SASŪ High-Performance Analytics Samples
The SAS High-Performance Analytics sample programs and install verification tests can be run only after you
edit and submit this file.
The file contains site-specific information about your environment so that the procedures can run successfully.
Example 1 for for PROC HPLOGISTIC
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: hploge01 */
/* TITLE: Example 1 for for PROC HPLOGISTIC */
/* Model Selection */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: Logistic regression analysis, */
/* Binary response data */
/* Variable selection */
/* PROCS: HPLOGISTIC */
/* DATA: */
/* */
/* SUPPORT: Bob Derr */
/* REF: SAS/HPA User's Guide, PROC HPLOGISTIC chapter */
/* MISC: */
/* */
/****************************************************************/
/*****************************************************************
Example 1: Model Selection
****************************************************************/
/*
The data, from the Getting Started example (hploggs1), consists of
100 observations on a dichotomous response variable y, a character
variable C, and 10 continuous variables x1--x10. A forward
selection technique is used to select the variables for use in a
main effects binary logistic regression model of these data.
*/
title 'Example 1: Modeling Binomial Data';
data getStarted;
input C$ y x1-x10;
datalines;
D 0 10.2 6 1.6 38 15 2.4 20 0.8 8.5 3.9
F 1 12.2 6 2.6 42 61 1.5 10 0.6 8.5 0.7
D 1 7.7 1 2.1 38 61 1 90 0.6 7.5 5.2
J 1 10.9 7 3.5 46 42 0.3 0 0.2 6 3.6
E 0 17.3 6 3.8 26 47 0.9 10 0.4 1.5 4.7
A 0 18.7 4 1.8 2 34 1.7 80 1 9.5 2.2
B 0 7.2 1 0.3 48 61 1.1 10 0.8 3.5 4
D 0 0.1 3 2.4 0 65 1.6 70 0.8 3.5 0.7
H 1 2.4 4 0.7 38 22 0.2 20 0 3 4.2
J 0 15.6 7 1.4 0 98 0.3 0 1 5 5.2
J 0 11.1 3 2.4 42 55 2.2 60 0.6 4.5 0.7
F 0 4 6 0.9 4 36 2.1 30 0.8 9 4.6
A 0 6.2 2 1.8 14 79 1.1 70 0.2 0 5.1
H 0 3.7 3 0.8 12 66 1.3 40 0.4 0.5 3.3
A 1 9.2 3 2.3 48 51 2.3 50 0 6 5.4
G 0 14 3 2 18 12 2.2 0 0 3 3.4
E 1 19.5 6 3.7 26 81 0.1 30 0.6 5 4.8
C 0 11 3 2.8 38 9 1.7 50 0.8 6.5 0.9
I 0 15.3 7 2.2 20 98 2.7 100 0.4 7 0.8
H 1 7.4 4 0.5 28 65 1.3 60 0.2 9.5 5.4
F 0 11.4 2 1.4 42 12 2.4 10 0.4 1 4.5
C 1 19.4 1 0.4 42 4 2.4 10 0 6.5 0.1
G 0 5.9 4 2.6 12 57 0.8 50 0.4 2 5.8
G 1 15.8 6 3.7 34 8 1.3 90 0.6 2.5 5.7
I 0 10 3 1.9 16 80 3 90 0.4 9.5 1.9
E 0 15.7 1 2.7 32 25 1.7 20 0.2 8.5 6
G 0 11 5 2.9 48 53 0.1 50 1 3.5 1.2
J 1 16.8 0 0.9 14 86 1.4 40 0.8 9 5
D 1 11 4 3.2 48 63 2.8 90 0.6 0 2.2
J 1 4.8 7 3.6 24 1 2.2 20 1 8.5 0.5
J 1 10.4 5 2 42 56 1 20 0 3.5 4.2
G 0 12.7 7 3.6 8 56 2.1 70 1 4.5 1.5
G 0 6.8 1 3.2 30 27 0.6 0 0.8 2 5.6
E 0 8.8 0 3.2 2 67 0.7 10 0.4 1 5
I 1 0.2 0 2.9 10 41 2.3 60 0.2 9 0.3
J 1 4.6 7 3.9 50 61 2.1 50 0.4 3 4.9
J 1 2.3 2 3.2 36 98 0.1 40 0.6 4.5 4.3
I 0 10.8 3 2.7 28 58 0.8 80 0.8 3 6
B 0 9.3 2 3.3 44 44 0.3 50 0.8 5.5 0.4
F 0 9.2 6 0.6 4 64 0.1 0 0.6 4.5 3.9
D 0 7.4 0 2.9 14 0 0.2 30 0.8 7.5 4.5
G 0 18.3 3 3.1 8 60 0.3 60 0.2 7 1.9
F 0 5.3 4 0.2 48 63 2.3 80 0.2 8 5.2
C 0 2.6 5 2.2 24 4 1.3 20 0 2 1.4
F 0 13.8 4 3.6 4 7 1.1 10 0.4 3.5 1.9
B 1 12.4 6 1.7 30 44 1.1 60 0.2 6 1.5
I 0 1.3 1 1.3 8 53 1.1 70 0.6 7 0.8
F 0 18.2 7 1.7 26 92 2.2 30 1 8.5 4.8
J 0 5.2 2 2.2 18 12 1.4 90 0.8 4 4.9
G 1 9.4 2 0.8 22 86 0.4 30 0.4 1 5.9
J 1 10.4 2 1.7 26 31 2.4 10 0.2 7 1.6
J 0 13 1 1.8 14 11 2.3 50 0.6 5.5 2.6
A 0 17.9 4 3.1 46 58 2.6 90 0.6 1.5 3.2
D 1 19.4 6 3 20 50 2.8 100 0.2 9 1.2
I 0 19.6 3 3.6 22 19 1.2 0 0.6 5 4.1
I 1 6 2 1.5 30 30 2.2 20 0.4 8.5 5.3
G 0 13.8 1 2.7 0 52 2.4 20 0.8 6 2
B 0 14.3 4 2.9 30 11 0.6 90 0.6 0.5 4.9
E 0 15.6 0 0.4 38 79 0.4 80 0.4 1 3.3
D 0 14 2 1 22 61 3 90 0.6 2 0.1
C 1 9.4 5 0.4 12 53 1.7 40 0 3 1.1
H 0 13.2 1 1.6 40 15 0.7 40 0.2 9 5.5
A 0 13.5 5 2.4 18 89 1.6 20 0.4 9.5 4.7
E 0 2.6 4 2.3 38 6 0.8 20 0.4 5 5.3
E 0 12.4 3 1.3 26 8 2.8 10 0.8 6 5.8
D 0 7.6 2 0.9 44 89 1.3 50 0.8 6 0.4
I 0 12.7 1 2.3 42 6 2.4 10 0.4 1 3
C 1 10.7 4 3.2 28 23 2.2 90 0.8 5.5 2.8
H 0 10.1 2 2.3 10 62 0.9 50 0.4 2.5 3.7
C 1 16.6 1 0.5 12 88 0.1 20 0.6 5.5 1.8
I 1 0.2 3 2.2 8 71 1.7 80 0.4 0.5 5.5
C 0 10.8 4 3.5 30 70 2.3 60 0.4 4.5 5.9
F 0 7.1 4 3 14 63 2.4 70 0 7 3.1
D 0 16.5 1 3.3 30 80 1.6 40 0 3.5 2.7
H 0 17.1 7 2.1 30 45 1.5 60 0.6 0.5 2.8
D 0 4.3 1 1.5 24 44 0 70 0 5 0.5
H 0 15 2 0.2 14 87 1.8 50 0 4.5 4.7
G 0 19.7 3 1.9 36 99 1.5 10 0.6 3 1.7
H 1 2.8 6 0.6 34 21 2 60 1 9 4.7
G 0 16.6 3 3.3 46 1 1.4 70 0.6 1.5 5.3
E 0 11.7 5 2.7 48 4 0.9 60 0.8 4.5 1.6
F 0 15.6 3 0.2 4 79 0.5 0 0.8 1.5 2.9
C 1 5.3 6 1.4 8 64 2 80 0.4 9 4.2
B 1 8.1 7 1.7 40 36 1.4 60 0.6 6 3.9
I 0 14.8 2 3.2 8 37 0.4 10 0 4.5 3
D 0 7.4 4 3 12 3 0.6 60 0.6 7 0.7
D 0 4.8 3 2.3 44 41 1.9 60 0.2 3 3.1
A 0 4.5 0 0.2 4 48 1.7 80 0.8 9 4.2
D 0 6.9 6 3.3 14 92 0.5 40 0.4 7.5 5
B 0 4.7 4 0.9 14 99 2.4 80 1 0.5 0.7
I 1 7.5 4 2.1 20 79 0.4 40 0.4 2.5 0.7
C 0 6.1 0 1.4 38 18 2.3 60 0.8 4.5 0.7
C 0 18.3 1 1 26 98 2.7 20 1 8.5 0.5
F 0 16.4 7 1.2 32 94 2.9 40 0.4 5.5 2.1
I 0 9.4 2 2.3 32 42 0.2 70 0.4 8.5 0.3
F 1 17.9 4 1.3 32 42 2 40 0.2 1 5.4
H 0 14.9 3 1.6 36 74 2.6 60 0.2 1 2.3
C 0 12.7 0 2.6 0 88 1.1 80 0.8 0.5 2.1
F 0 5.4 4 1.5 2 1 1.8 70 0.4 5.5 3.6
J 1 12.1 4 1.8 20 59 1.3 60 0.4 3 3.8
;
proc hplogistic data=getStarted;
class C;
model y = C x1-x10;
selection method=forward details=all;
run;