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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;