Example 1 for PROC LOGISTIC
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/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: LOGIEX1 */
/* TITLE: Example 1 for PROC LOGISTIC */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: logistic regression analysis, */
/* binomial response data, */
/* PROCS: LOGISTIC */
/* DATA: */
/* */
/* SUPPORT: Bob Derr */
/* REF: SAS/STAT User's Guide, PROC LOGISTIC chapter */
/* MISC: */
/* */
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Example 1. Stepwise Logistic Regression and Predicted Values
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/*
The data, taken from Lee (1974), consist of patient characteristics and
whether or not cancer remission occurred, and are saved in the data set
Remission. The variable remiss is the cancer remission indicator variable
with a value of 1 for remission and a value of 0 for nonremission. The other
six variables are the risk factors thought to be related to cancer remission.
The first call to the LOGISTIC procedure illustrates the use of stepwise
selection to identify the prognostic factors for cancer remission. Two
output data sets are printed: one contains the parameter estimates and the
estimated covariance matrix; the other contains the predicted values and
confidence limits for the probabilities of cancer remission.
The second call to the LOGISTIC procedure illustrates the FAST option for
backward elimination.
*/
title 'Example 1. Stepwise Regression';
data Remission;
input remiss cell smear infil li blast temp;
label remiss='Complete Remission';
datalines;
1 .8 .83 .66 1.9 1.1 .996
1 .9 .36 .32 1.4 .74 .992
0 .8 .88 .7 .8 .176 .982
0 1 .87 .87 .7 1.053 .986
1 .9 .75 .68 1.3 .519 .98
0 1 .65 .65 .6 .519 .982
1 .95 .97 .92 1 1.23 .992
0 .95 .87 .83 1.9 1.354 1.02
0 1 .45 .45 .8 .322 .999
0 .95 .36 .34 .5 0 1.038
0 .85 .39 .33 .7 .279 .988
0 .7 .76 .53 1.2 .146 .982
0 .8 .46 .37 .4 .38 1.006
0 .2 .39 .08 .8 .114 .99
0 1 .9 .9 1.1 1.037 .99
1 1 .84 .84 1.9 2.064 1.02
0 .65 .42 .27 .5 .114 1.014
0 1 .75 .75 1 1.322 1.004
0 .5 .44 .22 .6 .114 .99
1 1 .63 .63 1.1 1.072 .986
0 1 .33 .33 .4 .176 1.01
0 .9 .93 .84 .6 1.591 1.02
1 1 .58 .58 1 .531 1.002
0 .95 .32 .3 1.6 .886 .988
1 1 .6 .6 1.7 .964 .99
1 1 .69 .69 .9 .398 .986
0 1 .73 .73 .7 .398 .986
;
title 'Stepwise Regression on Cancer Remission Data';
proc logistic data=Remission outest=betas covout;
model remiss(event='1')=cell smear infil li blast temp
/ selection=stepwise
slentry=0.3
slstay=0.35
details
lackfit;
output out=pred p=phat lower=lcl upper=ucl
predprob=(individual crossvalidate);
run;
proc print data=betas;
title2 'Parameter Estimates and Covariance Matrix';
run;
proc print data=pred;
title2 'Predicted Probabilities and 95% Confidence Limits';
run;
title 'Backward Elimination on Cancer Remission Data';
proc logistic data=Remission;
model remiss(event='1')=temp cell li smear blast
/ selection=backward fast slstay=0.2 ctable;
run;