## 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;
%let _ROC_XAXISOPTS_LABEL=False Positive Fraction;
%let _ROC_YAXISOPTS_LABEL=True Positive Fraction;
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;