X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=tests%2Flanguage%2Fstats%2Flogistic.at;h=93882d336c02802750c6fe80f5f947155a436ce0;hb=cee6f0eb54144da7034566fa1bcdcee22337ae6a;hp=3d7ae76c38de9de5ac8635edea76e58799fc3b37;hpb=5dbf5abcbed01f04422d4dead1c0ae0bb7efde4f;p=pspp diff --git a/tests/language/stats/logistic.at b/tests/language/stats/logistic.at index 3d7ae76c38..93882d336c 100644 --- a/tests/language/stats/logistic.at +++ b/tests/language/stats/logistic.at @@ -1,16 +1,16 @@ dnl PSPP - a program for statistical analysis. dnl Copyright (C) 2017 Free Software Foundation, Inc. -dnl +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 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 dnl You should have received a copy of the GNU General Public License dnl along with this program. If not, see . dnl @@ -95,6 +95,7 @@ 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 @@ -109,44 +110,43 @@ logistic regression . ]) -AT_CHECK([pspp -O format=csv lr-data.sps], [0], - [dnl -Table: Dependent Variable Encoding +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 -1.000,0 -2.000,1 +1.000,.000 +2.000,1.000 Table: Case Processing Summary Unweighted Cases,N,Percent -Included in Analysis,66,100.000 -Missing Cases,0,.000 -Total,66,100.000 +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 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square -,37.323,.455,.659 +Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square +1,37.323,.455,.659 Table: Classification Table -,,,Predicted,, -,,,outcome,,"Percentage -Correct" -,Observed,,1.000,2.000, -Step 1,outcome,1.000,43,5,89.583 -,,2.000,4,14,77.778 -,Overall Percentage,,,,86.364 +,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 @@ -178,13 +178,13 @@ 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,66,100.000 -< Missing Cases,0,.000 -< Total,66,100.000 +< Included in Analysis,66,100.0% +< Missing Cases,0,.0% +< Total,66,100.0% --- -> Included in Analysis,66,94.286 -> Missing Cases,4,5.714 -> Total,70,100.000 +> Included in Analysis,66,94.3% +> Missing Cases,4,5.7% +> Total,70,100.0% ]) AT_CLEANUP @@ -195,6 +195,7 @@ 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 @@ -233,19 +234,19 @@ 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,66,100.000 +< Included in Analysis,66,100.0% --- -> Included in Analysis,63,100.000 +> Included in Analysis,63,100.0% 10c10 -< Total,66,100.000 +< Total,66,100.0% --- -> Total,63,100.000 -23,24c23,24 -< Step 1,outcome,1.000,43,5,89.583 -< ,,2.000,4,14,77.778 +> 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,1.000,43.000,5.000,89.583 -> ,,2.000,4.000,14.000,77.778 +> Step 1,outcome,1.000,43.000,5.000,89.6% +> ,,2.000,4.000,14.000,77.8% ]) @@ -256,6 +257,7 @@ 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. @@ -273,33 +275,31 @@ compute constant = 1. logistic regression female with constant /noconst. ]) -AT_CHECK([pspp -O format=csv non-const.sps], [0], - [dnl +AT_CHECK([pspp -O format=csv non-const.sps], [0], [dnl Table: Dependent Variable Encoding Original Value,Internal Value -.00,0 -1.00,1 +.00,.000 +1.00,1.000 Table: Case Processing Summary Unweighted Cases,N,Percent -Included in Analysis,200,100.000 -Missing Cases,0,.000 -Total,200,100.000 +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 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square -,275.637,.008,.011 +Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square +1,275.637,.008,.011 Table: Classification Table -,,,Predicted,, -,,,female,,"Percentage -Correct" -,Observed,,.00,1.00, -Step 1,female,.00,0,91,.000 -,,1.00,0,109,100.000 -,Overall Percentage,,,,54.500 +,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) @@ -313,6 +313,7 @@ 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. @@ -336,410 +337,411 @@ 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 +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 + 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 @@ -753,41 +755,39 @@ logistic regression . ]) -AT_CHECK([pspp -O format=csv lr-cat.sps], [0], - [dnl +AT_CHECK([pspp -O format=csv lr-cat.sps], [0], [dnl Table: Dependent Variable Encoding Original Value,Internal Value -4.000,0 -9.000,1 +4.000,.000 +9.000,1.000 Table: Case Processing Summary Unweighted Cases,N,Percent -Included in Analysis,400,100.000 -Missing Cases,0,.000 -Total,400,100.000 +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 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square -,458.517,.098,.138 +Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square +1,458.517,.098,.138 Table: Categorical Variables' Codings -,,,Parameter coding,, -,,Frequency,(1),(2),(3) +,,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 -,,,Predicted,, -,,,y,,"Percentage -Correct" -,Observed,,4.000,9.000, -Step 1,y,4.000,254,19,93.040 -,,9.000,97,30,23.622 -,Overall Percentage,,,,71.000 +,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) @@ -799,219 +799,219 @@ Step 1,b1,.002,.001,4.284,1,.038,1.002 ,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 + 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. +set format=F20.3 /small=0. data list notable file='lr-cat2.data' list /read honcomp wiz science *. string ses(a1). @@ -1022,40 +1022,38 @@ logistic regression honcomp with read science ses ]) -AT_CHECK([pspp -O format=csv stringcat.sps], [0], - [dnl +AT_CHECK([pspp -O format=csv stringcat.sps], [0], [dnl Table: Dependent Variable Encoding Original Value,Internal Value -.000,0 -1.000,1 +.000,.000 +1.000,1.000 Table: Case Processing Summary Unweighted Cases,N,Percent -Included in Analysis,200,100.000 -Missing Cases,0,.000 -Total,200,100.000 +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 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square -,165.701,.280,.408 +Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square +1,165.701,.280,.408 Table: Categorical Variables' Codings -,,,Parameter coding, -,,Frequency,(1),(2) +,,Frequency,Parameter coding, +,,,(1),(2) ses,a,47,1,0 ,b,95,0,1 ,c,58,0,0 Table: Classification Table -,,,Predicted,, -,,,honcomp,,"Percentage -Correct" -,Observed,,.000,1.000, -Step 1,honcomp,.000,132,15,89.796 -,,1.000,26,27,50.943 -,Overall Percentage,,,,79.500 +,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) @@ -1073,6 +1071,7 @@ 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*. @@ -1093,108 +1092,109 @@ 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 + .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 @@ -1213,13 +1213,13 @@ AT_CHECK([pspp -O format=csv miss.sps > file2], [0], [ignore]) AT_CHECK([diff file1 file2], [1], [dnl 8,10c8,10 -< Included in Analysis,100,100.00 -< Missing Cases,0,.00 -< Total,100,100.00 +< Included in Analysis,100,100.0% +< Missing Cases,0,.0% +< Total,100,100.0% --- -> Included in Analysis,100,99.01 -> Missing Cases,1,.99 -> Total,101,100.00 +> Included in Analysis,100,99.0% +> Missing Cases,1,1.0% +> Total,101,100.0% ]) AT_CLEANUP @@ -1230,210 +1230,211 @@ 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 +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 +logistic regression disease WITH age sciostat sector savings /categorical = sciostat sector /print = ci(95). @@ -1442,24 +1443,24 @@ logistic regression AT_CHECK([pspp -O format=csv ci.sps], [0], [dnl Table: Dependent Variable Encoding Original Value,Internal Value -.000,0 -1.000,1 +.000,.000 +1.000,1.000 Table: Case Processing Summary Unweighted Cases,N,Percent -Included in Analysis,196,100.000 -Missing Cases,0,.000 -Total,196,100.000 +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 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square -,211.195,.120,.172 +Step,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square +1,211.195,.120,.172 Table: Categorical Variables' Codings -,,,Parameter coding, -,,Frequency,(1),(2) +,,Frequency,Parameter coding, +,,,(1),(2) sciostat,1.000,77,1,0 ,2.000,49,0,1 ,3.000,70,0,0 @@ -1467,17 +1468,16 @@ sector,1.000,117,1, ,2.000,79,0, Table: Classification Table -,,,Predicted,, -,,,disease,,"Percentage -Correct" -,Observed,,.000,1.000, -Step 1,disease,.000,131,8,94.245 -,,1.000,41,16,28.070 -,Overall Percentage,,,,75.000 +,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 -,,,,,,,,95% CI for Exp(B), -,,B,S.E.,Wald,df,Sig.,Exp(B),Lower,Upper +,,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,,,