+ i 39.349 140.00 i +m i 38.626 141.00 i + m i 38.249 142.00 i + m i 38.471 143.00 i m+ i 39.409 144.00 i m + i 41.011 145.00 i m + i 43.087 146.00 i m + i 45.406 147.00 i m + i 47.742 148.00 i m + i 49.872 149.00 i m + i 51.604 150.00 i m + i 52.815 151.00 i m + i 53.486 152.00 i m + i 53.642 153.00 i m + i 53.276 154.00 i m + i 52.344 155.00 i m + i 50.815 156.00 i m + i 48.720 157.00 i m + i 46.180 158.00 i m + i 43.393 159.00 i m + i 40.591 160.00 i + m i 37.983 161.00 i + m i 35.722 162.00 i + m i 33.921 163.00 i + m i 32.684 164.00 i + m i 32.106 165.00 i + m i 32.232 166.00 i + m i 32.994 167.00 i + m i 34.207 168.00 i + m i 35.618 169.00 i + m i 36.974 170.00 i + m i 38.080 171.00 i +m i 38.791 172.00 i m i 39.002 173.00 i +m i 38.724 174.00 i + m i 38.055 175.00 i + m i 37.128 176.00 i + m i 36.047 177.00 i + m i 34.859 178.00 i + m i 33.594 179.00 i + m i 32.295 180.00 i + m i 31.060 181.00 i + m i 29.998 182.00 i + m i 29.205 183.00 i + m i 28.723 184.00 i + m i 28.547 185.00 i + m i 28.668 186.00 i + m i 29.109 187.00 i + m i 29.919 188.00 i + m i 31.119 189.00 i + m i 32.649 190.00 i + m i 34.381 191.00 i + m i 36.171 192.00 i + m i 37.886 193.00 i m+ i 39.408 194.00 i m + i 40.621 195.00 i m + i 41.430 196.00 i m + i 41.815 197.00 i m + i 41.858 198.00 i m + i 41.681 199.00 i m + i 41.339 200.00 i m + i 40.812 201.00 i m + i 40.056 202.00 i m i 39.052 203.00 i + m i 37.838 204.00 i + m i 36.522 205.00 i + m i 35.275 206.00 i + m i 34.325 207.00 i + m i 33.954 208.00 i + m i 34.416 209.00 i + m i 35.740 210.00 i + m i 37.716 211.00 i m + i 40.017 212.00 i m + i 42.334 213.00 i m + i 44.438 214.00 i m + i 46.206 215.00 i m + i 47.597 216.00 i m + i 48.667 217.00 i m + i 49.589 218.00 i m + i 50.609 219.00 i m + i 51.878 220.00 i m + i 53.421 221.00 i m + i 55.145 1 starpac 2.08s (03/15/90) plot of phase of smoothed demodulated series location of zero is given by plot character 0 -6.2832 -5.0265 -3.7699 -2.5133 -1.2566 0.0000 1.2566 2.5133 3.7699 5.0265 6.2832 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i b 0 a i 2.0000 i b 0 a i 3.0000 i b 0 a i 4.0000 i b 0 a i 5.0000 i b 0 a i 6.0000 i b 0 a i 7.0000 i b 0 a i 8.0000 i a 0 b i 9.0000 i a 0 b i 10.000 i a 0 b i 11.000 i a 0 b i 12.000 i a 0 b i 13.000 i a 0 b i 14.000 i a 0 b i 15.000 i a 0 b i 16.000 i a 0 b i 17.000 i a 0 b i 18.000 i a 0 b i 19.000 i a 0 b i 20.000 i a 0 b i 21.000 i a 0 b i 22.000 i a 0 b i 23.000 i a 0 b i 24.000 i a 0 b i 25.000 i a 0 b i 26.000 i a 0 b i 27.000 i b 0 a i 28.000 i b 0 a i 29.000 i b 0 a i 30.000 i b 0 a i 31.000 i b 0 a i 32.000 i b 0 a i 33.000 i b 0 a i 34.000 i b 0 a i 35.000 i b 0 a i 36.000 i b 0 a i 37.000 i b 0 a i 38.000 i b 0 a i 39.000 i b 0 a i 40.000 i b 0 a i 41.000 i a 0 b i 42.000 i a 0 b i 43.000 i a 0 b i 44.000 i a 0 b i 45.000 i a 0 b i 46.000 i a 0 b i 47.000 i a 0 b i 48.000 i a 0 b i 49.000 i a 0 b i 50.000 i a 0 b i 51.000 i a 0 b i 52.000 i a 0 b i 53.000 i a 0 b i 54.000 i a 0 b i 55.000 i a 0 b i 56.000 i a 0 b i 57.000 i a 0 b i 58.000 i a 0 b i 59.000 i a 0 b i 60.000 i a 0 b i 61.000 i a 0 b i 62.000 i a 0 b i 63.000 i a 0 b i 64.000 i a 0 b i 65.000 i a 0 b i 66.000 i a 0 b i 67.000 i a 0 b i 68.000 i a 0 b i 69.000 i a 0 b i 70.000 i a 0 b i 71.000 i a 0 b i 72.000 i a 0 b i 73.000 i a 0 b i 74.000 i a 0 b i 75.000 i a 0 b i 76.000 i a 0 b i 77.000 i a 0 b i 78.000 i a 0 b i 79.000 i a 0 b i 80.000 i a 0 b i 81.000 i a 0 b i 82.000 i a 0 b i 83.000 i a 0 b i 84.000 i a 0 b i 85.000 i a 0 b i 86.000 i a 0 b i 87.000 i a 0 b i 88.000 i a 0 b i 89.000 i a 0 b i 90.000 i a 0 b i 91.000 i b 0 a i 92.000 i b 0 a i 93.000 i b 0 a i 94.000 i b 0 a i 95.000 i b 0 a i 96.000 i b 0 a i 97.000 i b 0 a i 98.000 i b 0 a i 99.000 i b 0 a i 100.00 i b 0 a i 101.00 i b 0 a i 102.00 i b 0 a i 103.00 i b 0 a i 104.00 i b 0 a i 105.00 i b 0 a i 106.00 i b 0 a i 107.00 i b 0 a i 108.00 i b 0 a i 109.00 i b 0 a i 110.00 i b 0 a i 111.00 i b 0 a i 112.00 i b 0 a i 113.00 i b 0 a i 114.00 i b 0 a i 115.00 i b 0 a i 116.00 i b 0 a i 117.00 i b 0 a i 118.00 i b 0 a i 119.00 i a 0 b i 120.00 i a 0 b i 121.00 i a 0 b i 122.00 i a 0 b i 123.00 i a 0 b i 124.00 i a 0 b i 125.00 i a 0 b i 126.00 i a 0 b i 127.00 i a 0 b i 128.00 i a 0 b i 129.00 i b 0 a i 130.00 i b 0 a i 131.00 i b 0 a i 132.00 i b 0 a i 133.00 i b 0 a i 134.00 i b 0 a i 135.00 i b 0 a i 136.00 i b 0 a i 137.00 i b 0 a i 138.00 i b 0 a i 139.00 i b 0 a i 140.00 i b 0 a i 141.00 i b 0 a i 142.00 i b 0 a i 143.00 i b 0 a i 144.00 i b 0 a i 145.00 i b 0 a i 146.00 i b 0 a i 147.00 i b 0 a i 148.00 i b 0 a i 149.00 i b 0 a i 150.00 i b 0 a i 151.00 i b 0 a i 152.00 i b 0 a i 153.00 i b 0 a i 154.00 i b 0 a i 155.00 i b 0 a i 156.00 i b 0 a i 157.00 i b 0 a i 158.00 i b 0 a i 159.00 i b 0 a i 160.00 i b 0 a i 161.00 i b 0 a i 162.00 i b 0 a i 163.00 i b 0 a i 164.00 i b 0 a i 165.00 i b 0 a i 166.00 i b 0 a i 167.00 i b 0 a i 168.00 i b 0 a i 169.00 i b 0 a i 170.00 i b 0 a i 171.00 i b 0 a i 172.00 i b 0 a i 173.00 i b 0 a i 174.00 i b 0 a i 175.00 i b 0 a i 176.00 i b 0 a i 177.00 i b 0 a i 178.00 i b 0 a i 179.00 i b 0 a i 180.00 i b 0 a i 181.00 i b 0 a i 182.00 i b 0 a i 183.00 i b 0 a i 184.00 i b 0 a i 185.00 i b 0 a i 186.00 i b 0 a i 187.00 i b 0 a i 188.00 i b 0 a i 189.00 i b 0 a i 190.00 i b 0 a i 191.00 i b 0 a i 192.00 i b 0 a i 193.00 i b 0 a i 194.00 i b 0 a i 195.00 i b 0 a i 196.00 i b 0 a i 197.00 i b 0 a i 198.00 i b 0 a i 199.00 i b 0 a i 200.00 i b 0 a i 201.00 i b 0 a i 202.00 i b 0 a i 203.00 i b 0 a i 204.00 i b 0 a i 205.00 i b 0 a i 206.00 i b 0 a i 207.00 i b 0 a i 208.00 i b 0 a i 209.00 i b 0 a i 210.00 i b 0 a i 211.00 i b 0 a i 212.00 i b 0 a i 213.00 i b 0 a i 214.00 i b 0 a i 215.00 i b 0 a i 216.00 i b 0 a i 217.00 i b 0 a i 218.00 i b 0 a i 219.00 i b 0 a i 220.00 i b 0 a i 221.00 i b 0 a i the value of ierr is 0 1*ch12 simple test of acf starpac 2.08s (03/15/90) autocorrelation analysis average of the series = .1450000 standard deviation of the series = 1.277758 number of time points = 100 largest lag value used = 33 autocorrelation function estimate (acf) lag 1 2 3 4 5 6 7 8 9 10 11 12 acf 0.54 0.34 0.22 0.26 0.27 0.15 0.06 0.03 0.06 0.02 -0.04 -0.11 standard error 0.10 0.12 0.13 0.13 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 lag 13 14 15 16 17 18 19 20 21 22 23 24 acf -0.14 -0.10 -0.10 -0.11 -0.12 -0.07 0.03 0.04 0.02 -0.03 0.02 0.06 standard error 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.13 0.13 lag 25 26 27 28 29 30 31 32 33 acf 0.05 -0.03 -0.04 -0.06 -0.06 -0.03 -0.09 -0.16 -0.17 standard error 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 the chi square test statistic of the null hypothesis of white noise = 79.54 degrees of freedom = 33 observed significance level = 0.0000 1 starpac 2.08s (03/15/90) autocorrelation function estimate (acf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++++++++++++++++ i .33597 3.0000 i ++++++++++++ i .21955 4.0000 i ++++++++++++++ i .26190 5.0000 i +++++++++++++++ i .27456 6.0000 i +++++++++ i .15137 7.0000 i ++++ i 0.56358E-01 8.0000 i +++ i 0.30065E-01 9.0000 i ++++ i 0.58569E-01 10.000 i ++ i 0.17475E-01 11.000 i +++ i -0.35159E-01 12.000 i +++++++ i -.11314 13.000 i ++++++++ i -.13858 14.000 i ++++++ i -.10145 15.000 i ++++++ i -.10083 16.000 i ++++++ i -.10666 17.000 i +++++++ i -.12337 18.000 i ++++ i -0.65475E-01 19.000 i ++ i 0.26352E-01 20.000 i +++ i 0.37046E-01 21.000 i ++ i 0.24165E-01 22.000 i ++ i -0.27097E-01 23.000 i ++ i 0.15920E-01 24.000 i ++++ i 0.58745E-01 25.000 i ++++ i 0.52299E-01 26.000 i +++ i -0.30013E-01 27.000 i +++ i -0.42630E-01 28.000 i ++++ i -0.61556E-01 29.000 i ++++ i -0.55425E-01 30.000 i ++ i -0.28172E-01 31.000 i ++++++ i -0.91703E-01 32.000 i +++++++++ i -.15668 33.000 i ++++++++++ i -.17497 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) and autoregressive order selection statistics lag 1 2 3 4 5 6 7 8 9 10 11 12 pacf 0.54 0.06 0.02 0.17 0.09 -0.11 -0.05 0.00 0.02 -0.06 -0.03 -0.08 aic 0.00 1.60 3.56 2.53 3.70 4.59 6.32 8.34 10.32 11.94 13.85 15.18 f ratio 40.28 0.39 0.05 2.92 0.80 1.04 0.26 0.00 0.03 0.36 0.10 0.62 f probability 0.00 0.54 0.83 0.09 0.37 0.31 0.61 0.99 0.87 0.55 0.75 0.43 lag 13 14 15 16 17 18 19 20 21 22 23 24 pacf -0.06 0.01 -0.02 -0.01 -0.01 0.07 0.10 0.00 0.01 -0.05 0.02 0.01 aic 16.81 18.83 20.83 22.88 24.94 26.56 27.63 29.72 31.80 33.65 35.72 37.83 f ratio 0.35 0.02 0.04 0.01 0.00 0.36 0.81 0.00 0.01 0.20 0.04 0.01 f probability 0.55 0.89 0.84 0.93 0.94 0.55 0.37 0.98 0.91 0.66 0.84 0.90 lag 25 26 27 28 29 30 31 32 33 pacf -0.03 -0.10 -0.00 -0.08 -0.05 0.04 -0.06 -0.10 -0.03 aic 39.87 41.10 43.26 44.82 46.78 48.80 50.70 52.03 54.21 f ratio 0.07 0.68 0.00 0.44 0.16 0.13 0.22 0.61 0.05 f probability 0.80 0.41 0.97 0.51 0.69 0.72 0.64 0.44 0.83 order autoregressive process selected = 1 one step prediction variance of process selected = 1.16885 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 coefficient value 0.5397 1 starpac 2.08s (03/15/90) partial autocorrelation function estimate (pacf) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 i ++++++++++++++++++++++++++++ i .53974 2.0000 i ++++ i 0.63018E-01 3.0000 i ++ i 0.22136E-01 4.0000 i ++++++++++ i .17266 5.0000 i ++++++ i 0.91595E-01 6.0000 i ++++++ i -.10523 7.0000 i ++++ i -0.53166E-01 8.0000 i + i 0.93008E-03 9.0000 i ++ i 0.17874E-01 10.000 i ++++ i -0.63464E-01 11.000 i +++ i -0.33574E-01 12.000 i +++++ i -0.84130E-01 13.000 i ++++ i -0.63805E-01 14.000 i ++ i 0.14446E-01 15.000 i ++ i -0.21400E-01 16.000 i + i -0.92887E-02 17.000 i + i -0.76473E-02 18.000 i ++++ i 0.66474E-01 19.000 i ++++++ i .10021 20.000 i + i 0.30542E-02 21.000 i ++ i 0.13109E-01 22.000 i ++++ i -0.50444E-01 23.000 i ++ i 0.22769E-01 24.000 i ++ i 0.13963E-01 25.000 i +++ i -0.30205E-01 26.000 i ++++++ i -0.96142E-01 27.000 i + i -0.47261E-02 28.000 i +++++ i -0.78259E-01 29.000 i +++ i -0.48012E-01 30.000 i +++ i 0.42976E-01 31.000 i ++++ i -0.56478E-01 32.000 i ++++++ i -0.95175E-01 33.000 i ++ i -0.27238E-01 the value of ierr is 0 1simple test of ccf starpac 2.08s (03/15/90) cross correlation analysis series 1 series 2 average of the series = -0.5960004E-01-0.9959999E-01 standard deviation of the series = 1.399513 1.202491 number of time points = 50 50 largest lag value to be used = 32 cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) lag -25 -26 -27 -28 -29 -30 -31 -32 ccf -0.10 0.06 0.16 0.16 0.15 0.11 -0.00 -0.10 standard error 0.15 0.14 0.14 0.14 0.13 0.13 0.13 0.12 lag -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 ccf -0.07 -0.05 -0.11 -0.19 -0.09 0.08 0.18 0.17 0.09 -0.06 -0.12 -0.14 standard error 0.18 0.18 0.18 0.18 0.17 0.17 0.17 0.16 0.16 0.16 0.15 0.15 lag -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 ccf -0.52 -0.60 -0.33 -0.05 0.15 0.37 0.35 0.13 -0.05 -0.13 0.00 0.04 standard error 0.21 0.21 0.21 0.21 0.20 0.20 0.20 0.20 0.19 0.19 0.19 0.19 lag 0 ccf -0.02 standard error 0.14 lag 1 2 3 4 5 6 7 8 9 10 11 12 ccf 0.41 0.51 0.48 0.33 0.01 -0.18 -0.36 -0.26 -0.04 -0.02 0.06 0.02 standard error 0.21 0.21 0.21 0.21 0.20 0.20 0.20 0.20 0.19 0.19 0.19 0.19 lag 13 14 15 16 17 18 19 20 21 22 23 24 ccf 0.04 0.05 0.08 0.20 0.14 0.12 -0.19 -0.27 -0.20 -0.14 0.07 0.17 standard error 0.18 0.18 0.18 0.18 0.17 0.17 0.17 0.16 0.16 0.16 0.15 0.15 lag 25 26 27 28 29 30 31 32 ccf 0.19 0.10 -0.08 -0.08 -0.10 -0.11 -0.07 0.02 standard error 0.15 0.14 0.14 0.14 0.13 0.13 0.13 0.12 1 starpac 2.08s (03/15/90) cross correlation function estimate (ccf) ccf correlates series 1 at time t with series 2 at time t + k. (if peak correlation occures at positive (negative) lag then series 1 leads (lags) series 2) -1.0000 -0.8000 -0.6000 -0.4000 -0.2000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- -32.000 i ++++++ i -0.98861E-01 -31.000 i + i -0.28858E-02 -30.000 i ++++++ i .10712 -29.000 i ++++++++ i .14956 -28.000 i +++++++++ i .16054 -27.000 i +++++++++ i .16150 -26.000 i ++++ i 0.60338E-01 -25.000 i ++++++ i -.10388 -24.000 i ++++++++ i -.14362 -23.000 i +++++++ i -.12479 -22.000 i ++++ i -0.55619E-01 -21.000 i +++++ i 0.88452E-01 -20.000 i +++++++++ i .16802 -19.000 i ++++++++++ i .18040 -18.000 i +++++ i 0.77358E-01 -17.000 i ++++++ i -0.92188E-01 -16.000 i +++++++++++ i -.19470 -15.000 i +++++++ i -.11176 -14.000 i ++++ i -0.50033E-01 -13.000 i ++++ i -0.69442E-01 -12.000 i +++ i 0.37527E-01 -11.000 i + i 0.43801E-02 -10.000 i +++++++ i -.12992 -9.0000 i ++++ i -0.53960E-01 -8.0000 i ++++++++ i .13037 -7.0000 i ++++++++++++++++++ i .34581 -6.0000 i +++++++++++++++++++ i .36962 -5.0000 i ++++++++ i .14975 -4.0000 i ++++ i -0.52573E-01 -3.0000 i +++++++++++++++++ i -.32785 -2.0000 i +++++++++++++++++++++++++++++++ i -.59800 -1.0000 i +++++++++++++++++++++++++++ i -.51544 .00000 i ++ i -0.17402E-01 1.0000 i ++++++++++++++++++++++ i .41146 2.0000 i +++++++++++++++++++++++++++ i .51130 3.0000 i +++++++++++++++++++++++++ i .48166 4.0000 i ++++++++++++++++++ i .33171 5.0000 i ++ i 0.12280E-01 6.0000 i ++++++++++ i -.18197 7.0000 i +++++++++++++++++++ i -.35999 8.0000 i ++++++++++++++ i -.25903 9.0000 i +++ i -0.41510E-01 10.000 i ++ i -0.16659E-01 11.000 i ++++ i 0.62016E-01 12.000 i ++ i 0.18025E-01 13.000 i +++ i 0.40650E-01 14.000 i ++++ i 0.52799E-01 15.000 i +++++ i 0.79604E-01 16.000 i +++++++++++ i .20153 17.000 i ++++++++ i .13843 18.000 i +++++++ i .11858 19.000 i +++++++++++ i -.19467 20.000 i +++++++++++++++ i -.27015 21.000 i +++++++++++ i -.19730 22.000 i ++++++++ i -.14359 23.000 i +++++ i 0.73960E-01 24.000 i ++++++++++ i .17440 25.000 i +++++++++++ i .19101 26.000 i ++++++ i 0.99405E-01 27.000 i +++++ i -0.84161E-01 28.000 i +++++ i -0.76186E-01 29.000 i ++++++ i -.10048 30.000 i ++++++ i -.10986 31.000 i ++++ i -0.66356E-01 32.000 i ++ i 0.15525E-01 the value of ierr is 0 1simple test of ufs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4685 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - ++++++++++ c - i +++++++ i i ++++ i i ++++ i i +++ i -0.9096 - +++ - i ++ i i +++ i i ++ i i ++ i -1.8193 - ++ - i b ++ * w i i + i i ++ i i ++ i -2.7289 - + - i ++ i i + i i + i i ++ i -3.6385 - i + - i + i i ++ i i + i i + i -4.5482 - + - i + i i ++ i i + i i + i -5.4578 - + - i + i i + i i ++ i i + i -6.3674 - + - i + i i + i i + i i ++ i -7.2771 - + - i + i i + i i ++ i i + i -8.1867 - ++ - i + i i ++ i i ++ i i +++ i -9.0963 - +++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2381 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 - +++++++++++++++++++++++ c - i ++++++ i i +++ i i ++ i i ++ i -1.1087 - ++ - i + i i ++ i i + i i + i -2.2173 - + - i + i i + i i + b * w i i + i -3.3260 - + - i i i + i i + i i + i -4.4346 - + - i i i + i i + i i i + i -5.5433 - - i + i i + i i + i i i -6.6519 - + - i + i i + i i + i i + i -7.7606 - + - i + i i + i i + i i + i -8.8692 - ++ - i ++ i i + i i +++ i i ++ i -9.9779 - +++ - i ++ i i +++ i i ++++ i i ++++ i -11.0865 - ++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1225 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.2541 - c - i +++++ i i +++ ++ ++ i i ++++ ++ + i i +++ + + i -1.1943 - ++ ++ + - i +++ ++ i i ++++ + i i + i i i -2.6428 - + - i i i + i i + i i i -4.0912 - + b * w - i i i + i i i i + i -5.5397 - + - i + i i i i + i i ++ i -6.9881 - + i - i + i i ++ i i + i i + i -8.4366 - + - i + i i + i i ++ i i + i -9.8850 - + - i + i i ++ +++++++ i i ++ +++ ++ i i ++++++ + i -11.3335 - + - i + i i + i i + i i + i -12.7819 - + - i + i i + i i ++ i i +++ i -14.2304 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum -- (parzen window with lag wind. trunc. pt.= 32 / bw=0.0650 / edf= 7) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.5084 - c - i i i i i i i ++++ i -0.4448 - + + - i ++ i i + + i i +++++++ + i i ++ + i -2.3981 - ++ + - i +++ + + i i + i i + + i i + + + i -4.3514 - ++ + b * w - i + i i i i + i i i -6.3046 - - i + i i i i +++ i i + ++ ++ i -8.2579 - +++ + i - i + i i + i i ++ i i + i -10.2111 - + - i +++ i i + + ++ i i + + + i i ++ + + i -12.1644 - +++++ + - i + + + i i + i i + + + i i + + i -14.1176 - +++ - i + i i + i i + + i i + + i -16.0709 - + - i +++ i i i i i i i -18.0242 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. the value of ierr is 0 1simple test of uas starpac 2.08s (03/15/90) Autoregressive Order Selection Statistics: lag 1 2 3 4 5 6 7 8 9 10 11 12 aic 5.08 0.00 1.91 3.54 3.90 5.93 7.30 7.35 9.30 11.37 13.46 15.40 f ratio 23.53 7.15 0.09 0.35 1.48 0.01 0.57 1.68 0.10 0.01 0.02 0.14 f probability 0.00 0.01 0.76 0.55 0.23 0.93 0.45 0.20 0.75 0.91 0.89 0.71 lag 13 14 15 16 17 18 19 20 21 22 23 24 aic 16.80 18.98 20.84 22.94 25.12 27.38 25.94 27.94 29.59 31.88 34.44 36.43 f ratio 0.55 0.00 0.24 0.10 0.06 0.04 2.37 0.24 0.45 0.12 0.00 0.32 f probability 0.46 0.96 0.63 0.75 0.81 0.84 0.13 0.63 0.51 0.74 0.94 0.58 lag 25 26 27 28 29 30 31 32 aic 38.98 41.68 44.55 46.93 49.91 53.00 55.64 59.10 f ratio 0.08 0.04 0.00 0.25 0.04 0.04 0.25 0.00 f probability 0.79 0.85 0.99 0.62 0.85 0.85 0.63 1.00 order autoregressive process selected = 2 one step prediction variance of process selected = .883569 Yule-Walker estimates of the coefficients of the autoregressive process selected coefficient number 1 2 coefficient value 0.7819-0.3634 starpac 2.08s (03/15/90) Fourier spectrum (+) (lag wind. trunc. pt.= 16 BW =0.1225 IDF = 12 and order 2 autoregressive spectrum (.) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0981 - .... c - i .... ...++ i i ... ++..+++ i i +++++ .. ++ . + i i +++ .... ++ .. + i -1.3503 - ...2++ + . + - i ..... ++ ++ . + i i +++++++ . + i i . i i .+ i -2.7988 - .+ - i i i .+ i i . i i 2 i -4.2472 - . b * w - i +. i i +. i i . i i + . i -5.6957 - . - i + . i i + . i i + . i i + . i -7.1441 - + . i - i + . i i ++ . i i + . i i ++.. i -8.5926 - + . - i + . i i + .. i i + . i i + .. i -10.0410 - + . - i + .. i i ++ .. +++ i i + .. ++++ +++ i i +++ 22+ + i -11.4895 - +++ .. ++ - i ... + i i ... + i i .... + i i .....2 i -12.9380 - 2....... - i + i i + i i + i i ++ i -14.3864 - ++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. the value of ierr is 0 1simple test of taper (no output unless error found) the value of ierr is 0 1simple test of pgms starpac 2.08s (03/15/90) sample periodogram (in decibels) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 46.7879 - + - i i i i i 2 + i i + i 41.0117 - 2 + - i + i i + + i i ++ + i i + + 2 + i 35.2356 - + ++ + - i ++ + + ++ i i + + + 2 i i + + ++ + i i + + + + + + i 29.4594 - +2+ + + - i + + + 2 + ++ i i + + +++ ++ 2 ++ + i i + + + 2 + i i + + + ++ + + i 23.6832 - + + + + ++ + - i ++ + 2 + + + + i i + + 2 2 + + i i +++ + ++ ++++ 2 + ++ ++ + i i + + + + ++ 2 ++ + + i 17.9071 - + + + + + + + + + ++ + ++ - i + + + ++ + + + + + i i + + 2 + + ++ + 2++ + ++ i i + ++ + + + 2 + + ++ + i i + + + + ++ + + i 12.1309 - + +++ +2 + + + ++ 3+ + + + + - i + + + + ++ i i + + + + + 2 + + i i + + + + + i i + + + + + i 6.3547 - - i + ++ i i + + + i i + + i i i 0.5786 - + + - i i i + i i i i i -5.1976 - - i i i i i i i i -10.9738 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. the value of ierr is 0 1simple test of mdflt (no output unless error found) the value of ierr is 0 1display results of mdflt starpac 2.08s (03/15/90) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 8545.2412 - 322 - 8000.0000 - +2 +2 - i 2 + i 6000.0000 - + 2 - i + + i i 2 + i 4000.0000 - + ++ - i 223 + + i i 22 + + i i ++ + + i i 2 i i 2 + + i 2000.0000 - 2 + + - i + 2 + i i 2 + + i i 32 + i i + i i i 1000.0000 - + - i + i 800.0000 - + - i + i 600.0000 - + - i 2 i i + i 400.0000 - 22+32323 - i ++ 3+ i i +2 i i +2 i i 3 i i 2+ i 200.0000 - 2 - i 3 i i 22 i i +2 i i 3 i i 2+ i 100.0000 - 22 - 80.0000 - 3 - i 3 i i 2 i 60.0000 - 3 - i 2 i i 3+ i 40.0000 - +32 +2323233 2232332322 - i 32 2+ 32332+ i i 2+ +32 i i ++ 2+ i i 2 2+ i i 2+ 3 i 20.0000 - 223 - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 the value of ierr is 0 1simple test of bfs starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i i i i i i i i 0.7000 - - i i i i i i i i 0.6000 - - i ............ i i ... .... i i ... .. i i .. .. i 0.5000 - .. .. - i .. .. i i .. ++++++++++++ . i i .. +++ +++ . i i . +++ +++ .. i 0.4000 - .. ++ ++ . - i . ++ + . i i . + ++ .. i i .. ++ ........ + . i i . ++ .... .... ++ . i 0.3000 - .. + .. ... + . - i . ++ ... .. + . i i . + .. . ++ . i i .. ++ .. .. + .. i i . + . .. + . i 0.2000 - .. ++ .. . ++ . - i . + .. .. + . i i . ++ .. . + . i i + . .. ++ i i ++ .. . + i 0.1000 - ++ .. .. ++ - i ++ .. .. + i i -----------22-------22------------------------------------------------------------22-----22---------- i i +++ ... .. +++ i i +++++ .... ... +++ i 0.0000 - +++ . . ++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 4 / bw=0.4657 / edf= 93) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i ++++++ ++++ i i +++ ++ i 3.7699 - + + - i + + i i ++ + i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i i i +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ i i ++++++ ++++ i i +++ ++ i -2.5133 - + + - i + + i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i i i i i i i i 0.8000 - - i .............. i i ... ... i i .. ... i i . ... i 0.7000 - . .. - i . +++++++++ .. i i . +++ +++ . i i . ++ +++ .. i i . + ++ . i 0.6000 - . + ++ . - i + ++ .. i i . + ++ . i i . + ..... + . i i + .... ... + . i 0.5000 - . . ... ++ . - i + .. .. + . i i . + . .. + . i i . .. + . i i . + . . + . i 0.4000 - + . .. + . - i . . + . i i . + . . + .. i i . . + . i i . + .. + . i 0.3000 - . . + . - i + . . + i i . + i i + . . + i i . . + i 0.2000 - + . + - i + . . ++ i i . + i i + . .. + i i -----------2------------------------------------------------------2--------22------------------------ i 0.1000 - + . ++ - i + . .. ++ i i + . . +++ i i + . ... +++ i i + . .. ++++++ i 0.0000 - ++ ++++++++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 8 / bw=0.2349 / edf= 47) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - - i +++++++++++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ ++++++++ i i ++++++ i 3.7699 - + ++++ - i ++ i i + ++ i i i i i 2.5133 - - i i i i i i i i 1.2566 - - i i i i i i i i 0.0000 - - i i i i i i i i -1.2566 - - i +++++++++++++++++ i i +++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++ i i ++ ++++++++ i i ++++++ i -2.5133 - + ++++ - i ++ i i + i i + + i i i -3.7699 - - i i i i i i i i -5.0265 - - i i i i i i i i -6.2832 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i i 0.9000 - - i .. i i .... ..... i i .. .. i i . ... i 0.8000 - . ............... - i . .. i i . ++++++ . i i ++ ++ . i i . + ++ . i 0.7000 - + ++ . - i . + + . i i +++ ++++++ . i i . + +++++ ++ . i i + + .. i 0.6000 - + . - i . ...... + .. i i + . .. . i i . . + . i i . + . . + .. i 0.5000 - . . + . - i . i i + . .. + i i . ..... + i i . ....... . + i 0.4000 - + . + - i . . + i i . + i i + . + i i . + i 0.3000 - . ++ - i . + i i + . + i i -----------------------------------------------------2------------22--------------------------------- i i . . ++ i 0.2000 - + +++++ - i . ++++ i i . . ++ i i + .. + i i . + i 0.1000 - . .. + - i + . ++ i i . .. + i i + .. ++ ++ i i + +++ ++++ i 0.0000 - + +++ - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 16 / bw=0.1191 / edf= 24) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i i i i 5.0265 - + ++++++++ - i ++++ +++++++ +++++++ +++++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i +++ i i +++ i 3.7699 - +++ - i ++ i i ++ i i + i i ++ i 2.5133 - + - i i i + i i i i + i 1.2566 - + - i i i + i i ++ i i + i 0.0000 - 2 +2 - i i i i i i i i -1.2566 - + ++++++++ - i ++++ +++++++ +++++++ +++++ i i +++++++++++++++++++++++++ ++++++++++++++ +++ i i +++ i i +++ i -2.5133 - +++ - i ++ i i ++ i i + i i ++ i -3.7699 - + - i i i + i i i i + i -5.0265 - + - i i i + i i ++ i i + i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (squared coherency component) (+), 95 pct. confidence limits (.) and 95 pct. significance level (-) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 1.0000 - - i i i i i i i ........ .... i 0.9000 - .. .. . . - i ....... . . i i . . . i i . . i i . +++++ . ++ i 0.8000 - + ++ .. . + . - i . + + ... + ... i i + + .. i i + + + . i i +++++ + i 0.7000 - + + - i + i i + + i i + i i + + i 0.6000 - + ... - i . . + + .. + i i . . . i i . + + . + i i . . ++ + i 0.5000 - + ++ - i . . + i i ----------------------------2----------------2--------2---------------------------------------------- i i . i i ... . + + i 0.4000 - + . . . + - i . + + i i . + + + + i i . . + + + i i + + i 0.3000 - . + - i + . . +++ + + i i i i . + + i i . . . + i 0.2000 - . + + - i + . . . + i i . . .. + + i i .. ++ ++ ++++ i i ++ i 0.1000 - + . + + - i + i i + + i i + + i i ++ ++ i 0.0000 - - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. 1 starpac 2.08s (03/15/90) -- smoothed Fourier spectrum (phase component) -- (parzen window with lag wind. trunc. pt.= 33 / bw=0.0595 / edf= 12) -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 6.2832 - - i i i i i + i i + ++++ i 5.0265 - + ++ +++++++++++ + - i ++++ +++ +++ +++ ++ i i ++++++++++++++++++++++ +++++++++++++ + i i +++++ i i +++ i 3.7699 - ++ - i + i i + i i + i i +++ i 2.5133 - ++ - i + i i i i + i i i 1.2566 - - i + i i i i ++ i i +++ i 0.0000 - 2 +2 - i i i i i + i i + ++++ i -1.2566 - + ++ +++++++++++ + - i ++++ +++ +++ +++ ++ i i ++++++++++++++++++++++ +++++++++++++ + i i +++++ i i +++ i -2.5133 - ++ - i + i i + i i + i i +++ i -3.7699 - ++ - i + i i i i + i i i -5.0265 - - i + i i i i ++ i i +++ i -6.2832 - + - -i---------i---------i---------i---------i---------i---------i---------i---------i---------i---------i- 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 0.4500 0.5000 +freq period inf 20. 10. 6.6667 5. 4. 3.3333 2.8571 2.5 2.2222 2. the value of ierr is 0 1*ch13 simple test of aime starpac 2.08s (03/15/90) +***************************************************************************** * nonlinear least squares estimation for the parameters of an arima model * * using backforecasts * ***************************************************************************** summary of initial conditions ------------------------------ model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 --step size for ------parameter --approximating -----------------parameter description starting values ----------scale -----derivative index ---------type --order --fixed ----------(par) --------(scale) ----------(stp) 1 mu --- no 0.00000000E+00 + default 0.99999997E-04 2 ma (factor 1) 1 no 0.39500001E+00 + default 0.99969810E-04 3 ma (factor 2) 12 no 0.61500001E+00 + default 0.10001950E-03 number of observations (n) 144 maximum number of iterations allowed (mit) 21 maximum number of model subroutine calls allowed 42 convergence criterion for test based on the forecasted relative change in residual sum of squares (stopss) 0.2422E-04 maximum scaled relative change in the parameters (stopp) 0.3453E-03 maximum change allowed in the parameters at the first iteration (delta) 100.0 residual sum of squares for input parameter values .1762 (backforecasts included) residual standard deviation for input parameter values (rsd) 0.3710E-01 based on degrees of freedom 144 - 13 - 3 = 128 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued iteration number 1 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 2 0.3709E-01 .1761 0.1894E-03 0.1885E-03 y 0.8840E-02 y current parameter values index 1 2 3 value -0.1398369E-03 .3956066 .6157864 iteration number 2 ---------------------- model forecasted calls rsd rss rel chng rss rel chng rss rel chng par value chkd value chkd 3 0.3709E-01 .1761 0.4230E-06 0.3150E-06 y 0.3589E-03 y current parameter values index 1 2 3 value -0.1402997E-03 .3956220 .6162286 ***** residual sum of squares convergence ***** 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued results from least squares fit ------------------------------- -----predicted ----std dev of ---std row --------series ---------value ----pred value ------residual ---res 1 0.47184987E+01 0.47186551E+01 0.24117725E-02 -0.15640259E-03 -0.00 2 0.47706847E+01 0.47648969E+01 0.61983722E-02 0.57878494E-02 0.16 3 0.48828020E+01 0.48947802E+01 0.23940692E-02 -0.11978149E-01 -0.32 4 0.48598123E+01 0.48486543E+01 0.28008039E-02 0.11157990E-01 0.30 5 0.47957907E+01 0.48222179E+01 0.79720765E-02 -0.26427269E-01 -0.73 6 0.49052749E+01 0.49267063E+01 0.47373977E-02 -0.21431446E-01 -0.58 7 0.49972124E+01 0.50175581E+01 0.42205341E-02 -0.20345688E-01 -0.55 8 0.49972124E+01 0.50095205E+01 0.39644623E-02 -0.12308121E-01 -0.33 9 0.49126549E+01 0.49085040E+01 0.55768760E-02 0.41508675E-02 0.11 10 0.47791233E+01 0.47746720E+01 0.28516890E-02 0.44512749E-02 0.12 11 0.46443911E+01 0.46444154E+01 0.29099598E-02 -0.24318695E-04 -0.00 12 0.47706847E+01 0.47883601E+01 0.31972441E-02 -0.17675400E-01 -0.48 13 0.47449322E+01 0.47855816E+01 0.45245904E-02 -0.40649414E-01 -1.10 14 0.48362818E+01 0.48094549E+01 0.62704799E-02 0.26826859E-01 0.73 15 0.49487600E+01 0.49464378E+01 0.15280457E-02 0.23221970E-02 0.06 16 0.49052749E+01 0.49149151E+01 0.51955198E-03 -0.96402168E-02 -0.26 17 0.48283138E+01 0.48639326E+01 0.64206012E-02 -0.35618782E-01 -0.97 18 0.50039463E+01 0.49585133E+01 0.46572033E-02 0.45433044E-01 1.23 19 0.51357985E+01 0.50850821E+01 0.37487999E-02 0.50716400E-01 1.37 20 0.51357985E+01 0.51182184E+01 0.54070610E-02 0.17580032E-01 0.48 21 0.50625949E+01 0.50385871E+01 0.43920684E-02 0.24007797E-01 0.65 22 0.48903489E+01 0.49176941E+01 0.33219468E-02 -0.27345181E-01 -0.74 23 0.47361984E+01 0.47673950E+01 0.99724589E-03 -0.31196594E-01 -0.84 24 0.49416423E+01 0.48855801E+01 0.29025662E-02 0.56062222E-01 1.52 25 0.49767337E+01 0.49143105E+01 0.47942246E-02 0.62423229E-01 1.70 26 0.50106354E+01 0.50168056E+01 0.75723268E-02 -0.61702728E-02 -0.17 27 0.51817837E+01 0.51305237E+01 0.25358603E-02 0.51259995E-01 1.39 28 0.50937500E+01 0.51243854E+01 0.52848808E-02 -0.30635357E-01 -0.83 29 0.51474943E+01 0.50483680E+01 0.66658221E-02 0.99126339E-01 2.72 30 0.51817837E+01 0.52470894E+01 0.80803242E-02 -0.65305710E-01 -1.80 31 0.52933049E+01 0.53191552E+01 0.34681433E-02 -0.25850296E-01 -0.70 32 0.52933049E+01 0.53049226E+01 0.34897379E-02 -0.11617661E-01 -0.31 33 0.52149358E+01 0.52140489E+01 0.45372774E-02 0.88691711E-03 0.02 34 0.50875964E+01 0.50649028E+01 0.25984033E-02 0.22693634E-01 0.61 35 0.49836068E+01 0.49368854E+01 0.34704178E-02 0.46721458E-01 1.27 36 0.51119876E+01 0.51282740E+01 0.59185876E-02 -0.16286373E-01 -0.44 37 0.51416636E+01 0.51285825E+01 0.26028112E-02 0.13081074E-01 0.35 38 0.51929569E+01 0.51892705E+01 0.34079044E-02 0.36864281E-02 0.10 39 0.52626901E+01 0.53294144E+01 0.41824467E-02 -0.66724300E-01 -1.81 40 0.51984968E+01 0.52322893E+01 0.62058717E-02 -0.33792496E-01 -0.92 . . . . . . . . . . . . . . . . . . 144 0.60684257E+01 0.60533690E+01 0.68745483E-02 0.15056610E-01 0.41 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued std res vs row number 3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ - - - - - - - * - 2.25+ * + - * * - - * * - - * * * * * ** - - * * * * * * * * - 0.75+ * * * * ** * * * + - * * * * * * ** * * * * - - ** * * * * * * * * * * *- -* *** * * * * * * * * ** * * * * * * - - * * * * * * ** * ** * * * * * * * * * * - -0.75+ *** * * * * * * * * * * * * * * + - * * * * ** * * ** * * * - - * * * * ** * - - * - - * * * - -2.25+ * + - * - - - - - - * - -3.75++---------+---------+---------+---------+---------+----+----+---------+---------+---------+---------+---------++ 1.0 72.5 144.0 autocorrelation function of residuals normal probability plot of std res 1++---------+---------+----*----+---------+---------++ 3.75++---------+---------+----+----+---------+---------++ - * - - - - *** - - - - **** - - - - * - - *- 6+ ** + 2.25+ * + - * - - ** - - *** - - ** - - *** - - **** - - * - - **** - 11+ * + 0.75+ **** + - * - - *** - - ** - - *** - - * - - *** - - * - - **** - 16+ **** + -0.75+ ***** + - ** - - ***** - - * - - ***** - - * - - * - - *** - - *** - 21+ * + -2.25+ * + - * - - * - - **** - - - - * - - - - * - -* - 26++---------+---------+----*----+---------+---------++ -3.75++---------+---------+----+----+---------+---------++ -1.00 0.0 1.00 -2.5 0.0 2.5 1 starpac 2.08s (03/15/90) +nonlinear least squares estimation for the parameters of an arima model, continued model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 variance-covariance and correlation matrices of the estimated (unfixed) parameters ---------------------------------------------------------------------------------- - approximation based on assumption that residuals are small - covariances are above the diagonal - variances are on the diagonal - correlation coefficients are below the diagonal column 1 2 3 1 0.8701930E-06 -0.3492827E-06 -0.2054117E-06 2 -0.4590556E-02 0.6652854E-02 -0.3223744E-03 3 -0.3144393E-02 -0.5643858E-01 0.4904117E-02 estimates from least squares fit --------------------------------- ------parameter -----std dev of ---------------------approximate -----------------parameter description ------estimates ------parameter ----------ratio ----95 percent confidence limits index ---------type --order --fixed ----------(par) ------estimates par/(sd of par) ----------lower ----------upper 1 mu --- no -0.14029970E-03 + 0.93284133E-03 -0.15040039E+00 -0.16848352E-02 0.14042358E-02 2 ma (factor 1) 1 no 0.39562196E+00 + 0.81565030E-01 0.48503871E+01 0.26057211E+00 0.53067183E+00 3 ma (factor 2) 12 no 0.61622858E+00 + 0.70029400E-01 0.87995691E+01 0.50027865E+00 0.73217851E+00 number of observations (n) 144 residual sum of squares .1761308 (backforecasts included) residual standard deviation 0.3709477E-01 based on degrees of freedom 144 - 13 - 3 = 128 approximate condition number 87.43862 the value of ierr is 0 1simple test of aimf starpac 2.08s (03/15/90) +*********************** * arima forecasting * *********************** model summary ------------- model specification factor (p d q) s 1 0 1 1 1 2 0 1 1 12 ------parameter --------parameter description ------estimates index ---------type --order ----------(par) 1 mu --- -0.14029970E-03 2 ma (factor 1) 1 0.39562196E+00 3 ma (factor 2) 12 0.61622858E+00 number of observations (n) 144 residual sum of squares .1761309 (backforecasts included) residual standard deviation 0.3709478E-01 based on degrees of freedom 144 - 13 - 3 = 128 1 starpac 2.08s (03/15/90) +arima forecasting, continued forecasts for origin 1 --------------------95 percent 5.8618417 6.1683917 6.4749413 --------------confidence limits ---------actual 6.0151167 6.4749413 6.6282163 ------forecasts ----------lower ----------upper -------if known i---------i---------i---------i---------i---------i ------------[x] ------------[(] ------------[)] ------------[*] 140 i * i 140 6.4068799 141 i * i 141 6.2304816 142 i * i 142 6.1333981 143 i * i 143 5.9661469 144 i.............*.....................................i 144 6.0684257 145 i (---x----) i 145 6.1447396 6.0714059 6.2180734 146 i (----x-----) i 146 6.0049758 5.9192891 6.0906625 147 i (-----x------) i 147 6.0823040 5.9858332 6.1787748 148 i (------x------) i 148 6.1899605 6.0837955 6.2961254 149 i (-------x------) i 149 6.1981754 6.0831304 6.3132205 150 i (-------x-------) i 150 6.3457961 6.2225089 6.4690833 151 i (-------x--------)i 151 6.4972043 6.3661923 6.6282163 152 i (--------x--------) i 152 6.4499850 6.3116789 6.5882912 153 i (--------x---------) i 153 6.2968817 6.1516476 6.4421158 154 i (---------x---------) i 154 6.2028213 6.0509748 6.3546677 155 i(---------x----------) i 155 6.0200243 5.8618417 6.1782069 156 i (----------x---------) i 156 6.1174726 5.9531984 6.2817469 157 i (-----------x-----------) i 157 6.1973166 6.0177693 6.3768640 158 i(------------x-----------) i 158 6.0574126 5.8676796 6.2471457 159 i (------------x------------) i 159 6.1346002 5.9352007 6.3339996 the value of ierr is 0 STARPAC_PRB: Normal end of execution. May 19 2007 12:10:17.654 PM