+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 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_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,1.000,43,5,89.6%
+,,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
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).
+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,66,100.000
-< 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,66,95.652
-> Missing Cases,3,4.348
-> Total,69,100.000
+> Included in Analysis,66,94.3%
+> Missing Cases,4,5.7%
+> Total,70,100.0%
])
AT_CLEANUP
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
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
+> 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%
])
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.
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,.0%
+,,1.00,0,109,100.0%
+,Overall Percentage,,,,54.5%
Table: Variables in the Equation
,,B,S.E.,Wald,df,Sig.,Exp(B)
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.
error: Dependent variable's values are not dichotomous.
])
-AT_CLEANUP
\ No newline at end of file
+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
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+ 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
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+ 580 3.20 2 9
+ 580 3.50 2 4
+ 620 3.30 1 4
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+ 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
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+ 600 3.54 1 9
+ 540 3.38 4 4
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+ 360 2.56 3 4
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+ 660 3.07 3 4
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+ 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
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+ 600 3.28 3 4
+ 600 2.82 4 4
+ 460 3.58 2 4
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+ 740 3.52 4 9
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+ 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
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+ 480 3.57 2 4
+ 540 3.51 2 4
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+ 680 3.64 3 4
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+ 520 3.51 2 4
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+ 760 4.00 1 9
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+ 680 2.96 3 9
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+ 580 3.77 4 4
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+ 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
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+ 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
+,,,Predicted,,
+,,,y,,Percentage Correct
+,Observed,,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.
+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
+,,,Predicted,,
+,,,honcomp,,Percentage Correct
+,Observed,,.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
+,,,Predicted,,
+,,,disease,,Percentage Correct
+,Observed,,.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
+
projects / pspp / blobdiff