Example 7 for PROC LOGISTIC
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/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: LOGIEX7 */
/* TITLE: Example 7 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 7. ROC Curve, Customized Odds Ratios, Goodness-of-Fit
Statistics, R-Square and Confidence Limits
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/*
This example plots an ROC curve, estimates a customized odds ratio, produces
the traditional goodness-of-fit analysis, prints the generalized R^2 measures
for the fitted model, and calculates the normal confidence intervals for the
regression parameters.
The data consist of three variables: N (number of subjects in a sample),
DISEASE (number of diseased subjects in the sample), and AGE (age for the
sample). A linear logistic regression model is fit to study the effect of
age on the probability of contracting the disease.
Finally, ODS Graphics and the PLOTS= option are used
to plot the ROC curve, and the EFFECTPLOT statement displays the
model-predicted probabilities.*/
title 'Example 7: ROC Curve, R-Square, ...';
data Data1;
input disease n age;
datalines;
0 14 25
0 20 35
0 19 45
7 18 55
6 12 65
17 17 75
;
ods graphics on;
proc logistic data=Data1 plots(only)=roc(id=obs);
model disease/n=age / scale=none
clparm=wald
clodds=pl
rsquare;
units age=10;
effectplot;
run;
ods graphics off;