| blob | b7285e26d1e3f17ba28bc51d8c359cd24c8ccd2d |
1 AT_BANNER([LOGISTIC REGRESSION])
3 dnl These examples are adapted from
4 dnl http://www.uvm.edu/~dhowell/gradstat/psych341/lectures/Logistic%20Regression/LogisticReg1.html
8 m4_define([LOGIT_TEST_DATA],
9 [AT_DATA([lr-data.txt], dnl
10 105.00 1.00 33.00 3.00 2.00 .35 17.00 20.00 .50110 -2.00440 1
11 106.00 1.00 50.00 2.00 3.00 .38 7.00 15.00 .20168 -1.25264 1
12 107.00 1.00 91.00 3.00 2.00 .28 15.00 7.00 .00897 -1.00905 1
13 108.00 1.00 90.00 3.00 2.00 .20 2.00 2.00 .00972 -1.00982 1
14 109.00 1.00 70.00 3.00 3.00 .38 23.00 27.00 .04745 -1.04981 1
15 111.00 2.00 31.00 2.00 2.00 .00 19.00 10.00 .54159 1.84640 1
16 112.00 1.00 91.00 2.00 3.00 .18 6.00 16.00 .00897 -1.00905 1
17 113.00 1.00 81.00 3.00 2.00 .00 3.00 9.00 .01998 -1.02039 1
18 114.00 2.00 15.00 1.00 2.00 .13 19.00 13.00 .81241 1.23090 1
19 116.00 2.00 1.00 1.00 2.00 .88 15.00 7.00 .93102 1.07410 1
20 117.00 1.00 93.00 3.00 2.00 .18 9.00 15.00 .00764 -1.00770 1
21 118.00 2.00 14.00 1.00 3.00 .15 23.00 18.00 .82447 1.21289 1
22 120.00 1.00 91.00 2.00 2.00 .43 17.00 14.00 .00897 -1.00905 1
23 121.00 1.00 55.00 3.00 2.00 .69 20.00 14.00 .14409 -1.16834 1
24 122.00 1.00 70.00 2.00 3.00 .03 .00 6.00 .04745 -1.04981 1
25 123.00 1.00 25.00 2.00 2.00 .45 4.00 10.00 .65789 -2.92301 1
26 125.00 1.00 91.00 2.00 2.00 .13 .00 3.00 .00897 -1.00905 1
27 126.00 1.00 91.00 3.00 3.00 .23 4.00 6.00 .00897 -1.00905 1
28 127.00 1.00 91.00 3.00 2.00 .00 8.00 8.00 .00897 -1.00905 1
29 128.00 2.00 13.00 2.00 2.00 .65 16.00 14.00 .83592 1.19629 1
30 129.00 1.00 50.00 2.00 2.00 .25 20.00 23.00 .20168 -1.25264 1
31 135.00 1.00 90.00 3.00 3.00 .03 5.00 12.00 .00972 -1.00982 1
32 138.00 1.00 70.00 3.00 3.00 .10 1.00 6.00 .04745 -1.04981 1
33 139.00 2.00 19.00 3.00 3.00 .10 11.00 12.00 .75787 1.31949 1
34 149.00 2.00 50.00 3.00 2.00 .03 .00 .00 .20168 4.95826 1
35 204.00 1.00 50.00 3.00 1.00 .13 .00 1.00 .20168 -1.25264 1
36 205.00 1.00 91.00 3.00 3.00 .72 16.00 18.00 .00897 -1.00905 1
37 206.00 2.00 24.00 1.00 1.00 .10 5.00 21.00 .67592 1.47947 1
38 207.00 1.00 80.00 3.00 3.00 .13 6.00 7.00 .02164 -1.02212 1
39 208.00 1.00 87.00 2.00 2.00 .18 9.00 20.00 .01237 -1.01253 1
40 209.00 1.00 70.00 2.00 2.00 .53 15.00 12.00 .04745 -1.04981 1
41 211.00 1.00 55.00 2.00 1.00 .33 8.00 5.00 .14409 -1.16834 1
42 212.00 1.00 56.00 3.00 1.00 .30 6.00 20.00 .13436 -1.15522 1
43 214.00 1.00 54.00 2.00 2.00 .15 .00 16.00 .15439 -1.18258 1
44 215.00 1.00 71.00 3.00 3.00 .35 12.00 12.00 .04391 -1.04592 1
45 217.00 2.00 36.00 1.00 1.00 .10 12.00 8.00 .44049 2.27020 1
46 218.00 1.00 91.00 2.00 2.00 .05 11.00 25.00 .00897 -1.00905 1
47 219.00 1.00 91.00 2.00 2.00 1.23 11.00 24.00 .00897 -1.00905 1
48 220.00 1.00 91.00 2.00 3.00 .08 8.00 11.00 .00897 -1.00905 1
49 221.00 1.00 91.00 2.00 2.00 .33 5.00 11.00 .00897 -1.00905 1
50 222.00 2.00 36.00 2.00 1.00 .18 5.00 3.00 .44049 2.27020 1
51 223.00 1.00 70.00 2.00 3.00 .18 14.00 3.00 .04745 -1.04981 1
52 224.00 1.00 91.00 2.00 2.00 .43 2.00 10.00 .00897 -1.00905 1
53 225.00 1.00 55.00 2.00 1.00 .18 6.00 11.00 .14409 -1.16834 1
54 229.00 2.00 75.00 2.00 2.00 .40 30.00 25.00 .03212 31.12941 1
55 232.00 1.00 91.00 3.00 2.00 .15 6.00 3.00 .00897 -1.00905 1
56 233.00 1.00 70.00 2.00 1.00 .00 11.00 8.00 .04745 -1.04981 1
57 234.00 1.00 54.00 3.00 2.00 .10 .00 .00 .15439 -1.18258 1
58 237.00 1.00 70.00 3.00 2.00 .18 5.00 25.00 .04745 -1.04981 1
59 241.00 1.00 19.00 2.00 3.00 .33 13.00 9.00 .75787 -4.12995 1
60 304.00 2.00 18.00 2.00 2.00 .26 25.00 6.00 .77245 1.29458 1
61 305.00 1.00 88.00 3.00 2.00 1.35 17.00 29.00 .01142 -1.01155 1
62 306.00 1.00 70.00 2.00 3.00 .63 14.00 33.00 .04745 -1.04981 1
63 307.00 1.00 85.00 2.00 2.00 2.65 18.00 14.00 .01452 -1.01474 1
64 308.00 1.00 13.00 2.00 2.00 .23 5.00 5.00 .83592 -6.09442 1
65 309.00 2.00 13.00 2.00 2.00 .23 7.00 17.00 .83592 1.19629 1
66 311.00 2.00 1.00 2.00 2.00 .50 20.00 14.00 .93102 1.07410 1
67 315.00 1.00 19.00 2.00 3.00 .18 1.00 11.00 .75787 -4.12995 1
68 316.00 1.00 88.00 2.00 2.00 .38 12.00 11.00 .01142 -1.01155 2
69 318.00 1.00 88.00 3.00 2.00 .03 5.00 5.00 .01142 -1.01155 3
70 319.00 2.00 18.00 2.00 3.00 .30 15.00 16.00 .77245 1.29458 1
71 321.00 2.00 15.00 2.00 2.00 .63 15.00 18.00 .81241 1.23090 1
72 322.00 1.00 88.00 3.00 2.00 .40 18.00 15.00 .01142 -1.01155 1
73 325.00 2.00 18.00 2.00 3.00 1.00 28.00 18.00 .77245 1.29458 1
74 329.00 1.00 88.00 3.00 2.00 .03 7.00 11.00 .01142 -1.01155 4
75 332.00 2.00 2.00 2.00 2.00 .05 8.00 9.00 .92562 1.08036 1
76 )])
78 dnl Note: In the above data cases 305, 316 318 and 329 have identical values
79 dnl of the 2nd and 3rd variables. We use this for weight testing.
81 AT_SETUP([LOGISTIC REGRESSION basic test])
83 LOGIT_TEST_DATA
85 AT_DATA([lr-data.sps], [dnl
86 set format = F12.3.
87 set decimal dot.
88 data list notable file='lr-data.txt'
89 list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
91 logistic regression
92 variables = outcome with survrate
93 .
94 ])
96 AT_CHECK([pspp -O format=csv lr-data.sps], [0],
97 [dnl
98 Table: Dependent Variable Encoding
99 Original Value,Internal Value
100 1.000,0
101 2.000,1
103 Table: Case Processing Summary
104 Unweighted Cases,N,Percent
105 Included in Analysis,66,100.000
106 Missing Cases,0,.000
107 Total,66,100.000
109 note: Estimation terminated at iteration number 6 because parameter estimates changed by less than 0.001
111 Table: Model Summary
112 Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
113 ,37.323,.455,.659
115 Table: Classification Table
116 ,,,Predicted,,
117 ,,,outcome,,"Percentage
118 Correct"
119 ,Observed,,1.000,2.000,
120 Step 1,outcome,1.000,43,5,89.583
121 ,,2.000,4,14,77.778
122 ,Overall Percentage,,,,86.364
124 Table: Variables in the Equation
125 ,,B,S.E.,Wald,df,Sig.,Exp(B)
126 Step 1,survrate,-.081,.019,17.756,1,.000,.922
127 ,Constant,2.684,.811,10.941,1,.001,14.639
128 ])
131 AT_CLEANUP
133 AT_SETUP([LOGISTIC REGRESSION missing values])
135 LOGIT_TEST_DATA
137 AT_DATA([lr-data.sps], [dnl
138 set format = F12.3.
139 set decimal dot.
140 data list notable file='lr-data.txt'
141 list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
143 missing values survrate (999) avoid (44444) outcome (99).
145 logistic regression
146 variables = outcome with survrate avoid
147 .
148 ])
150 AT_CHECK([pspp -O format=csv lr-data.sps > run0], [0], [ignore])
152 dnl Append some cases with missing values into the data.
153 cat >> lr-data.txt << HERE
154 105.00 1.00 999.00 3.00 2.00 .35 17.00 20.00 .50110 -2.00440 1
155 106.00 1.00 999.00 2.00 3.00 .38 7.00 15.00 .20168 -1.25264 1
156 107.00 1.00 5.00 3.00 2.00 .28 44444 34 .00897 -1.00905 1
157 108.00 99 5.00 3.00 2.00 .28 4 34 .00897 -1.00905 1
158 HERE
160 AT_CHECK([pspp -O format=csv lr-data.sps > run1], [0], [ignore])
162 dnl Only the summary information should be different
163 AT_CHECK([diff run0 run1], [1], [dnl
164 8,10c8,10
165 < Included in Analysis,66,100.000
166 < Missing Cases,0,.000
167 < Total,66,100.000
168 ---
169 > Included in Analysis,66,94.286
170 > Missing Cases,4,5.714
171 > Total,70,100.000
172 ])
174 AT_CLEANUP
178 dnl Check that a weighted dataset is interpreted correctly
179 dnl To do this, the same data set is used, one weighted, one not.
180 dnl The weighted dataset omits certain cases which are identical
181 AT_SETUP([LOGISTIC REGRESSION weights])
183 LOGIT_TEST_DATA
185 AT_DATA([lr-data-unweighted.sps], [dnl
186 set format = F12.3.
187 set decimal dot.
188 data list notable file='lr-data.txt'
189 list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
191 logistic regression
192 variables = outcome with survrate
193 .
194 ])
196 AT_DATA([lr-data-weighted.sps], [dnl
197 set format = F12.3.
198 set decimal dot.
199 data list notable file='lr-data.txt'
200 list /id outcome survrate prognos amttreat gsi avoid intrus pre_1 lre_1 w *.
202 weight by w.
204 * Omit duplicate cases.
205 select if id <> 305 and id <> 316 and id <> 318.
207 logistic regression
208 variables = outcome with survrate
209 .
210 ])
213 AT_CHECK([pspp -O format=csv lr-data-unweighted.sps > unweighted-result], [0], [ignore])
214 AT_CHECK([pspp -O format=csv lr-data-weighted.sps > weighted-result], [0], [ignore])
216 dnl The only difference should be the summary information, since
217 dnl this displays the unweighted totals.
218 AT_CHECK([diff unweighted-result weighted-result], [1], [dnl
219 8c8
220 < Included in Analysis,66,100.000
221 ---
222 > Included in Analysis,63,100.000
223 10c10
224 < Total,66,100.000
225 ---
226 > Total,63,100.000
227 23,24c23,24
228 < Step 1,outcome,1.000,43,5,89.583
229 < ,,2.000,4,14,77.778
230 ---
231 > Step 1,outcome,1.000,43.000,5.000,89.583
232 > ,,2.000,4.000,14.000,77.778
233 ])
236 AT_CLEANUP
239 dnl Check that the /NOCONST option works as intended.
240 dnl The results this produces are very similar to those
241 dnl at the example in http://www.ats.ucla.edu/stat/SPSS/faq/logregconst.htm
242 AT_SETUP([LOGISTIC REGRESSION without constant])
244 AT_DATA([non-const.sps], [dnl
245 set format=F20.3.
247 input program.
248 loop #i = 1 to 200.
249 compute female = (#i > 91).
250 end case.
251 end loop.
252 end file.
253 end input program.
255 compute constant = 1.
257 logistic regression female with constant /noconst.
258 ])
260 AT_CHECK([pspp -O format=csv non-const.sps], [0],
261 [dnl
262 Table: Dependent Variable Encoding
263 Original Value,Internal Value
264 .00,0
265 1.00,1
267 Table: Case Processing Summary
268 Unweighted Cases,N,Percent
269 Included in Analysis,200,100.000
270 Missing Cases,0,.000
271 Total,200,100.000
273 note: Estimation terminated at iteration number 2 because parameter estimates changed by less than 0.001
275 Table: Model Summary
276 Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
277 ,275.637,.008,.011
279 Table: Classification Table
280 ,,,Predicted,,
281 ,,,female,,"Percentage
282 Correct"
283 ,Observed,,.00,1.00,
284 Step 1,female,.00,0,91,.000
285 ,,1.00,0,109,100.000
286 ,Overall Percentage,,,,54.500
288 Table: Variables in the Equation
289 ,,B,S.E.,Wald,df,Sig.,Exp(B)
290 Step 1,constant,.180,.142,1.616,1,.204,1.198
291 ])
293 AT_CLEANUP
297 dnl Check that if somebody passes a dependent variable which is not dichtomous,
298 dnl then an error is raised.
299 AT_SETUP([LOGISTIC REGRESSION non-dichotomous dep var])
301 AT_DATA([non-dich.sps], [dnl
302 data list notable list /y x1 x2 x3 x4.
303 begin data.
304 1 2 3 4 5
305 0 2 3 4 8
306 2 3 4 5 6
307 end data.
309 logistic regression y with x1 x2 x3 x4.
310 ])
312 AT_CHECK([pspp -O format=csv non-dich.sps], [1],
313 [dnl
314 error: Dependent variable's values are not dichotomous.
315 ])
317 AT_CLEANUP
321 dnl An example to check the behaviour of LOGISTIC REGRESSION with a categorical
322 dnl variable. This examṕle was inspired from that at:
323 dnl http://www.ats.ucla.edu/stat/spss/dae/logit.htm
324 AT_SETUP([LOGISTIC REGRESSION with categorical])
326 AT_DATA([lr-cat.data], [dnl
327 620 3.07 2 4
328 800 4.00 3 9
329 580 3.40 2 4
330 600 3.13 2 4
331 540 2.70 2 4
332 660 3.31 4 4
333 480 3.58 1 9
334 620 4.00 1 9
335 680 3.98 2 9
336 580 3.40 4 4
337 760 3.35 3 4
338 700 3.72 2 4
339 460 3.64 1 9
340 540 3.28 3 4
341 680 3.48 3 4
342 740 3.31 1 4
343 460 3.77 3 4
344 740 3.54 1 4
345 600 3.63 3 4
346 620 3.05 2 4
347 560 3.04 3 4
348 520 2.70 3 4
349 640 3.35 3 4
350 620 3.58 2 4
351 660 3.70 4 9
352 500 2.86 4 4
353 640 3.50 2 4
354 720 4.00 3 4
355 720 3.94 3 4
356 400 3.65 2 4
357 800 2.90 2 4
358 520 2.90 3 4
359 440 3.24 4 4
360 580 3.51 2 4
361 500 3.31 3 4
362 440 3.22 1 4
363 540 3.17 1 9
364 420 3.02 1 4
365 780 3.22 2 9
366 440 3.13 4 4
367 800 3.66 1 9
368 580 3.32 2 9
369 480 2.67 2 9
370 700 4.00 1 9
371 740 2.97 2 9
372 700 3.83 2 4
373 640 3.93 2 4
374 800 3.90 2 4
375 400 3.38 2 4
376 700 3.52 2 4
377 680 3.00 4 9
378 540 3.20 1 4
379 580 4.00 2 4
380 780 4.00 2 9
381 220 2.83 3 4
382 580 3.20 2 9
383 580 3.50 2 4
384 620 3.30 1 4
385 520 3.65 4 9
386 600 3.38 3 9
387 660 3.77 3 4
388 580 2.86 4 9
389 580 3.46 2 9
390 560 3.36 3 4
391 740 4.00 3 9
392 480 3.44 3 4
393 640 3.19 4 9
394 600 3.54 1 9
395 540 3.38 4 4
396 500 2.81 3 4
397 360 2.56 3 4
398 460 3.15 4 4
399 460 2.63 2 4
400 440 2.76 2 4
401 740 3.62 4 4
402 380 3.38 2 4
403 640 3.63 1 9
404 800 3.73 1 4
405 660 3.67 2 4
406 760 3.00 2 9
407 420 2.96 1 4
408 740 3.74 4 4
409 800 3.75 2 4
410 620 3.40 2 4
411 660 3.67 3 9
412 400 3.35 3 4
413 680 3.14 2 4
414 660 3.47 3 9
415 660 3.63 2 9
416 420 3.41 4 4
417 660 4.00 1 4
418 680 3.70 2 4
419 620 3.23 3 9
420 520 3.35 3 4
421 500 4.00 3 4
422 400 3.36 2 4
423 700 3.56 1 9
424 540 3.81 1 9
425 520 2.68 3 9
426 540 3.50 2 4
427 700 4.00 2 4
428 600 3.64 3 9
429 800 3.31 3 4
430 520 3.29 1 4
431 580 3.69 1 4
432 380 3.43 3 4
433 560 3.19 3 4
434 760 2.81 1 9
435 540 3.13 2 4
436 660 3.14 2 9
437 520 3.81 1 9
438 680 3.19 4 4
439 540 3.78 4 4
440 500 3.57 3 4
441 660 3.49 2 4
442 340 3.00 2 9
443 400 3.15 2 9
444 420 3.92 4 4
445 760 3.35 2 9
446 700 2.94 2 4
447 540 3.04 1 4
448 780 3.87 4 4
449 560 3.78 2 4
450 700 3.82 3 4
451 400 2.93 3 4
452 440 3.45 2 9
453 800 3.47 3 4
454 340 3.15 3 4
455 520 4.00 1 9
456 520 3.15 3 4
457 600 2.98 2 9
458 420 2.69 2 4
459 460 3.44 2 4
460 620 3.71 1 9
461 480 3.13 2 4
462 580 3.40 3 4
463 540 3.39 3 9
464 540 3.94 3 4
465 440 2.98 3 4
466 380 3.59 4 4
467 500 2.97 4 4
468 340 2.92 3 4
469 440 3.15 2 4
470 600 3.48 2 4
471 420 2.67 3 4
472 460 3.07 2 4
473 460 3.45 3 9
474 480 3.39 4 4
475 480 2.78 3 4
476 720 3.42 2 9
477 680 3.67 2 9
478 800 3.89 2 4
479 360 3.00 3 4
480 620 3.17 2 9
481 700 3.52 4 9
482 540 3.19 2 4
483 580 3.30 2 4
484 800 4.00 3 9
485 660 3.33 2 4
486 380 3.34 3 4
487 720 3.84 3 4
488 600 3.59 2 4
489 500 3.03 3 4
490 640 3.81 2 4
491 540 3.49 1 9
492 680 3.85 3 9
493 540 3.84 2 9
494 460 2.93 3 4
495 380 2.94 3 4
496 620 3.22 2 4
497 740 3.37 4 4
498 620 4.00 2 4
499 800 3.74 1 9
500 400 3.31 3 4
501 540 3.46 4 4
502 620 3.18 2 9
503 480 2.91 1 9
504 300 2.84 2 9
505 440 2.48 4 4
506 640 2.79 2 4
507 400 3.23 4 9
508 680 3.46 2 9
509 620 3.37 1 9
510 700 3.92 2 4
511 620 3.37 2 9
512 620 3.63 2 4
513 620 3.95 3 9
514 560 2.52 2 4
515 520 2.62 2 4
516 600 3.35 2 4
517 700 4.00 1 4
518 640 3.67 3 4
519 640 4.00 3 4
520 520 2.93 4 4
521 620 3.21 4 4
522 680 3.99 3 4
523 660 3.34 3 4
524 700 3.45 3 4
525 560 3.36 1 9
526 800 2.78 2 4
527 500 3.88 4 4
528 700 3.65 2 4
529 680 3.76 3 9
530 660 3.07 3 4
531 580 3.46 4 4
532 460 2.87 2 4
533 600 3.31 4 4
534 620 3.94 4 4
535 400 3.05 2 4
536 800 3.43 2 9
537 600 3.58 1 9
538 580 3.36 2 4
539 540 3.16 3 4
540 500 2.71 2 4
541 600 3.28 3 4
542 600 2.82 4 4
543 460 3.58 2 4
544 520 2.85 3 4
545 740 3.52 4 9
546 500 3.95 4 4
547 560 3.61 3 4
548 620 3.45 2 9
549 640 3.51 2 4
550 660 3.44 2 9
551 660 2.91 3 9
552 540 3.28 1 4
553 560 2.98 1 9
554 800 3.97 1 4
555 720 3.77 3 4
556 720 3.64 1 9
557 480 3.71 4 9
558 680 3.34 2 4
559 680 3.11 2 4
560 540 2.81 3 4
561 620 3.75 2 9
562 540 3.12 1 4
563 560 3.48 2 9
564 720 3.40 3 4
565 680 3.90 1 4
566 640 3.76 3 4
567 560 3.16 1 4
568 520 3.30 2 9
569 640 3.12 3 4
570 580 3.57 3 4
571 540 3.55 4 9
572 780 3.63 4 9
573 600 3.89 1 9
574 800 4.00 1 9
575 580 3.29 4 4
576 360 3.27 3 4
577 800 4.00 2 9
578 640 3.52 4 4
579 720 3.45 4 4
580 580 3.06 2 4
581 580 3.02 2 4
582 500 3.60 3 9
583 580 3.12 3 9
584 600 2.82 4 4
585 620 3.99 3 4
586 700 4.00 3 4
587 480 4.00 2 4
588 560 2.95 2 4
589 560 4.00 3 4
590 560 2.65 3 9
591 400 3.08 2 4
592 480 2.62 2 9
593 640 3.86 3 4
594 480 3.57 2 4
595 540 3.51 2 4
596 380 3.33 4 4
597 680 3.64 3 4
598 400 3.51 3 4
599 340 2.90 1 4
600 700 3.08 2 4
601 480 3.02 1 9
602 600 3.15 2 9
603 780 3.80 3 9
604 520 3.74 2 9
605 520 3.51 2 4
606 640 3.73 3 4
607 560 3.32 4 4
608 620 2.85 2 4
609 700 3.28 1 4
610 760 4.00 1 9
611 800 3.60 2 4
612 580 3.34 2 4
613 540 3.77 2 9
614 640 3.17 2 4
615 540 3.02 4 4
616 680 3.08 4 4
617 680 3.31 2 4
618 680 2.96 3 9
619 700 2.88 2 4
620 580 3.77 4 4
621 540 3.49 2 9
622 700 3.56 2 9
623 600 3.56 2 9
624 560 3.59 2 4
625 640 2.94 2 9
626 560 3.33 4 4
627 620 3.69 3 4
628 680 3.27 2 9
629 460 3.14 3 4
630 500 3.53 4 4
631 620 3.33 3 4
632 600 3.62 3 4
633 500 3.01 4 4
634 740 3.34 4 4
635 560 3.69 3 9
636 620 3.95 3 9
637 740 3.86 2 9
638 800 3.53 1 9
639 620 3.78 3 4
640 700 3.27 2 4
641 540 3.78 2 9
642 700 3.65 2 4
643 800 3.22 1 9
644 560 3.59 2 9
645 800 3.15 4 4
646 520 3.90 3 9
647 520 3.74 4 9
648 480 2.55 1 4
649 800 4.00 4 4
650 620 3.09 4 4
651 560 3.49 4 4
652 500 3.17 3 4
653 480 3.40 2 4
654 460 2.98 1 4
655 580 3.58 1 9
656 640 3.30 2 4
657 480 3.45 2 4
658 440 3.17 2 4
659 660 3.32 1 4
660 500 3.08 3 4
661 660 3.94 2 4
662 720 3.31 1 4
663 460 3.64 3 9
664 500 2.93 4 4
665 800 3.54 3 4
666 580 2.93 2 4
667 620 3.61 1 9
668 500 2.98 3 4
669 660 4.00 2 9
670 560 3.24 4 4
671 560 2.42 2 4
672 580 3.80 2 4
673 500 3.23 4 4
674 680 2.42 1 9
675 580 3.46 3 4
676 800 3.91 3 4
677 700 2.90 4 4
678 520 3.12 2 4
679 300 2.92 4 4
680 560 3.43 3 4
681 620 3.63 3 4
682 500 2.79 4 4
683 360 3.14 1 4
684 640 3.94 2 9
685 460 3.99 3 9
686 300 3.01 3 4
687 520 2.73 2 4
688 600 3.47 2 9
689 580 3.25 1 4
690 520 3.10 4 4
691 620 3.43 3 4
692 380 2.91 4 4
693 660 3.59 3 4
694 660 3.95 2 9
695 540 3.33 3 4
696 740 4.00 3 4
697 640 3.38 3 4
698 600 3.89 3 4
699 720 3.88 3 4
700 580 4.00 3 4
701 420 2.26 4 4
702 520 4.00 2 9
703 800 3.70 1 9
704 700 4.00 1 9
705 480 3.43 2 4
706 660 3.45 4 4
707 520 3.25 3 4
708 560 2.71 3 4
709 600 3.32 2 4
710 580 2.88 2 4
711 660 3.88 2 9
712 600 3.22 1 4
713 580 4.00 1 4
714 660 3.60 3 9
715 500 3.35 2 4
716 520 2.98 2 4
717 660 3.49 2 9
718 560 3.07 2 4
719 500 3.13 2 9
720 720 3.50 3 9
721 440 3.39 2 9
722 640 3.95 2 9
723 380 3.61 3 4
724 800 3.05 2 9
725 520 3.19 3 9
726 600 3.40 3 4
727 ])
729 AT_DATA([lr-cat.sps], [dnl
730 set format=F20.3.
732 data list notable list file='lr-cat.data' /b1 b2 bcat y.
734 logistic regression
735 y with b1 b2 bcat
736 /categorical = bcat
737 .
738 ])
740 AT_CHECK([pspp -O format=csv lr-cat.sps], [0],
741 [dnl
742 Table: Dependent Variable Encoding
743 Original Value,Internal Value
744 4.000,0
745 9.000,1
747 Table: Case Processing Summary
748 Unweighted Cases,N,Percent
749 Included in Analysis,400,100.000
750 Missing Cases,0,.000
751 Total,400,100.000
753 note: Estimation terminated at iteration number 4 because parameter estimates changed by less than 0.001
755 Table: Model Summary
756 Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
757 ,458.517,.098,.138
759 Table: Categorical Variables' Codings
760 ,,,Parameter coding,,
761 ,,Frequency,(1),(2),(3)
762 bcat,1.000,61,1,0,0
763 ,2.000,151,0,1,0
764 ,3.000,121,0,0,1
765 ,4.000,67,0,0,0
767 Table: Classification Table
768 ,,,Predicted,,
769 ,,,y,,"Percentage
770 Correct"
771 ,Observed,,4.000,9.000,
772 Step 1,y,4.000,254,19,93.040
773 ,,9.000,97,30,23.622
774 ,Overall Percentage,,,,71.000
776 Table: Variables in the Equation
777 ,,B,S.E.,Wald,df,Sig.,Exp(B)
778 Step 1,b1,.002,.001,4.284,1,.038,1.002
779 ,b2,.804,.332,5.872,1,.015,2.235
780 ,bcat,,,20.895,3,.000,
781 ,bcat(1),1.551,.418,13.788,1,.000,4.718
782 ,bcat(2),.876,.367,5.706,1,.017,2.401
783 ,bcat(3),.211,.393,.289,1,.591,1.235
784 ,Constant,-5.541,1.138,23.709,1,.000,.004
785 ])
787 AT_CLEANUP
791 dnl This example is inspired by http://www.ats.ucla.edu/stat/spss/output/logistic.htm
792 AT_SETUP([LOGISTIC REGRESSION with cat var 2])
794 AT_DATA([lr-cat2.data], [dnl
795 60.00 1.00 8.00 50.00
796 47.00 .00 9.00 42.00
797 57.00 1.00 7.00 53.00
798 60.00 .00 8.00 53.00
799 68.00 .00 8.00 66.00
800 63.00 .00 8.00 55.00
801 65.00 .00 8.00 63.00
802 52.00 .00 8.00 61.00
803 34.00 .00 9.00 42.00
804 37.00 .00 8.00 39.00
805 68.00 1.00 9.00 69.00
806 60.00 .00 9.00 61.00
807 44.00 .00 9.00 58.00
808 42.00 .00 8.00 47.00
809 57.00 1.00 7.00 61.00
810 55.00 1.00 8.00 50.00
811 55.00 .00 9.00 58.00
812 44.00 .00 8.00 63.00
813 50.00 1.00 9.00 66.00
814 44.00 .00 8.00 39.00
815 55.00 .00 8.00 58.00
816 44.00 .00 8.00 50.00
817 47.00 1.00 7.00 34.00
818 48.00 .00 8.00 44.00
819 45.00 .00 7.00 31.00
820 43.00 .00 8.00 50.00
821 39.00 .00 8.00 42.00
822 63.00 .00 9.00 50.00
823 47.00 .00 8.00 58.00
824 42.00 .00 7.00 50.00
825 50.00 .00 9.00 36.00
826 47.00 .00 7.00 33.00
827 60.00 .00 9.00 61.00
828 47.00 .00 7.00 42.00
829 68.00 1.00 9.00 69.00
830 52.00 .00 8.00 54.00
831 63.00 1.00 9.00 61.00
832 65.00 1.00 9.00 61.00
833 63.00 1.00 9.00 53.00
834 57.00 .00 8.00 51.00
835 34.00 .00 8.00 36.00
836 50.00 .00 8.00 39.00
837 52.00 1.00 7.00 56.00
838 45.00 .00 7.00 34.00
839 47.00 1.00 7.00 53.00
840 34.00 .00 7.00 39.00
841 50.00 1.00 8.00 55.00
842 60.00 .00 9.00 58.00
843 63.00 .00 8.00 58.00
844 35.00 .00 7.00 51.00
845 50.00 .00 8.00 58.00
846 68.00 .00 8.00 63.00
847 41.00 .00 9.00 34.00
848 47.00 .00 8.00 47.00
849 76.00 .00 9.00 64.00
850 44.00 .00 8.00 44.00
851 36.00 .00 9.00 50.00
852 68.00 1.00 9.00 55.00
853 47.00 1.00 8.00 50.00
854 50.00 .00 7.00 53.00
855 68.00 .00 8.00 74.00
856 39.00 .00 7.00 44.00
857 50.00 .00 8.00 55.00
858 52.00 .00 9.00 61.00
859 47.00 .00 8.00 53.00
860 39.00 .00 7.00 47.00
861 55.00 1.00 9.00 49.00
862 68.00 1.00 8.00 50.00
863 52.00 1.00 9.00 63.00
864 55.00 .00 8.00 58.00
865 57.00 .00 8.00 55.00
866 66.00 1.00 9.00 61.00
867 65.00 1.00 7.00 58.00
868 42.00 .00 7.00 42.00
869 68.00 1.00 7.00 59.00
870 60.00 1.00 9.00 61.00
871 52.00 .00 8.00 55.00
872 57.00 1.00 7.00 54.00
873 42.00 .00 9.00 50.00
874 42.00 .00 8.00 47.00
875 57.00 .00 8.00 50.00
876 47.00 .00 7.00 45.00
877 44.00 .00 7.00 40.00
878 43.00 .00 9.00 55.00
879 31.00 .00 8.00 39.00
880 37.00 .00 7.00 33.00
881 63.00 1.00 7.00 63.00
882 47.00 .00 8.00 39.00
883 57.00 1.00 8.00 63.00
884 52.00 .00 8.00 44.00
885 44.00 .00 7.00 35.00
886 52.00 .00 7.00 55.00
887 55.00 .00 7.00 69.00
888 52.00 .00 8.00 53.00
889 55.00 .00 9.00 61.00
890 65.00 1.00 9.00 63.00
891 55.00 .00 8.00 44.00
892 63.00 .00 7.00 65.00
893 44.00 .00 7.00 39.00
894 47.00 .00 7.00 36.00
895 63.00 1.00 9.00 55.00
896 68.00 .00 8.00 66.00
897 34.00 .00 8.00 39.00
898 47.00 .00 9.00 50.00
899 50.00 .00 9.00 58.00
900 63.00 .00 8.00 66.00
901 44.00 .00 7.00 34.00
902 44.00 .00 8.00 50.00
903 50.00 .00 8.00 53.00
904 47.00 1.00 9.00 69.00
905 65.00 .00 9.00 58.00
906 57.00 .00 8.00 47.00
907 39.00 .00 8.00 39.00
908 47.00 .00 8.00 53.00
909 50.00 1.00 7.00 63.00
910 50.00 .00 8.00 50.00
911 63.00 .00 9.00 53.00
912 73.00 1.00 9.00 61.00
913 44.00 .00 7.00 47.00
914 47.00 .00 8.00 42.00
915 47.00 .00 8.00 58.00
916 36.00 .00 7.00 61.00
917 57.00 1.00 8.00 55.00
918 53.00 1.00 8.00 57.00
919 63.00 .00 7.00 66.00
920 50.00 .00 8.00 34.00
921 47.00 .00 9.00 48.00
922 57.00 1.00 8.00 58.00
923 39.00 .00 8.00 53.00
924 42.00 .00 8.00 42.00
925 42.00 .00 9.00 31.00
926 42.00 .00 8.00 72.00
927 46.00 .00 8.00 44.00
928 55.00 .00 8.00 42.00
929 42.00 .00 8.00 47.00
930 50.00 .00 8.00 44.00
931 44.00 .00 9.00 39.00
932 73.00 1.00 8.00 69.00
933 71.00 1.00 9.00 58.00
934 50.00 .00 9.00 49.00
935 63.00 1.00 7.00 54.00
936 42.00 .00 8.00 36.00
937 47.00 .00 7.00 42.00
938 39.00 .00 9.00 26.00
939 63.00 .00 8.00 58.00
940 50.00 .00 8.00 55.00
941 65.00 1.00 8.00 55.00
942 76.00 1.00 9.00 67.00
943 71.00 1.00 8.00 66.00
944 39.00 .00 9.00 47.00
945 47.00 1.00 9.00 63.00
946 60.00 .00 7.00 50.00
947 63.00 .00 9.00 55.00
948 54.00 1.00 9.00 55.00
949 55.00 1.00 8.00 58.00
950 57.00 .00 8.00 61.00
951 55.00 1.00 9.00 63.00
952 42.00 .00 7.00 50.00
953 50.00 .00 8.00 44.00
954 55.00 .00 8.00 42.00
955 42.00 .00 7.00 50.00
956 34.00 .00 8.00 39.00
957 65.00 .00 9.00 46.00
958 52.00 .00 7.00 58.00
959 44.00 .00 8.00 39.00
960 65.00 1.00 9.00 66.00
961 47.00 .00 8.00 42.00
962 41.00 .00 7.00 39.00
963 68.00 .00 9.00 63.00
964 63.00 1.00 8.00 72.00
965 52.00 .00 8.00 53.00
966 57.00 .00 8.00 50.00
967 68.00 .00 8.00 55.00
968 42.00 .00 8.00 56.00
969 47.00 .00 8.00 48.00
970 73.00 1.00 9.00 58.00
971 39.00 .00 8.00 50.00
972 63.00 1.00 9.00 69.00
973 60.00 .00 8.00 55.00
974 65.00 1.00 9.00 66.00
975 73.00 1.00 8.00 63.00
976 52.00 .00 8.00 55.00
977 36.00 .00 8.00 42.00
978 28.00 .00 7.00 44.00
979 47.00 .00 8.00 44.00
980 57.00 .00 7.00 47.00
981 34.00 .00 7.00 29.00
982 47.00 .00 9.00 66.00
983 57.00 .00 8.00 58.00
984 60.00 1.00 9.00 50.00
985 50.00 .00 9.00 47.00
986 73.00 1.00 9.00 55.00
987 52.00 1.00 8.00 47.00
988 55.00 .00 8.00 53.00
989 47.00 .00 8.00 53.00
990 50.00 .00 8.00 61.00
991 61.00 .00 7.00 44.00
992 52.00 .00 9.00 53.00
993 47.00 .00 7.00 40.00
994 47.00 .00 7.00 50.00
995 ])
997 AT_DATA([stringcat.sps], [dnl
998 set format=F20.3.
999 data list notable file='lr-cat2.data' list /read honcomp wiz science *.
1001 string ses(a1).
1002 recode wiz (7 = "a") (8 = "b") (9 = "c") into ses.
1004 logistic regression honcomp with read science ses
1005 /categorical = ses.
1007 ])
1009 AT_CHECK([pspp -O format=csv stringcat.sps], [0],
1010 [dnl
1011 Table: Dependent Variable Encoding
1012 Original Value,Internal Value
1013 .000,0
1014 1.000,1
1016 Table: Case Processing Summary
1017 Unweighted Cases,N,Percent
1018 Included in Analysis,200,100.000
1019 Missing Cases,0,.000
1020 Total,200,100.000
1022 note: Estimation terminated at iteration number 5 because parameter estimates changed by less than 0.001
1024 Table: Model Summary
1025 Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
1026 ,165.701,.280,.408
1028 Table: Categorical Variables' Codings
1029 ,,,Parameter coding,
1030 ,,Frequency,(1),(2)
1031 ses,a,47,1,0
1032 ,b,95,0,1
1033 ,c,58,0,0
1035 Table: Classification Table
1036 ,,,Predicted,,
1037 ,,,honcomp,,"Percentage
1038 Correct"
1039 ,Observed,,.000,1.000,
1040 Step 1,honcomp,.000,132,15,89.796
1041 ,,1.000,26,27,50.943
1042 ,Overall Percentage,,,,79.500
1044 Table: Variables in the Equation
1045 ,,B,S.E.,Wald,df,Sig.,Exp(B)
1046 Step 1,read,.098,.025,15.199,1,.000,1.103
1047 ,science,.066,.027,5.867,1,.015,1.068
1048 ,ses,,,6.690,2,.035,
1049 ,ses(1),.058,.532,.012,1,.913,1.060
1050 ,ses(2),-1.013,.444,5.212,1,.022,.363
1051 ,Constant,-9.561,1.662,33.113,1,.000,.000
1052 ])
1054 AT_CLEANUP
1057 dnl Check that it doesn't crash if a categorical variable
1058 dnl has only one distinct value
1059 AT_SETUP([LOGISTIC REGRESSION identical categories])
1061 AT_DATA([crash.sps], [dnl
1062 data list notable list /y x1 x2*.
1063 begin data
1064 0 1 1
1065 1 2 1
1066 end data.
1068 logistic regression y with x1 x2
1069 /categorical = x2.
1070 ])
1072 AT_CHECK([pspp -O format=csv crash.sps], [1], [ignore])
1074 AT_CLEANUP
1077 dnl Test that missing values on the categorical predictors are treated
1078 dnl properly.
1079 AT_SETUP([LOGISTIC REGRESSION missing categoricals])
1081 AT_DATA([data.txt], [dnl
1082 .00 3.69 .00
1083 .00 1.16 1.00
1084 1.00 -12.99 .00
1085 .00 2.97 1.00
1086 .00 20.48 .00
1087 .00 4.90 .00
1088 1.00 -4.38 .00
1089 .00 -1.69 1.00
1090 1.00 -5.71 .00
1091 1.00 -14.28 .00
1092 .00 9.00 .00
1093 .00 2.89 1.00
1094 .00 13.51 1.00
1095 .00 23.32 1.00
1096 .00 2.31 1.00
1097 .00 -2.07 1.00
1098 1.00 -4.52 1.00
1099 1.00 -5.83 .00
1100 1.00 -1.91 .00
1101 1.00 -11.12 1.00
1102 .00 -1.51 .00
1103 .00 6.59 1.00
1104 .00 19.28 1.00
1105 .00 5.94 .00
1106 .00 8.21 1.00
1107 .00 8.11 1.00
1108 .00 2.49 .00
1109 .00 9.62 .00
1110 1.00 -20.74 1.00
1111 .00 -1.41 1.00
1112 .00 15.15 1.00
1113 .00 9.39 .00
1114 1.00 -15.14 1.00
1115 1.00 -5.86 .00
1116 1.00 -11.64 1.00
1117 1.00 -14.36 .00
1118 1.00 -8.95 1.00
1119 1.00 -16.42 1.00
1120 1.00 -1.04 1.00
1121 .00 12.89 1.00
1122 .00 -7.08 1.00
1123 .00 4.87 1.00
1124 .00 11.53 1.00
1125 1.00 -6.24 1.00
1126 .00 1.25 1.00
1127 .00 4.39 1.00
1128 .00 3.17 .00
1129 .00 19.39 1.00
1130 .00 13.03 1.00
1131 .00 2.43 .00
1132 1.00 -14.73 1.00
1133 .00 8.25 1.00
1134 1.00 -13.28 1.00
1135 .00 5.27 1.00
1136 1.00 -3.46 1.00
1137 .00 13.81 1.00
1138 .00 1.35 1.00
1139 1.00 -3.94 1.00
1140 .00 20.73 1.00
1141 1.00 -15.40 .00
1142 1.00 -11.01 1.00
1143 .00 4.56 .00
1144 1.00 -15.35 1.00
1145 .00 15.21 .00
1146 .00 5.34 1.00
1147 1.00 -21.55 1.00
1148 .00 10.12 1.00
1149 .00 -.73 1.00
1150 .00 15.28 1.00
1151 .00 11.08 1.00
1152 1.00 -8.24 .00
1153 .00 2.46 .00
1154 .00 9.60 .00
1155 .00 11.24 .00
1156 .00 14.13 1.00
1157 .00 19.72 1.00
1158 .00 5.58 .00
1159 .00 26.23 1.00
1160 .00 7.25 .00
1161 1.00 -.79 .00
1162 .00 6.24 .00
1163 1.00 1.16 .00
1164 1.00 -7.89 1.00
1165 1.00 -1.86 1.00
1166 1.00 -10.80 1.00
1167 1.00 -5.51 .00
1168 .00 7.51 .00
1169 .00 11.18 .00
1170 .00 8.73 .00
1171 1.00 -11.21 1.00
1172 1.00 -13.24 .00
1173 .00 19.34 .00
1174 .00 9.32 1.00
1175 .00 17.97 1.00
1176 1.00 -1.56 1.00
1177 1.00 -3.13 .00
1178 .00 3.98 .00
1179 .00 -1.21 1.00
1180 .00 2.37 .00
1181 1.00 -18.03 1.00
1182 ])
1184 AT_DATA([miss.sps], [dnl
1185 data list notable file='data.txt' list /y x1 cat0*.
1187 logistic regression y with x1 cat0
1188 /categorical = cat0.
1189 ])
1191 AT_CHECK([pspp -O format=csv miss.sps > file1], [0], [ignore])
1193 dnl Append a case with a missing categorical.
1194 AT_CHECK([echo '1 34 .' >> data.txt], [0], [ignore])
1196 AT_CHECK([pspp -O format=csv miss.sps > file2], [0], [ignore])
1198 AT_CHECK([diff file1 file2], [1], [dnl
1199 8,10c8,10
1200 < Included in Analysis,100,100.00
1201 < Missing Cases,0,.00
1202 < Total,100,100.00
1203 ---
1204 > Included in Analysis,100,99.01
1205 > Missing Cases,1,.99
1206 > Total,101,100.00
1207 ])
1209 AT_CLEANUP
1212 dnl Check that the confidence intervals are properly reported.
1213 dnl Use an example with categoricals, because that was buggy at
1214 dnl one point. The data in this example comes from:
1215 dnl http://people.ysu.edu/~gchang/SPSSE/SPSS_lab2Regression.pdf
1216 AT_SETUP([LOGISTIC REGRESSION confidence interval])
1218 AT_DATA([ci.sps], [dnl
1219 set FORMAT=F20.3
1220 data list notable list /disease age sciostat sector savings *.
1221 begin data.
1222 0 33 1 1 1
1223 0 35 1 1 1
1224 0 6 1 1 0
1225 0 60 1 1 1
1226 1 18 3 1 0
1227 0 26 3 1 0
1228 0 6 3 1 0
1229 1 31 2 1 1
1230 1 26 2 1 0
1231 0 37 2 1 0
1232 0 23 1 1 0
1233 0 23 1 1 0
1234 0 27 1 1 1
1235 1 9 1 1 1
1236 1 37 1 2 1
1237 1 22 1 2 1
1238 1 67 1 2 1
1239 0 8 1 2 1
1240 1 6 1 2 1
1241 1 15 1 2 1
1242 1 21 2 2 1
1243 1 32 2 2 1
1244 1 16 1 2 1
1245 0 11 2 2 0
1246 0 14 3 2 0
1247 0 9 2 2 0
1248 0 18 2 2 0
1249 0 2 3 1 0
1250 0 61 3 1 1
1251 0 20 3 1 0
1252 0 16 3 1 0
1253 0 9 2 1 0
1254 0 35 2 1 1
1255 0 4 1 1 1
1256 0 44 3 2 0
1257 1 11 3 2 0
1258 0 3 2 2 1
1259 0 6 3 2 0
1260 1 17 2 2 0
1261 0 1 3 2 1
1262 1 53 2 2 1
1263 1 13 1 2 0
1264 0 24 1 2 0
1265 1 70 1 2 1
1266 1 16 3 2 1
1267 0 12 2 2 1
1268 1 20 3 2 1
1269 0 65 3 2 1
1270 1 40 2 2 0
1271 1 38 2 2 1
1272 1 68 2 2 1
1273 1 74 1 2 1
1274 1 14 1 2 1
1275 1 27 1 2 1
1276 0 31 1 2 1
1277 0 18 1 2 1
1278 0 39 1 2 0
1279 0 50 1 2 1
1280 0 31 1 2 1
1281 0 61 1 2 1
1282 0 18 3 1 0
1283 0 5 3 1 0
1284 0 2 3 1 1
1285 0 16 3 1 0
1286 1 59 3 1 1
1287 0 22 3 1 0
1288 0 24 1 1 1
1289 0 30 1 1 1
1290 0 46 1 1 1
1291 0 28 1 1 0
1292 0 27 1 1 1
1293 1 27 1 1 0
1294 0 28 1 1 1
1295 1 52 1 1 1
1296 0 11 3 1 1
1297 0 6 2 1 1
1298 0 46 3 1 0
1299 1 20 2 1 1
1300 0 3 1 1 1
1301 0 18 2 1 0
1302 0 25 2 1 0
1303 0 6 3 1 1
1304 1 65 3 1 1
1305 0 51 3 1 1
1306 0 39 2 1 1
1307 0 8 1 1 1
1308 0 8 2 1 0
1309 0 14 3 1 0
1310 0 6 3 1 0
1311 0 6 3 1 1
1312 0 7 3 1 0
1313 0 4 3 1 0
1314 0 8 3 1 0
1315 0 9 2 1 0
1316 1 32 3 1 0
1317 0 19 3 1 0
1318 0 11 3 1 0
1319 0 35 3 1 0
1320 0 16 1 1 0
1321 0 1 1 1 1
1322 0 6 1 1 1
1323 0 27 1 1 1
1324 0 25 1 1 1
1325 0 18 1 1 0
1326 0 37 3 1 0
1327 1 33 3 1 0
1328 0 27 2 1 0
1329 0 2 1 1 0
1330 0 8 2 1 0
1331 0 5 1 1 0
1332 0 1 1 1 1
1333 0 32 1 1 0
1334 1 25 1 1 1
1335 0 15 1 2 0
1336 0 15 1 2 1
1337 0 26 1 2 1
1338 1 42 1 2 1
1339 0 7 1 2 1
1340 0 2 1 2 0
1341 1 65 1 2 1
1342 0 33 2 2 1
1343 1 8 2 2 0
1344 0 30 2 2 0
1345 0 5 3 2 0
1346 0 15 3 2 0
1347 1 60 3 2 1
1348 1 13 3 2 1
1349 0 70 3 1 1
1350 0 5 3 1 0
1351 0 3 3 1 1
1352 0 50 2 1 1
1353 0 6 2 1 0
1354 0 12 2 1 1
1355 1 39 3 2 0
1356 0 15 2 2 1
1357 1 35 2 2 0
1358 0 2 2 2 1
1359 0 17 3 2 0
1360 1 43 3 2 1
1361 0 30 2 2 1
1362 0 11 1 2 1
1363 1 39 1 2 1
1364 0 32 1 2 1
1365 0 17 1 2 1
1366 0 3 3 2 1
1367 0 7 3 2 0
1368 0 2 2 2 0
1369 1 64 2 2 1
1370 1 13 1 2 2
1371 1 15 2 2 1
1372 0 48 2 2 1
1373 0 23 1 2 1
1374 1 48 1 2 0
1375 0 25 1 2 1
1376 0 12 1 2 1
1377 1 46 1 2 1
1378 0 79 1 2 1
1379 0 56 1 2 1
1380 0 8 1 2 1
1381 1 29 3 1 0
1382 1 35 3 1 0
1383 1 11 3 1 0
1384 0 69 3 1 1
1385 1 21 3 1 0
1386 0 13 3 1 0
1387 0 21 1 1 1
1388 1 32 1 1 1
1389 1 24 1 1 0
1390 0 24 1 1 1
1391 0 73 1 1 1
1392 0 42 1 1 1
1393 1 34 1 1 1
1394 0 30 2 1 0
1395 0 7 2 1 0
1396 1 29 3 1 0
1397 1 22 3 1 0
1398 0 38 2 1 1
1399 0 13 2 1 1
1400 0 12 2 1 1
1401 0 42 3 1 0
1402 1 17 3 1 0
1403 0 21 3 1 1
1404 0 34 1 1 1
1405 0 1 3 1 0
1406 0 14 2 1 0
1407 0 16 2 1 0
1408 0 9 3 1 0
1409 0 53 3 1 0
1410 0 27 3 1 0
1411 0 15 3 1 0
1412 0 9 3 1 0
1413 0 4 2 1 1
1414 0 10 3 1 1
1415 0 31 3 1 0
1416 0 85 3 1 1
1417 0 24 2 1 0
1418 end data.
1420 logistic regression
1421 disease WITH age sciostat sector savings
1422 /categorical = sciostat sector
1423 /print = ci(95).
1424 ])
1426 AT_CHECK([pspp -O format=csv ci.sps], [0], [dnl
1427 Table: Dependent Variable Encoding
1428 Original Value,Internal Value
1429 .000,0
1430 1.000,1
1432 Table: Case Processing Summary
1433 Unweighted Cases,N,Percent
1434 Included in Analysis,196,100.000
1435 Missing Cases,0,.000
1436 Total,196,100.000
1438 note: Estimation terminated at iteration number 4 because parameter estimates changed by less than 0.001
1440 Table: Model Summary
1441 Step 1,-2 Log likelihood,Cox & Snell R Square,Nagelkerke R Square
1442 ,211.195,.120,.172
1444 Table: Categorical Variables' Codings
1445 ,,,Parameter coding,
1446 ,,Frequency,(1),(2)
1447 sciostat,1.000,77,1,0
1448 ,2.000,49,0,1
1449 ,3.000,70,0,0
1450 sector,1.000,117,1,
1451 ,2.000,79,0,
1453 Table: Classification Table
1454 ,,,Predicted,,
1455 ,,,disease,,"Percentage
1456 Correct"
1457 ,Observed,,.000,1.000,
1458 Step 1,disease,.000,131,8,94.245
1459 ,,1.000,41,16,28.070
1460 ,Overall Percentage,,,,75.000
1462 Table: Variables in the Equation
1463 ,,,,,,,,95% CI for Exp(B),
1464 ,,B,S.E.,Wald,df,Sig.,Exp(B),Lower,Upper
1465 Step 1,age,.027,.009,8.647,1,.003,1.027,1.009,1.045
1466 ,savings,.061,.386,.025,1,.874,1.063,.499,2.264
1467 ,sciostat,,,.440,2,.803,,,
1468 ,sciostat(1),-.278,.434,.409,1,.522,.757,.323,1.775
1469 ,sciostat(2),-.219,.459,.227,1,.634,.803,.327,1.976
1470 ,sector,,,11.974,1,.001,,,
1471 ,sector(1),-1.235,.357,11.974,1,.001,.291,.145,.586
1472 ,Constant,-.814,.452,3.246,1,.072,.443,,
1473 ])
1475 AT_CLEANUP