+++ /dev/null
-dnl PSPP - a program for statistical analysis.
-dnl Copyright (C) 2017 Free Software Foundation, Inc.
-dnl
-dnl This program is free software: you can redistribute it and/or modify
-dnl it under the terms of the GNU General Public License as published by
-dnl the Free Software Foundation, either version 3 of the License, or
-dnl (at your option) any later version.
-dnl
-dnl This program is distributed in the hope that it will be useful,
-dnl but WITHOUT ANY WARRANTY; without even the implied warranty of
-dnl MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-dnl GNU General Public License for more details.
-dnl
-dnl You should have received a copy of the GNU General Public License
-dnl along with this program. If not, see <http://www.gnu.org/licenses/>.
-dnl
-AT_BANNER([LOGISTIC REGRESSION])
-
-dnl These examples are adapted from
-dnl http://www.uvm.edu/~dhowell/gradstat/psych341/lectures/Logistic%20Regression/LogisticReg1.html
-
-
-
-m4_define([LOGIT_TEST_DATA],
- [AT_DATA([lr-data.txt], dnl
- 105.00 1.00 33.00 3.00 2.00 .35 17.00 20.00 .50110 -2.00440 1
- 106.00 1.00 50.00 2.00 3.00 .38 7.00 15.00 .20168 -1.25264 1
- 107.00 1.00 91.00 3.00 2.00 .28 15.00 7.00 .00897 -1.00905 1
- 108.00 1.00 90.00 3.00 2.00 .20 2.00 2.00 .00972 -1.00982 1
- 109.00 1.00 70.00 3.00 3.00 .38 23.00 27.00 .04745 -1.04981 1
- 111.00 2.00 31.00 2.00 2.00 .00 19.00 10.00 .54159 1.84640 1
- 112.00 1.00 91.00 2.00 3.00 .18 6.00 16.00 .00897 -1.00905 1
- 113.00 1.00 81.00 3.00 2.00 .00 3.00 9.00 .01998 -1.02039 1
- 114.00 2.00 15.00 1.00 2.00 .13 19.00 13.00 .81241 1.23090 1
- 116.00 2.00 1.00 1.00 2.00 .88 15.00 7.00 .93102 1.07410 1
- 117.00 1.00 93.00 3.00 2.00 .18 9.00 15.00 .00764 -1.00770 1
- 118.00 2.00 14.00 1.00 3.00 .15 23.00 18.00 .82447 1.21289 1
- 120.00 1.00 91.00 2.00 2.00 .43 17.00 14.00 .00897 -1.00905 1
- 121.00 1.00 55.00 3.00 2.00 .69 20.00 14.00 .14409 -1.16834 1
- 122.00 1.00 70.00 2.00 3.00 .03 .00 6.00 .04745 -1.04981 1
- 123.00 1.00 25.00 2.00 2.00 .45 4.00 10.00 .65789 -2.92301 1
- 125.00 1.00 91.00 2.00 2.00 .13 .00 3.00 .00897 -1.00905 1
- 126.00 1.00 91.00 3.00 3.00 .23 4.00 6.00 .00897 -1.00905 1
- 127.00 1.00 91.00 3.00 2.00 .00 8.00 8.00 .00897 -1.00905 1
- 128.00 2.00 13.00 2.00 2.00 .65 16.00 14.00 .83592 1.19629 1
- 129.00 1.00 50.00 2.00 2.00 .25 20.00 23.00 .20168 -1.25264 1
- 135.00 1.00 90.00 3.00 3.00 .03 5.00 12.00 .00972 -1.00982 1
- 138.00 1.00 70.00 3.00 3.00 .10 1.00 6.00 .04745 -1.04981 1
- 139.00 2.00 19.00 3.00 3.00 .10 11.00 12.00 .75787 1.31949 1
- 149.00 2.00 50.00 3.00 2.00 .03 .00 .00 .20168 4.95826 1
- 204.00 1.00 50.00 3.00 1.00 .13 .00 1.00 .20168 -1.25264 1
- 205.00 1.00 91.00 3.00 3.00 .72 16.00 18.00 .00897 -1.00905 1
- 206.00 2.00 24.00 1.00 1.00 .10 5.00 21.00 .67592 1.47947 1
- 207.00 1.00 80.00 3.00 3.00 .13 6.00 7.00 .02164 -1.02212 1
- 208.00 1.00 87.00 2.00 2.00 .18 9.00 20.00 .01237 -1.01253 1
- 209.00 1.00 70.00 2.00 2.00 .53 15.00 12.00 .04745 -1.04981 1
- 211.00 1.00 55.00 2.00 1.00 .33 8.00 5.00 .14409 -1.16834 1
- 212.00 1.00 56.00 3.00 1.00 .30 6.00 20.00 .13436 -1.15522 1
- 214.00 1.00 54.00 2.00 2.00 .15 .00 16.00 .15439 -1.18258 1
- 215.00 1.00 71.00 3.00 3.00 .35 12.00 12.00 .04391 -1.04592 1
- 217.00 2.00 36.00 1.00 1.00 .10 12.00 8.00 .44049 2.27020 1
- 218.00 1.00 91.00 2.00 2.00 .05 11.00 25.00 .00897 -1.00905 1
- 219.00 1.00 91.00 2.00 2.00 1.23 11.00 24.00 .00897 -1.00905 1
- 220.00 1.00 91.00 2.00 3.00 .08 8.00 11.00 .00897 -1.00905 1
- 221.00 1.00 91.00 2.00 2.00 .33 5.00 11.00 .00897 -1.00905 1
- 222.00 2.00 36.00 2.00 1.00 .18 5.00 3.00 .44049 2.27020 1
- 223.00 1.00 70.00 2.00 3.00 .18 14.00 3.00 .04745 -1.04981 1
- 224.00 1.00 91.00 2.00 2.00 .43 2.00 10.00 .00897 -1.00905 1
- 225.00 1.00 55.00 2.00 1.00 .18 6.00 11.00 .14409 -1.16834 1
- 229.00 2.00 75.00 2.00 2.00 .40 30.00 25.00 .03212 31.12941 1
- 232.00 1.00 91.00 3.00 2.00 .15 6.00 3.00 .00897 -1.00905 1
- 233.00 1.00 70.00 2.00 1.00 .00 11.00 8.00 .04745 -1.04981 1
- 234.00 1.00 54.00 3.00 2.00 .10 .00 .00 .15439 -1.18258 1
- 237.00 1.00 70.00 3.00 2.00 .18 5.00 25.00 .04745 -1.04981 1
- 241.00 1.00 19.00 2.00 3.00 .33 13.00 9.00 .75787 -4.12995 1
- 304.00 2.00 18.00 2.00 2.00 .26 25.00 6.00 .77245 1.29458 1
- 305.00 1.00 88.00 3.00 2.00 1.35 17.00 29.00 .01142 -1.01155 1
- 306.00 1.00 70.00 2.00 3.00 .63 14.00 33.00 .04745 -1.04981 1
- 307.00 1.00 85.00 2.00 2.00 2.65 18.00 14.00 .01452 -1.01474 1
- 308.00 1.00 13.00 2.00 2.00 .23 5.00 5.00 .83592 -6.09442 1
- 309.00 2.00 13.00 2.00 2.00 .23 7.00 17.00 .83592 1.19629 1
- 311.00 2.00 1.00 2.00 2.00 .50 20.00 14.00 .93102 1.07410 1
- 315.00 1.00 19.00 2.00 3.00 .18 1.00 11.00 .75787 -4.12995 1
- 316.00 1.00 88.00 2.00 2.00 .38 12.00 11.00 .01142 -1.01155 2
- 318.00 1.00 88.00 3.00 2.00 .03 5.00 5.00 .01142 -1.01155 3
- 319.00 2.00 18.00 2.00 3.00 .30 15.00 16.00 .77245 1.29458 1
- 321.00 2.00 15.00 2.00 2.00 .63 15.00 18.00 .81241 1.23090 1
- 322.00 1.00 88.00 3.00 2.00 .40 18.00 15.00 .01142 -1.01155 1
- 325.00 2.00 18.00 2.00 3.00 1.00 28.00 18.00 .77245 1.29458 1
- 329.00 1.00 88.00 3.00 2.00 .03 7.00 11.00 .01142 -1.01155 4
- 332.00 2.00 2.00 2.00 2.00 .05 8.00 9.00 .92562 1.08036 1
-)])
-
-dnl Note: In the above data cases 305, 316 318 and 329 have identical values
-dnl of the 2nd and 3rd variables. We use this for weight testing.
-
-AT_SETUP([LOGISTIC REGRESSION basic test])
-AT_KEYWORDS([categorical categoricals])
-
-LOGIT_TEST_DATA
-
-AT_DATA([lr-data.sps], [dnl
-set format = F12.3.
-set decimal dot.
-data list notable file='lr-data.txt'
- list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
-
-logistic regression
- variables = outcome with survrate
- .
-])
-
-AT_CHECK([pspp -o pspp.csv -o pspp.txt lr-data.sps], [0], [dnl
-note: Estimation terminated at iteration number 6 because parameter estimates changed by less than 0.001
-])
-AT_CHECK([cat pspp.csv], [0], [Table: Dependent Variable Encoding
-Original Value,Internal Value
-1.000,.000
-2.000,1.000
-
-Table: Case Processing Summary
-Unweighted Cases,N,Percent
-Included in Analysis,66,100.0%
-Missing Cases,0,.0%
-Total,66,100.0%
-
-note: Estimation terminated at iteration number 6 because parameter estimates changed by less than 0.001
-
-Table: Model Summary
-Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
-1,37.323,.455,.659
-
-Table: Classification Table
-,Observed,,Predicted,,
-,,,outcome,,Percentage Correct
-,,,1.000,2.000,
-Step 1,outcome,1.000,43,5,89.6%
-,,2.000,4,14,77.8%
-,Overall Percentage,,,,86.4%
-
-Table: Variables in the Equation
-,,B,S.E.,Wald,df,Sig.,Exp(B)
-Step 1,survrate,-.081,.019,17.756,1,.000,.922
-,Constant,2.684,.811,10.941,1,.001,14.639
-])
-AT_CLEANUP
-
-AT_SETUP([LOGISTIC REGRESSION missing values])
-AT_KEYWORDS([categorical categoricals])
-
-LOGIT_TEST_DATA
-
-AT_DATA([lr-data.sps], [dnl
-set format = F12.3.
-set decimal dot.
-data list notable file='lr-data.txt'
- list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
-
-missing values survrate (999) avoid (44444) outcome (99).
-
-logistic regression
- variables = outcome with survrate avoid
- .
-])
-
-AT_CHECK([pspp -O format=csv lr-data.sps > run0], [0], [ignore])
-
-dnl Append some cases with missing values into the data.
-cat >> lr-data.txt << HERE
- 105.00 1.00 999.00 3.00 2.00 .35 17.00 20.00 .50110 -2.00440 1
- 106.00 1.00 999.00 2.00 3.00 .38 7.00 15.00 .20168 -1.25264 1
- 107.00 1.00 5.00 3.00 2.00 .28 44444 34 .00897 -1.00905 1
- 108.00 99 5.00 3.00 2.00 .28 4 34 .00897 -1.00905 1
-HERE
-
-AT_CHECK([pspp -O format=csv lr-data.sps > run1], [0], [ignore])
-
-dnl Only the summary information should be different
-AT_CHECK([diff run0 run1], [1], [dnl
-8,10c8,10
-< Included in Analysis,66,100.0%
-< Missing Cases,0,.0%
-< Total,66,100.0%
----
-> Included in Analysis,66,94.3%
-> Missing Cases,4,5.7%
-> Total,70,100.0%
-])
-
-AT_CLEANUP
-
-
-
-dnl Check that a weighted dataset is interpreted correctly
-dnl To do this, the same data set is used, one weighted, one not.
-dnl The weighted dataset omits certain cases which are identical
-AT_SETUP([LOGISTIC REGRESSION weights])
-AT_KEYWORDS([categorical categoricals])
-
-LOGIT_TEST_DATA
-
-AT_DATA([lr-data-unweighted.sps], [dnl
-set format = F12.3.
-set decimal dot.
-data list notable file='lr-data.txt'
- list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
-
-logistic regression
- variables = outcome with survrate
- .
-])
-
-AT_DATA([lr-data-weighted.sps], [dnl
-set format = F12.3.
-set decimal dot.
-data list notable file='lr-data.txt'
- list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
-
-weight by w.
-
-* Omit duplicate cases.
-select if id <> 305 and id <> 316 and id <> 318.
-
-logistic regression
- variables = outcome with survrate
- .
-])
-
-
-AT_CHECK([pspp -O format=csv lr-data-unweighted.sps > unweighted-result], [0], [ignore])
-AT_CHECK([pspp -O format=csv lr-data-weighted.sps > weighted-result], [0], [ignore])
-
-dnl The only difference should be the summary information, since
-dnl this displays the unweighted totals.
-AT_CHECK([diff unweighted-result weighted-result], [1], [dnl
-8c8
-< Included in Analysis,66,100.0%
----
-> Included in Analysis,63,100.0%
-10c10
-< Total,66,100.0%
----
-> Total,63,100.0%
-22,23c22,23
-< Step 1,outcome,1.000,43,5,89.6%
-< ,,2.000,4,14,77.8%
----
-> Step 1,outcome,1.000,43.000,5.000,89.6%
-> ,,2.000,4.000,14.000,77.8%
-])
-
-
-AT_CLEANUP
-
-
-dnl Check that the /NOCONST option works as intended.
-dnl The results this produces are very similar to those
-dnl at the example in http://www.ats.ucla.edu/stat/SPSS/faq/logregconst.htm
-AT_SETUP([LOGISTIC REGRESSION without constant])
-AT_KEYWORDS([categorical categoricals])
-
-AT_DATA([non-const.sps], [dnl
-set format=F20.3.
-
-input program.
- loop #i = 1 to 200.
- compute female = (#i > 91).
- end case.
- end loop.
-end file.
-end input program.
-
-compute constant = 1.
-
-logistic regression female with constant /noconst.
-])
-
-AT_CHECK([pspp -O format=csv non-const.sps], [0], [dnl
-Table: Dependent Variable Encoding
-Original Value,Internal Value
-.00,.000
-1.00,1.000
-
-Table: Case Processing Summary
-Unweighted Cases,N,Percent
-Included in Analysis,200,100.0%
-Missing Cases,0,.0%
-Total,200,100.0%
-
-note: Estimation terminated at iteration number 2 because parameter estimates changed by less than 0.001
-
-Table: Model Summary
-Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
-1,275.637,.008,.011
-
-Table: Classification Table
-,Observed,,Predicted,,
-,,,female,,Percentage Correct
-,,,.00,1.00,
-Step 1,female,.00,0,91,.0%
-,,1.00,0,109,100.0%
-,Overall Percentage,,,,54.5%
-
-Table: Variables in the Equation
-,,B,S.E.,Wald,df,Sig.,Exp(B)
-Step 1,constant,.180,.142,1.616,1,.204,1.198
-])
-
-AT_CLEANUP
-
-
-
-dnl Check that if somebody passes a dependent variable which is not dichtomous,
-dnl then an error is raised.
-AT_SETUP([LOGISTIC REGRESSION non-dichotomous dep var])
-AT_KEYWORDS([categorical categoricals])
-
-AT_DATA([non-dich.sps], [dnl
-data list notable list /y x1 x2 x3 x4.
-begin data.
-1 2 3 4 5
-0 2 3 4 8
-2 3 4 5 6
-end data.
-
-logistic regression y with x1 x2 x3 x4.
-])
-
-AT_CHECK([pspp -O format=csv non-dich.sps], [1],
- [dnl
-error: Dependent variable's values are not dichotomous.
-])
-
-AT_CLEANUP
-
-
-
-dnl An example to check the behaviour of LOGISTIC REGRESSION with a categorical
-dnl variable. This examṕle was inspired from that at:
-dnl http://www.ats.ucla.edu/stat/spss/dae/logit.htm
-AT_SETUP([LOGISTIC REGRESSION with categorical])
-AT_KEYWORDS([categorical categoricals])
-
-AT_DATA([lr-cat.data], [dnl
- 620 3.07 2 4
- 800 4.00 3 9
- 580 3.40 2 4
- 600 3.13 2 4
- 540 2.70 2 4
- 660 3.31 4 4
- 480 3.58 1 9
- 620 4.00 1 9
- 680 3.98 2 9
- 580 3.40 4 4
- 760 3.35 3 4
- 700 3.72 2 4
- 460 3.64 1 9
- 540 3.28 3 4
- 680 3.48 3 4
- 740 3.31 1 4
- 460 3.77 3 4
- 740 3.54 1 4
- 600 3.63 3 4
- 620 3.05 2 4
- 560 3.04 3 4
- 520 2.70 3 4
- 640 3.35 3 4
- 620 3.58 2 4
- 660 3.70 4 9
- 500 2.86 4 4
- 640 3.50 2 4
- 720 4.00 3 4
- 720 3.94 3 4
- 400 3.65 2 4
- 800 2.90 2 4
- 520 2.90 3 4
- 440 3.24 4 4
- 580 3.51 2 4
- 500 3.31 3 4
- 440 3.22 1 4
- 540 3.17 1 9
- 420 3.02 1 4
- 780 3.22 2 9
- 440 3.13 4 4
- 800 3.66 1 9
- 580 3.32 2 9
- 480 2.67 2 9
- 700 4.00 1 9
- 740 2.97 2 9
- 700 3.83 2 4
- 640 3.93 2 4
- 800 3.90 2 4
- 400 3.38 2 4
- 700 3.52 2 4
- 680 3.00 4 9
- 540 3.20 1 4
- 580 4.00 2 4
- 780 4.00 2 9
- 220 2.83 3 4
- 580 3.20 2 9
- 580 3.50 2 4
- 620 3.30 1 4
- 520 3.65 4 9
- 600 3.38 3 9
- 660 3.77 3 4
- 580 2.86 4 9
- 580 3.46 2 9
- 560 3.36 3 4
- 740 4.00 3 9
- 480 3.44 3 4
- 640 3.19 4 9
- 600 3.54 1 9
- 540 3.38 4 4
- 500 2.81 3 4
- 360 2.56 3 4
- 460 3.15 4 4
- 460 2.63 2 4
- 440 2.76 2 4
- 740 3.62 4 4
- 380 3.38 2 4
- 640 3.63 1 9
- 800 3.73 1 4
- 660 3.67 2 4
- 760 3.00 2 9
- 420 2.96 1 4
- 740 3.74 4 4
- 800 3.75 2 4
- 620 3.40 2 4
- 660 3.67 3 9
- 400 3.35 3 4
- 680 3.14 2 4
- 660 3.47 3 9
- 660 3.63 2 9
- 420 3.41 4 4
- 660 4.00 1 4
- 680 3.70 2 4
- 620 3.23 3 9
- 520 3.35 3 4
- 500 4.00 3 4
- 400 3.36 2 4
- 700 3.56 1 9
- 540 3.81 1 9
- 520 2.68 3 9
- 540 3.50 2 4
- 700 4.00 2 4
- 600 3.64 3 9
- 800 3.31 3 4
- 520 3.29 1 4
- 580 3.69 1 4
- 380 3.43 3 4
- 560 3.19 3 4
- 760 2.81 1 9
- 540 3.13 2 4
- 660 3.14 2 9
- 520 3.81 1 9
- 680 3.19 4 4
- 540 3.78 4 4
- 500 3.57 3 4
- 660 3.49 2 4
- 340 3.00 2 9
- 400 3.15 2 9
- 420 3.92 4 4
- 760 3.35 2 9
- 700 2.94 2 4
- 540 3.04 1 4
- 780 3.87 4 4
- 560 3.78 2 4
- 700 3.82 3 4
- 400 2.93 3 4
- 440 3.45 2 9
- 800 3.47 3 4
- 340 3.15 3 4
- 520 4.00 1 9
- 520 3.15 3 4
- 600 2.98 2 9
- 420 2.69 2 4
- 460 3.44 2 4
- 620 3.71 1 9
- 480 3.13 2 4
- 580 3.40 3 4
- 540 3.39 3 9
- 540 3.94 3 4
- 440 2.98 3 4
- 380 3.59 4 4
- 500 2.97 4 4
- 340 2.92 3 4
- 440 3.15 2 4
- 600 3.48 2 4
- 420 2.67 3 4
- 460 3.07 2 4
- 460 3.45 3 9
- 480 3.39 4 4
- 480 2.78 3 4
- 720 3.42 2 9
- 680 3.67 2 9
- 800 3.89 2 4
- 360 3.00 3 4
- 620 3.17 2 9
- 700 3.52 4 9
- 540 3.19 2 4
- 580 3.30 2 4
- 800 4.00 3 9
- 660 3.33 2 4
- 380 3.34 3 4
- 720 3.84 3 4
- 600 3.59 2 4
- 500 3.03 3 4
- 640 3.81 2 4
- 540 3.49 1 9
- 680 3.85 3 9
- 540 3.84 2 9
- 460 2.93 3 4
- 380 2.94 3 4
- 620 3.22 2 4
- 740 3.37 4 4
- 620 4.00 2 4
- 800 3.74 1 9
- 400 3.31 3 4
- 540 3.46 4 4
- 620 3.18 2 9
- 480 2.91 1 9
- 300 2.84 2 9
- 440 2.48 4 4
- 640 2.79 2 4
- 400 3.23 4 9
- 680 3.46 2 9
- 620 3.37 1 9
- 700 3.92 2 4
- 620 3.37 2 9
- 620 3.63 2 4
- 620 3.95 3 9
- 560 2.52 2 4
- 520 2.62 2 4
- 600 3.35 2 4
- 700 4.00 1 4
- 640 3.67 3 4
- 640 4.00 3 4
- 520 2.93 4 4
- 620 3.21 4 4
- 680 3.99 3 4
- 660 3.34 3 4
- 700 3.45 3 4
- 560 3.36 1 9
- 800 2.78 2 4
- 500 3.88 4 4
- 700 3.65 2 4
- 680 3.76 3 9
- 660 3.07 3 4
- 580 3.46 4 4
- 460 2.87 2 4
- 600 3.31 4 4
- 620 3.94 4 4
- 400 3.05 2 4
- 800 3.43 2 9
- 600 3.58 1 9
- 580 3.36 2 4
- 540 3.16 3 4
- 500 2.71 2 4
- 600 3.28 3 4
- 600 2.82 4 4
- 460 3.58 2 4
- 520 2.85 3 4
- 740 3.52 4 9
- 500 3.95 4 4
- 560 3.61 3 4
- 620 3.45 2 9
- 640 3.51 2 4
- 660 3.44 2 9
- 660 2.91 3 9
- 540 3.28 1 4
- 560 2.98 1 9
- 800 3.97 1 4
- 720 3.77 3 4
- 720 3.64 1 9
- 480 3.71 4 9
- 680 3.34 2 4
- 680 3.11 2 4
- 540 2.81 3 4
- 620 3.75 2 9
- 540 3.12 1 4
- 560 3.48 2 9
- 720 3.40 3 4
- 680 3.90 1 4
- 640 3.76 3 4
- 560 3.16 1 4
- 520 3.30 2 9
- 640 3.12 3 4
- 580 3.57 3 4
- 540 3.55 4 9
- 780 3.63 4 9
- 600 3.89 1 9
- 800 4.00 1 9
- 580 3.29 4 4
- 360 3.27 3 4
- 800 4.00 2 9
- 640 3.52 4 4
- 720 3.45 4 4
- 580 3.06 2 4
- 580 3.02 2 4
- 500 3.60 3 9
- 580 3.12 3 9
- 600 2.82 4 4
- 620 3.99 3 4
- 700 4.00 3 4
- 480 4.00 2 4
- 560 2.95 2 4
- 560 4.00 3 4
- 560 2.65 3 9
- 400 3.08 2 4
- 480 2.62 2 9
- 640 3.86 3 4
- 480 3.57 2 4
- 540 3.51 2 4
- 380 3.33 4 4
- 680 3.64 3 4
- 400 3.51 3 4
- 340 2.90 1 4
- 700 3.08 2 4
- 480 3.02 1 9
- 600 3.15 2 9
- 780 3.80 3 9
- 520 3.74 2 9
- 520 3.51 2 4
- 640 3.73 3 4
- 560 3.32 4 4
- 620 2.85 2 4
- 700 3.28 1 4
- 760 4.00 1 9
- 800 3.60 2 4
- 580 3.34 2 4
- 540 3.77 2 9
- 640 3.17 2 4
- 540 3.02 4 4
- 680 3.08 4 4
- 680 3.31 2 4
- 680 2.96 3 9
- 700 2.88 2 4
- 580 3.77 4 4
- 540 3.49 2 9
- 700 3.56 2 9
- 600 3.56 2 9
- 560 3.59 2 4
- 640 2.94 2 9
- 560 3.33 4 4
- 620 3.69 3 4
- 680 3.27 2 9
- 460 3.14 3 4
- 500 3.53 4 4
- 620 3.33 3 4
- 600 3.62 3 4
- 500 3.01 4 4
- 740 3.34 4 4
- 560 3.69 3 9
- 620 3.95 3 9
- 740 3.86 2 9
- 800 3.53 1 9
- 620 3.78 3 4
- 700 3.27 2 4
- 540 3.78 2 9
- 700 3.65 2 4
- 800 3.22 1 9
- 560 3.59 2 9
- 800 3.15 4 4
- 520 3.90 3 9
- 520 3.74 4 9
- 480 2.55 1 4
- 800 4.00 4 4
- 620 3.09 4 4
- 560 3.49 4 4
- 500 3.17 3 4
- 480 3.40 2 4
- 460 2.98 1 4
- 580 3.58 1 9
- 640 3.30 2 4
- 480 3.45 2 4
- 440 3.17 2 4
- 660 3.32 1 4
- 500 3.08 3 4
- 660 3.94 2 4
- 720 3.31 1 4
- 460 3.64 3 9
- 500 2.93 4 4
- 800 3.54 3 4
- 580 2.93 2 4
- 620 3.61 1 9
- 500 2.98 3 4
- 660 4.00 2 9
- 560 3.24 4 4
- 560 2.42 2 4
- 580 3.80 2 4
- 500 3.23 4 4
- 680 2.42 1 9
- 580 3.46 3 4
- 800 3.91 3 4
- 700 2.90 4 4
- 520 3.12 2 4
- 300 2.92 4 4
- 560 3.43 3 4
- 620 3.63 3 4
- 500 2.79 4 4
- 360 3.14 1 4
- 640 3.94 2 9
- 460 3.99 3 9
- 300 3.01 3 4
- 520 2.73 2 4
- 600 3.47 2 9
- 580 3.25 1 4
- 520 3.10 4 4
- 620 3.43 3 4
- 380 2.91 4 4
- 660 3.59 3 4
- 660 3.95 2 9
- 540 3.33 3 4
- 740 4.00 3 4
- 640 3.38 3 4
- 600 3.89 3 4
- 720 3.88 3 4
- 580 4.00 3 4
- 420 2.26 4 4
- 520 4.00 2 9
- 800 3.70 1 9
- 700 4.00 1 9
- 480 3.43 2 4
- 660 3.45 4 4
- 520 3.25 3 4
- 560 2.71 3 4
- 600 3.32 2 4
- 580 2.88 2 4
- 660 3.88 2 9
- 600 3.22 1 4
- 580 4.00 1 4
- 660 3.60 3 9
- 500 3.35 2 4
- 520 2.98 2 4
- 660 3.49 2 9
- 560 3.07 2 4
- 500 3.13 2 9
- 720 3.50 3 9
- 440 3.39 2 9
- 640 3.95 2 9
- 380 3.61 3 4
- 800 3.05 2 9
- 520 3.19 3 9
- 600 3.40 3 4
-])
-
-AT_DATA([lr-cat.sps], [dnl
-set format=F20.3.
-
-data list notable list file='lr-cat.data' /b1 b2 bcat y.
-
-logistic regression
- y with b1 b2 bcat
- /categorical = bcat
- .
-])
-
-AT_CHECK([pspp -O format=csv lr-cat.sps], [0], [dnl
-Table: Dependent Variable Encoding
-Original Value,Internal Value
-4.000,.000
-9.000,1.000
-
-Table: Case Processing Summary
-Unweighted Cases,N,Percent
-Included in Analysis,400,100.0%
-Missing Cases,0,.0%
-Total,400,100.0%
-
-note: Estimation terminated at iteration number 4 because parameter estimates changed by less than 0.001
-
-Table: Model Summary
-Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
-1,458.517,.098,.138
-
-Table: Categorical Variables' Codings
-,,Frequency,Parameter coding,,
-,,,(1),(2),(3)
-bcat,1.000,61,1,0,0
-,2.000,151,0,1,0
-,3.000,121,0,0,1
-,4.000,67,0,0,0
-
-Table: Classification Table
-,Observed,,Predicted,,
-,,,y,,Percentage Correct
-,,,4.000,9.000,
-Step 1,y,4.000,254,19,93.0%
-,,9.000,97,30,23.6%
-,Overall Percentage,,,,71.0%
-
-Table: Variables in the Equation
-,,B,S.E.,Wald,df,Sig.,Exp(B)
-Step 1,b1,.002,.001,4.284,1,.038,1.002
-,b2,.804,.332,5.872,1,.015,2.235
-,bcat,,,20.895,3,.000,
-,bcat(1),1.551,.418,13.788,1,.000,4.718
-,bcat(2),.876,.367,5.706,1,.017,2.401
-,bcat(3),.211,.393,.289,1,.591,1.235
-,Constant,-5.541,1.138,23.709,1,.000,.004
-])
-AT_CLEANUP
-
-
-
-dnl This example is inspired by http://www.ats.ucla.edu/stat/spss/output/logistic.htm
-AT_SETUP([LOGISTIC REGRESSION with cat var 2])
-AT_KEYWORDS([categorical categoricals])
-
-AT_DATA([lr-cat2.data], [dnl
- 60.00 1.00 8.00 50.00
- 47.00 .00 9.00 42.00
- 57.00 1.00 7.00 53.00
- 60.00 .00 8.00 53.00
- 68.00 .00 8.00 66.00
- 63.00 .00 8.00 55.00
- 65.00 .00 8.00 63.00
- 52.00 .00 8.00 61.00
- 34.00 .00 9.00 42.00
- 37.00 .00 8.00 39.00
- 68.00 1.00 9.00 69.00
- 60.00 .00 9.00 61.00
- 44.00 .00 9.00 58.00
- 42.00 .00 8.00 47.00
- 57.00 1.00 7.00 61.00
- 55.00 1.00 8.00 50.00
- 55.00 .00 9.00 58.00
- 44.00 .00 8.00 63.00
- 50.00 1.00 9.00 66.00
- 44.00 .00 8.00 39.00
- 55.00 .00 8.00 58.00
- 44.00 .00 8.00 50.00
- 47.00 1.00 7.00 34.00
- 48.00 .00 8.00 44.00
- 45.00 .00 7.00 31.00
- 43.00 .00 8.00 50.00
- 39.00 .00 8.00 42.00
- 63.00 .00 9.00 50.00
- 47.00 .00 8.00 58.00
- 42.00 .00 7.00 50.00
- 50.00 .00 9.00 36.00
- 47.00 .00 7.00 33.00
- 60.00 .00 9.00 61.00
- 47.00 .00 7.00 42.00
- 68.00 1.00 9.00 69.00
- 52.00 .00 8.00 54.00
- 63.00 1.00 9.00 61.00
- 65.00 1.00 9.00 61.00
- 63.00 1.00 9.00 53.00
- 57.00 .00 8.00 51.00
- 34.00 .00 8.00 36.00
- 50.00 .00 8.00 39.00
- 52.00 1.00 7.00 56.00
- 45.00 .00 7.00 34.00
- 47.00 1.00 7.00 53.00
- 34.00 .00 7.00 39.00
- 50.00 1.00 8.00 55.00
- 60.00 .00 9.00 58.00
- 63.00 .00 8.00 58.00
- 35.00 .00 7.00 51.00
- 50.00 .00 8.00 58.00
- 68.00 .00 8.00 63.00
- 41.00 .00 9.00 34.00
- 47.00 .00 8.00 47.00
- 76.00 .00 9.00 64.00
- 44.00 .00 8.00 44.00
- 36.00 .00 9.00 50.00
- 68.00 1.00 9.00 55.00
- 47.00 1.00 8.00 50.00
- 50.00 .00 7.00 53.00
- 68.00 .00 8.00 74.00
- 39.00 .00 7.00 44.00
- 50.00 .00 8.00 55.00
- 52.00 .00 9.00 61.00
- 47.00 .00 8.00 53.00
- 39.00 .00 7.00 47.00
- 55.00 1.00 9.00 49.00
- 68.00 1.00 8.00 50.00
- 52.00 1.00 9.00 63.00
- 55.00 .00 8.00 58.00
- 57.00 .00 8.00 55.00
- 66.00 1.00 9.00 61.00
- 65.00 1.00 7.00 58.00
- 42.00 .00 7.00 42.00
- 68.00 1.00 7.00 59.00
- 60.00 1.00 9.00 61.00
- 52.00 .00 8.00 55.00
- 57.00 1.00 7.00 54.00
- 42.00 .00 9.00 50.00
- 42.00 .00 8.00 47.00
- 57.00 .00 8.00 50.00
- 47.00 .00 7.00 45.00
- 44.00 .00 7.00 40.00
- 43.00 .00 9.00 55.00
- 31.00 .00 8.00 39.00
- 37.00 .00 7.00 33.00
- 63.00 1.00 7.00 63.00
- 47.00 .00 8.00 39.00
- 57.00 1.00 8.00 63.00
- 52.00 .00 8.00 44.00
- 44.00 .00 7.00 35.00
- 52.00 .00 7.00 55.00
- 55.00 .00 7.00 69.00
- 52.00 .00 8.00 53.00
- 55.00 .00 9.00 61.00
- 65.00 1.00 9.00 63.00
- 55.00 .00 8.00 44.00
- 63.00 .00 7.00 65.00
- 44.00 .00 7.00 39.00
- 47.00 .00 7.00 36.00
- 63.00 1.00 9.00 55.00
- 68.00 .00 8.00 66.00
- 34.00 .00 8.00 39.00
- 47.00 .00 9.00 50.00
- 50.00 .00 9.00 58.00
- 63.00 .00 8.00 66.00
- 44.00 .00 7.00 34.00
- 44.00 .00 8.00 50.00
- 50.00 .00 8.00 53.00
- 47.00 1.00 9.00 69.00
- 65.00 .00 9.00 58.00
- 57.00 .00 8.00 47.00
- 39.00 .00 8.00 39.00
- 47.00 .00 8.00 53.00
- 50.00 1.00 7.00 63.00
- 50.00 .00 8.00 50.00
- 63.00 .00 9.00 53.00
- 73.00 1.00 9.00 61.00
- 44.00 .00 7.00 47.00
- 47.00 .00 8.00 42.00
- 47.00 .00 8.00 58.00
- 36.00 .00 7.00 61.00
- 57.00 1.00 8.00 55.00
- 53.00 1.00 8.00 57.00
- 63.00 .00 7.00 66.00
- 50.00 .00 8.00 34.00
- 47.00 .00 9.00 48.00
- 57.00 1.00 8.00 58.00
- 39.00 .00 8.00 53.00
- 42.00 .00 8.00 42.00
- 42.00 .00 9.00 31.00
- 42.00 .00 8.00 72.00
- 46.00 .00 8.00 44.00
- 55.00 .00 8.00 42.00
- 42.00 .00 8.00 47.00
- 50.00 .00 8.00 44.00
- 44.00 .00 9.00 39.00
- 73.00 1.00 8.00 69.00
- 71.00 1.00 9.00 58.00
- 50.00 .00 9.00 49.00
- 63.00 1.00 7.00 54.00
- 42.00 .00 8.00 36.00
- 47.00 .00 7.00 42.00
- 39.00 .00 9.00 26.00
- 63.00 .00 8.00 58.00
- 50.00 .00 8.00 55.00
- 65.00 1.00 8.00 55.00
- 76.00 1.00 9.00 67.00
- 71.00 1.00 8.00 66.00
- 39.00 .00 9.00 47.00
- 47.00 1.00 9.00 63.00
- 60.00 .00 7.00 50.00
- 63.00 .00 9.00 55.00
- 54.00 1.00 9.00 55.00
- 55.00 1.00 8.00 58.00
- 57.00 .00 8.00 61.00
- 55.00 1.00 9.00 63.00
- 42.00 .00 7.00 50.00
- 50.00 .00 8.00 44.00
- 55.00 .00 8.00 42.00
- 42.00 .00 7.00 50.00
- 34.00 .00 8.00 39.00
- 65.00 .00 9.00 46.00
- 52.00 .00 7.00 58.00
- 44.00 .00 8.00 39.00
- 65.00 1.00 9.00 66.00
- 47.00 .00 8.00 42.00
- 41.00 .00 7.00 39.00
- 68.00 .00 9.00 63.00
- 63.00 1.00 8.00 72.00
- 52.00 .00 8.00 53.00
- 57.00 .00 8.00 50.00
- 68.00 .00 8.00 55.00
- 42.00 .00 8.00 56.00
- 47.00 .00 8.00 48.00
- 73.00 1.00 9.00 58.00
- 39.00 .00 8.00 50.00
- 63.00 1.00 9.00 69.00
- 60.00 .00 8.00 55.00
- 65.00 1.00 9.00 66.00
- 73.00 1.00 8.00 63.00
- 52.00 .00 8.00 55.00
- 36.00 .00 8.00 42.00
- 28.00 .00 7.00 44.00
- 47.00 .00 8.00 44.00
- 57.00 .00 7.00 47.00
- 34.00 .00 7.00 29.00
- 47.00 .00 9.00 66.00
- 57.00 .00 8.00 58.00
- 60.00 1.00 9.00 50.00
- 50.00 .00 9.00 47.00
- 73.00 1.00 9.00 55.00
- 52.00 1.00 8.00 47.00
- 55.00 .00 8.00 53.00
- 47.00 .00 8.00 53.00
- 50.00 .00 8.00 61.00
- 61.00 .00 7.00 44.00
- 52.00 .00 9.00 53.00
- 47.00 .00 7.00 40.00
- 47.00 .00 7.00 50.00
-])
-
-AT_DATA([stringcat.sps], [dnl
-set format=F20.3 /small=0.
-data list notable file='lr-cat2.data' list /read honcomp wiz science *.
-
-string ses(a1).
-recode wiz (7 = "a") (8 = "b") (9 = "c") into ses.
-
-logistic regression honcomp with read science ses
- /categorical = ses.
-
-])
-
-AT_CHECK([pspp -O format=csv stringcat.sps], [0], [dnl
-Table: Dependent Variable Encoding
-Original Value,Internal Value
-.000,.000
-1.000,1.000
-
-Table: Case Processing Summary
-Unweighted Cases,N,Percent
-Included in Analysis,200,100.0%
-Missing Cases,0,.0%
-Total,200,100.0%
-
-note: Estimation terminated at iteration number 5 because parameter estimates changed by less than 0.001
-
-Table: Model Summary
-Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
-1,165.701,.280,.408
-
-Table: Categorical Variables' Codings
-,,Frequency,Parameter coding,
-,,,(1),(2)
-ses,a,47,1,0
-,b,95,0,1
-,c,58,0,0
-
-Table: Classification Table
-,Observed,,Predicted,,
-,,,honcomp,,Percentage Correct
-,,,.000,1.000,
-Step 1,honcomp,.000,132,15,89.8%
-,,1.000,26,27,50.9%
-,Overall Percentage,,,,79.5%
-
-Table: Variables in the Equation
-,,B,S.E.,Wald,df,Sig.,Exp(B)
-Step 1,read,.098,.025,15.199,1,.000,1.103
-,science,.066,.027,5.867,1,.015,1.068
-,ses,,,6.690,2,.035,
-,ses(1),.058,.532,.012,1,.913,1.060
-,ses(2),-1.013,.444,5.212,1,.022,.363
-,Constant,-9.561,1.662,33.113,1,.000,.000
-])
-
-AT_CLEANUP
-
-
-dnl Check that it doesn't crash if a categorical variable
-dnl has only one distinct value
-AT_SETUP([LOGISTIC REGRESSION identical categories])
-AT_KEYWORDS([categorical categoricals])
-
-AT_DATA([crash.sps], [dnl
-data list notable list /y x1 x2*.
-begin data
-0 1 1
-1 2 1
-end data.
-
-logistic regression y with x1 x2
- /categorical = x2.
-])
-
-AT_CHECK([pspp -O format=csv crash.sps], [1], [ignore])
-
-AT_CLEANUP
-
-
-dnl Test that missing values on the categorical predictors are treated
-dnl properly.
-AT_SETUP([LOGISTIC REGRESSION missing categoricals])
-AT_KEYWORDS([categorical categoricals])
-
-AT_DATA([data.txt], [dnl
- .00 3.69 .00
- .00 1.16 1.00
- 1.00 -12.99 .00
- .00 2.97 1.00
- .00 20.48 .00
- .00 4.90 .00
- 1.00 -4.38 .00
- .00 -1.69 1.00
- 1.00 -5.71 .00
- 1.00 -14.28 .00
- .00 9.00 .00
- .00 2.89 1.00
- .00 13.51 1.00
- .00 23.32 1.00
- .00 2.31 1.00
- .00 -2.07 1.00
- 1.00 -4.52 1.00
- 1.00 -5.83 .00
- 1.00 -1.91 .00
- 1.00 -11.12 1.00
- .00 -1.51 .00
- .00 6.59 1.00
- .00 19.28 1.00
- .00 5.94 .00
- .00 8.21 1.00
- .00 8.11 1.00
- .00 2.49 .00
- .00 9.62 .00
- 1.00 -20.74 1.00
- .00 -1.41 1.00
- .00 15.15 1.00
- .00 9.39 .00
- 1.00 -15.14 1.00
- 1.00 -5.86 .00
- 1.00 -11.64 1.00
- 1.00 -14.36 .00
- 1.00 -8.95 1.00
- 1.00 -16.42 1.00
- 1.00 -1.04 1.00
- .00 12.89 1.00
- .00 -7.08 1.00
- .00 4.87 1.00
- .00 11.53 1.00
- 1.00 -6.24 1.00
- .00 1.25 1.00
- .00 4.39 1.00
- .00 3.17 .00
- .00 19.39 1.00
- .00 13.03 1.00
- .00 2.43 .00
- 1.00 -14.73 1.00
- .00 8.25 1.00
- 1.00 -13.28 1.00
- .00 5.27 1.00
- 1.00 -3.46 1.00
- .00 13.81 1.00
- .00 1.35 1.00
- 1.00 -3.94 1.00
- .00 20.73 1.00
- 1.00 -15.40 .00
- 1.00 -11.01 1.00
- .00 4.56 .00
- 1.00 -15.35 1.00
- .00 15.21 .00
- .00 5.34 1.00
- 1.00 -21.55 1.00
- .00 10.12 1.00
- .00 -.73 1.00
- .00 15.28 1.00
- .00 11.08 1.00
- 1.00 -8.24 .00
- .00 2.46 .00
- .00 9.60 .00
- .00 11.24 .00
- .00 14.13 1.00
- .00 19.72 1.00
- .00 5.58 .00
- .00 26.23 1.00
- .00 7.25 .00
- 1.00 -.79 .00
- .00 6.24 .00
- 1.00 1.16 .00
- 1.00 -7.89 1.00
- 1.00 -1.86 1.00
- 1.00 -10.80 1.00
- 1.00 -5.51 .00
- .00 7.51 .00
- .00 11.18 .00
- .00 8.73 .00
- 1.00 -11.21 1.00
- 1.00 -13.24 .00
- .00 19.34 .00
- .00 9.32 1.00
- .00 17.97 1.00
- 1.00 -1.56 1.00
- 1.00 -3.13 .00
- .00 3.98 .00
- .00 -1.21 1.00
- .00 2.37 .00
- 1.00 -18.03 1.00
-])
-
-AT_DATA([miss.sps], [dnl
-data list notable file='data.txt' list /y x1 cat0*.
-
-logistic regression y with x1 cat0
- /categorical = cat0.
-])
-
-AT_CHECK([pspp -O format=csv miss.sps > file1], [0], [ignore])
-
-dnl Append a case with a missing categorical.
-AT_CHECK([echo '1 34 .' >> data.txt], [0], [ignore])
-
-AT_CHECK([pspp -O format=csv miss.sps > file2], [0], [ignore])
-
-AT_CHECK([diff file1 file2], [1], [dnl
-8,10c8,10
-< Included in Analysis,100,100.0%
-< Missing Cases,0,.0%
-< Total,100,100.0%
----
-> Included in Analysis,100,99.0%
-> Missing Cases,1,1.0%
-> Total,101,100.0%
-])
-
-AT_CLEANUP
-
-
-dnl Check that the confidence intervals are properly reported.
-dnl Use an example with categoricals, because that was buggy at
-dnl one point. The data in this example comes from:
-dnl http://people.ysu.edu/~gchang/SPSSE/SPSS_lab2Regression.pdf
-AT_SETUP([LOGISTIC REGRESSION confidence interval])
-AT_KEYWORDS([categorical categoricals])
-
-AT_DATA([ci.sps], [dnl
-set FORMAT=F20.3
-data list notable list /disease age sciostat sector savings *.
-begin data.
-0 33 1 1 1
-0 35 1 1 1
-0 6 1 1 0
-0 60 1 1 1
-1 18 3 1 0
-0 26 3 1 0
-0 6 3 1 0
-1 31 2 1 1
-1 26 2 1 0
-0 37 2 1 0
-0 23 1 1 0
-0 23 1 1 0
-0 27 1 1 1
-1 9 1 1 1
-1 37 1 2 1
-1 22 1 2 1
-1 67 1 2 1
-0 8 1 2 1
-1 6 1 2 1
-1 15 1 2 1
-1 21 2 2 1
-1 32 2 2 1
-1 16 1 2 1
-0 11 2 2 0
-0 14 3 2 0
-0 9 2 2 0
-0 18 2 2 0
-0 2 3 1 0
-0 61 3 1 1
-0 20 3 1 0
-0 16 3 1 0
-0 9 2 1 0
-0 35 2 1 1
-0 4 1 1 1
-0 44 3 2 0
-1 11 3 2 0
-0 3 2 2 1
-0 6 3 2 0
-1 17 2 2 0
-0 1 3 2 1
-1 53 2 2 1
-1 13 1 2 0
-0 24 1 2 0
-1 70 1 2 1
-1 16 3 2 1
-0 12 2 2 1
-1 20 3 2 1
-0 65 3 2 1
-1 40 2 2 0
-1 38 2 2 1
-1 68 2 2 1
-1 74 1 2 1
-1 14 1 2 1
-1 27 1 2 1
-0 31 1 2 1
-0 18 1 2 1
-0 39 1 2 0
-0 50 1 2 1
-0 31 1 2 1
-0 61 1 2 1
-0 18 3 1 0
-0 5 3 1 0
-0 2 3 1 1
-0 16 3 1 0
-1 59 3 1 1
-0 22 3 1 0
-0 24 1 1 1
-0 30 1 1 1
-0 46 1 1 1
-0 28 1 1 0
-0 27 1 1 1
-1 27 1 1 0
-0 28 1 1 1
-1 52 1 1 1
-0 11 3 1 1
-0 6 2 1 1
-0 46 3 1 0
-1 20 2 1 1
-0 3 1 1 1
-0 18 2 1 0
-0 25 2 1 0
-0 6 3 1 1
-1 65 3 1 1
-0 51 3 1 1
-0 39 2 1 1
-0 8 1 1 1
-0 8 2 1 0
-0 14 3 1 0
-0 6 3 1 0
-0 6 3 1 1
-0 7 3 1 0
-0 4 3 1 0
-0 8 3 1 0
-0 9 2 1 0
-1 32 3 1 0
-0 19 3 1 0
-0 11 3 1 0
-0 35 3 1 0
-0 16 1 1 0
-0 1 1 1 1
-0 6 1 1 1
-0 27 1 1 1
-0 25 1 1 1
-0 18 1 1 0
-0 37 3 1 0
-1 33 3 1 0
-0 27 2 1 0
-0 2 1 1 0
-0 8 2 1 0
-0 5 1 1 0
-0 1 1 1 1
-0 32 1 1 0
-1 25 1 1 1
-0 15 1 2 0
-0 15 1 2 1
-0 26 1 2 1
-1 42 1 2 1
-0 7 1 2 1
-0 2 1 2 0
-1 65 1 2 1
-0 33 2 2 1
-1 8 2 2 0
-0 30 2 2 0
-0 5 3 2 0
-0 15 3 2 0
-1 60 3 2 1
-1 13 3 2 1
-0 70 3 1 1
-0 5 3 1 0
-0 3 3 1 1
-0 50 2 1 1
-0 6 2 1 0
-0 12 2 1 1
-1 39 3 2 0
-0 15 2 2 1
-1 35 2 2 0
-0 2 2 2 1
-0 17 3 2 0
-1 43 3 2 1
-0 30 2 2 1
-0 11 1 2 1
-1 39 1 2 1
-0 32 1 2 1
-0 17 1 2 1
-0 3 3 2 1
-0 7 3 2 0
-0 2 2 2 0
-1 64 2 2 1
-1 13 1 2 2
-1 15 2 2 1
-0 48 2 2 1
-0 23 1 2 1
-1 48 1 2 0
-0 25 1 2 1
-0 12 1 2 1
-1 46 1 2 1
-0 79 1 2 1
-0 56 1 2 1
-0 8 1 2 1
-1 29 3 1 0
-1 35 3 1 0
-1 11 3 1 0
-0 69 3 1 1
-1 21 3 1 0
-0 13 3 1 0
-0 21 1 1 1
-1 32 1 1 1
-1 24 1 1 0
-0 24 1 1 1
-0 73 1 1 1
-0 42 1 1 1
-1 34 1 1 1
-0 30 2 1 0
-0 7 2 1 0
-1 29 3 1 0
-1 22 3 1 0
-0 38 2 1 1
-0 13 2 1 1
-0 12 2 1 1
-0 42 3 1 0
-1 17 3 1 0
-0 21 3 1 1
-0 34 1 1 1
-0 1 3 1 0
-0 14 2 1 0
-0 16 2 1 0
-0 9 3 1 0
-0 53 3 1 0
-0 27 3 1 0
-0 15 3 1 0
-0 9 3 1 0
-0 4 2 1 1
-0 10 3 1 1
-0 31 3 1 0
-0 85 3 1 1
-0 24 2 1 0
-end data.
-
-logistic regression
- disease WITH age sciostat sector savings
- /categorical = sciostat sector
- /print = ci(95).
-])
-
-AT_CHECK([pspp -O format=csv ci.sps], [0], [dnl
-Table: Dependent Variable Encoding
-Original Value,Internal Value
-.000,.000
-1.000,1.000
-
-Table: Case Processing Summary
-Unweighted Cases,N,Percent
-Included in Analysis,196,100.0%
-Missing Cases,0,.0%
-Total,196,100.0%
-
-note: Estimation terminated at iteration number 4 because parameter estimates changed by less than 0.001
-
-Table: Model Summary
-Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
-1,211.195,.120,.172
-
-Table: Categorical Variables' Codings
-,,Frequency,Parameter coding,
-,,,(1),(2)
-sciostat,1.000,77,1,0
-,2.000,49,0,1
-,3.000,70,0,0
-sector,1.000,117,1,
-,2.000,79,0,
-
-Table: Classification Table
-,Observed,,Predicted,,
-,,,disease,,Percentage Correct
-,,,.000,1.000,
-Step 1,disease,.000,131,8,94.2%
-,,1.000,41,16,28.1%
-,Overall Percentage,,,,75.0%
-
-Table: Variables in the Equation
-,,B,S.E.,Wald,df,Sig.,Exp(B),95% CI for Exp(B),
-,,,,,,,,Lower,Upper
-Step 1,age,.027,.009,8.647,1,.003,1.027,1.009,1.045
-,savings,.061,.386,.025,1,.874,1.063,.499,2.264
-,sciostat,,,.440,2,.803,,,
-,sciostat(1),-.278,.434,.409,1,.522,.757,.323,1.775
-,sciostat(2),-.219,.459,.227,1,.634,.803,.327,1.976
-,sector,,,11.974,1,.001,,,
-,sector(1),-1.235,.357,11.974,1,.001,.291,.145,.586
-,Constant,-.814,.452,3.246,1,.072,.443,,
-])
-
-AT_CLEANUP
-
-AT_SETUP([LOGISTIC REGRESSION syntax errors])
-AT_DATA([logistic.sps], [dnl
-DATA LIST LIST NOTABLE/x y z.
-LOGISTIC REGRESSION **.
-LOGISTIC REGRESSION x **.
-LOGISTIC REGRESSION x WITH **.
-LOGISTIC REGRESSION x WITH y/MISSING=**.
-LOGISTIC REGRESSION x WITH y/CATEGORICAL=**.
-LOGISTIC REGRESSION x WITH y/PRINT=CI **.
-LOGISTIC REGRESSION x WITH y/PRINT=CI(**).
-LOGISTIC REGRESSION x WITH y/PRINT=CI(123 **).
-LOGISTIC REGRESSION x WITH y/PRINT=**.
-LOGISTIC REGRESSION x WITH y/CRITERIA=BCON **.
-LOGISTIC REGRESSION x WITH y/CRITERIA=BCON(**).
-LOGISTIC REGRESSION x WITH y/CRITERIA=BCON(123 **).
-LOGISTIC REGRESSION x WITH y/CRITERIA=ITERATE **.
-LOGISTIC REGRESSION x WITH y/CRITERIA=ITERATE(**).
-LOGISTIC REGRESSION x WITH y/CRITERIA=ITERATE(123 **).
-LOGISTIC REGRESSION x WITH y/CRITERIA=LCON **.
-LOGISTIC REGRESSION x WITH y/CRITERIA=LCON(**).
-LOGISTIC REGRESSION x WITH y/CRITERIA=LCON(123 **).
-LOGISTIC REGRESSION x WITH y/CRITERIA=EPS **.
-LOGISTIC REGRESSION x WITH y/CRITERIA=EPS(**).
-LOGISTIC REGRESSION x WITH y/CRITERIA=EPS(123 **).
-LOGISTIC REGRESSION x WITH y/CRITERIA=CUT **.
-LOGISTIC REGRESSION x WITH y/CRITERIA=CUT(**).
-LOGISTIC REGRESSION x WITH y/CRITERIA=CUT(0.5 **).
-LOGISTIC REGRESSION x WITH y/CRITERIA=**.
-])
-AT_CHECK([pspp -O format=csv logistic.sps], [1], [dnl
-"logistic.sps:2.21-2.22: error: LOGISTIC REGRESSION: Syntax error expecting variable name.
- 2 | LOGISTIC REGRESSION **.
- | ^~"
-
-"logistic.sps:3.23-3.24: error: LOGISTIC REGRESSION: Syntax error expecting `WITH'.
- 3 | LOGISTIC REGRESSION x **.
- | ^~"
-
-"logistic.sps:4.28-4.29: error: LOGISTIC REGRESSION: Syntax error expecting variable name.
- 4 | LOGISTIC REGRESSION x WITH **.
- | ^~"
-
-"logistic.sps:5.38-5.39: error: LOGISTIC REGRESSION: Syntax error expecting INCLUDE or EXCLUDE.
- 5 | LOGISTIC REGRESSION x WITH y/MISSING=**.
- | ^~"
-
-"logistic.sps:6.42-6.43: error: LOGISTIC REGRESSION: Syntax error expecting variable name.
- 6 | LOGISTIC REGRESSION x WITH y/CATEGORICAL=**.
- | ^~"
-
-"logistic.sps:7.39-7.40: error: LOGISTIC REGRESSION: Syntax error expecting `('.
- 7 | LOGISTIC REGRESSION x WITH y/PRINT=CI **.
- | ^~"
-
-"logistic.sps:8.39-8.40: error: LOGISTIC REGRESSION: Syntax error expecting number.
- 8 | LOGISTIC REGRESSION x WITH y/PRINT=CI(**).
- | ^~"
-
-"logistic.sps:9.43-9.44: error: LOGISTIC REGRESSION: Syntax error expecting `)'.
- 9 | LOGISTIC REGRESSION x WITH y/PRINT=CI(123 **).
- | ^~"
-
-"logistic.sps:10.36-10.37: error: LOGISTIC REGRESSION: Syntax error expecting DEFAULT, SUMMARY, CI, or ALL.
- 10 | LOGISTIC REGRESSION x WITH y/PRINT=**.
- | ^~"
-
-"logistic.sps:11.44-11.45: error: LOGISTIC REGRESSION: Syntax error expecting `('.
- 11 | LOGISTIC REGRESSION x WITH y/CRITERIA=BCON **.
- | ^~"
-
-"logistic.sps:12.44-12.45: error: LOGISTIC REGRESSION: Syntax error expecting number.
- 12 | LOGISTIC REGRESSION x WITH y/CRITERIA=BCON(**).
- | ^~"
-
-"logistic.sps:13.48-13.49: error: LOGISTIC REGRESSION: Syntax error expecting `)'.
- 13 | LOGISTIC REGRESSION x WITH y/CRITERIA=BCON(123 **).
- | ^~"
-
-"logistic.sps:14.47-14.48: error: LOGISTIC REGRESSION: Syntax error expecting `('.
- 14 | LOGISTIC REGRESSION x WITH y/CRITERIA=ITERATE **.
- | ^~"
-
-"logistic.sps:15.47-15.48: error: LOGISTIC REGRESSION: Syntax error expecting non-negative integer for ITERATE.
- 15 | LOGISTIC REGRESSION x WITH y/CRITERIA=ITERATE(**).
- | ^~"
-
-"logistic.sps:16.51-16.52: error: LOGISTIC REGRESSION: Syntax error expecting `)'.
- 16 | LOGISTIC REGRESSION x WITH y/CRITERIA=ITERATE(123 **).
- | ^~"
-
-"logistic.sps:17.44-17.45: error: LOGISTIC REGRESSION: Syntax error expecting `('.
- 17 | LOGISTIC REGRESSION x WITH y/CRITERIA=LCON **.
- | ^~"
-
-"logistic.sps:18.44-18.45: error: LOGISTIC REGRESSION: Syntax error expecting number.
- 18 | LOGISTIC REGRESSION x WITH y/CRITERIA=LCON(**).
- | ^~"
-
-"logistic.sps:19.48-19.49: error: LOGISTIC REGRESSION: Syntax error expecting `)'.
- 19 | LOGISTIC REGRESSION x WITH y/CRITERIA=LCON(123 **).
- | ^~"
-
-"logistic.sps:20.43-20.44: error: LOGISTIC REGRESSION: Syntax error expecting `('.
- 20 | LOGISTIC REGRESSION x WITH y/CRITERIA=EPS **.
- | ^~"
-
-"logistic.sps:21.43-21.44: error: LOGISTIC REGRESSION: Syntax error expecting number.
- 21 | LOGISTIC REGRESSION x WITH y/CRITERIA=EPS(**).
- | ^~"
-
-"logistic.sps:22.47-22.48: error: LOGISTIC REGRESSION: Syntax error expecting `)'.
- 22 | LOGISTIC REGRESSION x WITH y/CRITERIA=EPS(123 **).
- | ^~"
-
-"logistic.sps:23.43-23.44: error: LOGISTIC REGRESSION: Syntax error expecting `('.
- 23 | LOGISTIC REGRESSION x WITH y/CRITERIA=CUT **.
- | ^~"
-
-"logistic.sps:24.43-24.44: error: LOGISTIC REGRESSION: Syntax error expecting number between 0 and 1 for CUT.
- 24 | LOGISTIC REGRESSION x WITH y/CRITERIA=CUT(**).
- | ^~"
-
-"logistic.sps:25.47-25.48: error: LOGISTIC REGRESSION: Syntax error expecting `)'.
- 25 | LOGISTIC REGRESSION x WITH y/CRITERIA=CUT(0.5 **).
- | ^~"
-
-"logistic.sps:26.39-26.40: error: LOGISTIC REGRESSION: Syntax error expecting BCON, ITERATE, LCON, EPS, or CUT.
- 26 | LOGISTIC REGRESSION x WITH y/CRITERIA=**.
- | ^~"
-])
-AT_CLEANUP
\ No newline at end of file
projects / pspp / blobdiff