Example 4 for PROC HPLOGISTIC
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: hplogex4 */
/* TITLE: Example 4 for PROC HPLOGISTIC */
/* DESC: Pima Indians Diabetes data set */
/* REF: Lim, Loh and Shih (2000) */
/* PRODUCT: HPSTAT */
/* SYSTEM: ALL */
/* KEYS: logistic regression analysis, */
/* binary response data */
/* PROCS: HPLOGISTIC */
/* DATA: McCullagh and Nelder (1989, p.175) */
/* */
/* SUPPORT: Bob Derr */
/* MISC: */
/* */
/****************************************************************/
/*****************************************************************
Example 4: Partitioning Data
*****************************************************************/
/*
This example uses the Pima Indian Diabetes data set, which can be
obtained from the UCI Machine Learning Repository (Asuncion and
Newman, 2007). It is extracted from a larger database that was
originally owned by the National Institute of Diabetes and
Digestive and Kidney Diseases. Data are for female patients who are
at least 21 years old, are of Pima Indian heritage, and live near
Phoenix, Arizona. The objective of this study is to investigate
the relationship between a diabetes diagnosis and variables that
represent physiological measurements and medical attributes. Some
missing or invalid observations are removed from the analysis. The
reduced data set contains 532 records. The DATA step creates the
data set Pima containing the following variables:
NPreg Number of pregnancies
Glucose Two-hour plasma glucose concentration in an oral glucose
tolerance test
Pressure Diastolic blood pressure (mm Hg)
Triceps Triceps skin fold thickness (mm)
BMI Body mass index (weight in kg/(height in m)**2)
Pedigree Diabetes pedigree function
Age Age (years)
Diabetes 0 if test negative for diabetes, 1 if test positive
Role 0 for training role, 1 for test
*/
title 'Example 4: Partitioning Data';
title 'Pima Indian Diabetes Study';
data Pima;
input NPreg Glucose Pressure Triceps BMI Pedigree Age Diabetes Role@@;
datalines;
6 148 72 35 33.6 0.627 50 1 0 1 85 66 29 26.6 0.351 31 0 1
1 89 66 23 28.1 0.167 21 0 0 3 78 50 32 31 0.248 26 1 0
2 197 70 45 30.5 0.158 53 1 0 5 166 72 19 25.8 0.587 51 1 1
0 118 84 47 45.8 0.551 31 1 0 1 103 30 38 43.3 0.183 33 0 0
3 126 88 41 39.3 0.704 27 0 0 9 119 80 35 29 0.263 29 1 0
1 97 66 15 23.2 0.487 22 0 1 5 109 75 26 36 0.546 60 0 0
3 88 58 11 24.8 0.267 22 0 0 10 122 78 31 27.6 0.512 45 0 0
4 103 60 33 24 0.966 33 0 1 9 102 76 37 32.9 0.665 46 1 1
2 90 68 42 38.2 0.503 27 1 1 4 111 72 47 37.1 1.39 56 1 0
3 180 64 25 34 0.271 26 0 1 7 106 92 18 22.7 0.235 48 0 0
9 171 110 24 45.4 0.721 54 1 0 0 180 66 39 42 1.893 25 1 0
2 71 70 27 28 0.586 22 0 0 1 103 80 11 19.4 0.491 22 0 1
1 101 50 15 24.2 0.526 26 0 1 5 88 66 21 24.4 0.342 30 0 0
7 150 66 42 34.7 0.718 42 0 1 1 73 50 10 23 0.248 21 0 0
0 105 64 41 41.5 0.173 22 0 0 5 99 74 27 29 0.203 32 0 0
0 109 88 30 32.5 0.855 38 1 0 1 95 66 13 19.6 0.334 25 0 0
4 146 85 27 28.9 0.189 27 0 0 2 100 66 20 32.9 0.867 28 1 0
4 129 86 20 35.1 0.231 23 0 0 5 95 72 33 37.7 0.37 27 0 1
2 112 66 22 25 0.307 24 0 0 3 113 44 13 22.4 0.14 22 0 1
7 83 78 26 29.3 0.767 36 0 0 0 101 65 28 24.6 0.237 22 0 0
13 106 72 54 36.6 0.178 45 0 1 2 100 68 25 38.5 0.324 26 0 0
15 136 70 32 37.1 0.153 43 1 1 4 123 80 15 32 0.443 34 0 0
7 81 78 40 46.7 0.261 42 0 0 2 92 62 28 31.6 0.13 24 0 0
6 93 50 30 28.7 0.356 23 0 0 1 122 90 51 49.7 0.325 31 1 1
1 81 72 18 26.6 0.283 24 0 0 1 126 56 29 28.7 0.801 21 0 0
4 144 58 28 29.5 0.287 37 0 0 1 89 76 34 31.2 0.192 23 0 1
7 160 54 32 30.5 0.588 39 1 0 4 97 60 23 28.2 0.443 22 0 1
0 162 76 56 53.2 0.759 25 1 1 2 107 74 30 33.6 0.404 23 0 0
1 88 30 42 55 0.496 26 1 0 1 117 88 24 34.5 0.403 40 1 0
4 173 70 14 29.7 0.361 33 1 1 3 170 64 37 34.5 0.356 30 1 0
8 84 74 31 38.3 0.457 39 0 0 0 100 70 26 30.8 0.597 21 0 0
0 93 60 25 28.7 0.532 22 0 0 5 106 82 30 39.5 0.286 38 0 0
2 108 52 26 32.5 0.318 22 0 0 2 106 64 35 30.5 1.4 34 0 1
2 90 70 17 27.3 0.085 22 0 1 9 156 86 28 34.3 1.189 42 1 0
1 153 82 42 40.6 0.687 23 0 0 7 152 88 44 50 0.337 36 1 0
2 88 74 19 29 0.229 22 0 0 17 163 72 41 40.9 0.817 47 1 1
4 151 90 38 29.7 0.294 36 0 0 7 102 74 40 37.2 0.204 45 0 0
0 114 80 34 44.2 0.167 27 0 0 6 104 74 18 29.9 0.722 41 1 0
2 75 64 24 29.7 0.37 33 0 0 8 179 72 42 32.7 0.719 36 1 1
0 129 110 46 67.1 0.319 26 1 0 1 128 98 41 32 1.321 33 1 0
8 109 76 39 27.9 0.64 31 1 1 4 109 64 44 34.8 0.905 26 1 0
0 113 80 16 31 0.874 21 0 0 0 108 68 20 27.3 0.787 32 0 1
5 111 72 28 23.9 0.407 27 0 0 8 196 76 29 37.5 0.605 57 1 0
2 81 60 22 27.7 0.29 25 0 0 0 147 85 54 42.8 0.375 24 0 0
5 109 62 41 35.8 0.514 25 1 0 6 125 68 30 30 0.464 32 0 0
5 85 74 22 29 1.224 32 1 1 7 142 60 33 28.8 0.687 61 0 0
1 100 66 15 23.6 0.666 26 0 1 1 87 78 27 34.6 0.101 22 0 0
3 162 52 38 37.2 0.652 24 1 0 4 197 70 39 36.7 2.329 31 0 1
0 117 80 31 45.2 0.089 24 0 0 6 134 80 37 46.2 0.238 46 1 0
3 74 68 28 29.7 0.293 23 0 1 7 181 84 21 35.9 0.586 51 1 0
0 179 90 27 44.1 0.686 23 1 1 1 91 64 24 29.2 0.192 21 0 0
4 91 70 32 33.1 0.446 22 0 0 6 119 50 22 27.1 1.318 33 1 0
2 146 76 35 38.2 0.329 29 0 1 9 184 85 15 30 1.213 49 1 0
0 165 90 33 52.3 0.427 23 0 0 9 124 70 33 35.4 0.282 34 0 0
1 111 86 19 30.1 0.143 23 0 0 2 90 80 14 24.4 0.249 24 0 1
1 113 64 35 33.6 0.543 21 1 0 3 111 56 39 30.1 0.557 30 0 0
11 155 76 28 33.3 1.353 51 1 0 4 95 70 32 32.1 0.612 24 0 1
5 96 74 18 33.6 0.997 43 0 0 2 128 64 42 40 1.101 24 0 0
10 101 86 37 45.6 1.136 38 1 0 2 108 62 32 25.2 0.128 21 0 0
2 100 70 52 40.5 0.677 25 0 1 7 106 60 24 26.5 0.296 29 1 0
0 104 64 23 27.8 0.454 23 0 1 2 108 62 10 25.3 0.881 22 0 1
7 133 88 15 32.4 0.262 37 0 0 7 136 74 26 26 0.647 51 0 0
1 119 86 39 45.6 0.808 29 1 0 4 96 56 17 20.8 0.34 26 0 0
0 78 88 29 36.9 0.434 21 0 0 0 107 62 30 36.6 0.757 25 1 0
6 151 62 31 35.5 0.692 28 0 0 2 146 70 38 28 0.337 29 1 0
0 126 84 29 30.7 0.52 24 0 0 2 144 58 33 31.6 0.422 25 1 0
2 120 76 37 39.7 0.215 29 0 0 10 161 68 23 25.5 0.326 47 1 0
0 128 68 19 30.5 1.391 25 1 0 2 124 68 28 32.9 0.875 30 1 1
2 155 74 17 26.6 0.433 27 1 1 3 113 50 10 29.5 0.626 25 0 0
7 109 80 31 35.9 1.127 43 1 1 3 115 66 39 38.1 0.15 28 0 0
13 152 90 33 26.8 0.731 43 1 0 2 112 75 32 35.7 0.148 21 0 0
1 157 72 21 25.6 0.123 24 0 1 1 122 64 32 35.1 0.692 30 1 0
2 102 86 36 45.5 0.127 23 1 1 6 105 70 32 30.8 0.122 37 0 1
8 118 72 19 23.1 1.476 46 0 0 2 87 58 16 32.7 0.166 25 0 0
1 95 60 18 23.9 0.26 22 0 1 1 130 70 13 25.9 0.472 22 0 1
1 95 74 21 25.9 0.673 36 0 0 8 126 88 36 38.5 0.349 49 0 0
1 139 46 19 28.7 0.654 22 0 0 3 99 62 19 21.8 0.279 26 0 1
1 125 50 40 33.3 0.962 28 1 0 1 196 76 36 36.5 0.875 29 1 0
5 189 64 33 31.2 0.583 29 1 0 5 103 108 37 39.2 0.305 65 0 1
4 147 74 25 34.9 0.385 30 0 1 5 99 54 28 34 0.499 30 0 0
3 81 86 16 27.5 0.306 22 0 1 3 173 82 48 38.4 2.137 25 1 0
0 84 64 22 35.8 0.545 21 0 1 0 98 82 15 25.2 0.299 22 0 1
1 87 60 37 37.2 0.509 22 0 1 0 93 100 39 43.4 1.021 35 0 0
0 105 68 22 20 0.236 22 0 1 1 90 62 18 25.1 1.268 25 0 0
1 125 70 24 24.3 0.221 25 0 0 1 119 54 13 22.3 0.205 24 0 1
5 116 74 29 32.3 0.66 35 1 0 8 105 100 36 43.3 0.239 45 1 0
3 100 68 23 31.6 0.949 28 0 1 1 131 64 14 23.7 0.389 21 0 0
2 127 58 24 27.7 1.6 25 0 0 3 96 56 34 24.7 0.944 39 0 1
3 193 70 31 34.9 0.241 25 1 0 5 136 84 41 35 0.286 35 1 1
9 72 78 25 31.6 0.28 38 0 0 1 172 68 49 42.4 0.702 28 1 1
6 102 90 39 35.7 0.674 28 0 0 1 112 72 30 34.4 0.528 25 0 0
1 143 84 23 42.4 1.076 22 0 0 3 173 84 33 35.7 0.258 22 1 0
4 144 82 32 38.5 0.554 37 1 0 3 129 64 29 26.4 0.219 28 1 0
1 119 88 41 45.3 0.507 26 0 0 2 94 68 18 26 0.561 21 0 1
0 102 64 46 40.6 0.496 21 0 0 8 151 78 32 42.9 0.516 36 1 0
1 181 64 30 34.1 0.328 38 1 0 1 95 82 25 35 0.233 43 1 0
3 89 74 16 30.4 0.551 38 0 0 1 80 74 11 30 0.527 22 0 0
1 90 68 8 24.5 1.138 36 0 0 0 189 104 25 34.3 0.435 41 1 1
4 117 64 27 33.2 0.23 24 0 0 0 180 78 63 59.4 2.42 25 1 1
0 104 64 37 33.6 0.51 22 1 0 0 120 74 18 30.5 0.285 26 0 1
1 82 64 13 21.2 0.415 23 0 0 0 91 68 32 39.9 0.381 25 0 1
9 134 74 33 25.9 0.46 81 0 1 9 120 72 22 20.8 0.733 48 0 0
8 74 70 40 35.3 0.705 39 0 0 5 88 78 30 27.6 0.258 37 0 0
0 124 56 13 21.8 0.452 21 0 1 0 97 64 36 36.8 0.6 25 0 1
1 144 82 40 41.3 0.607 28 0 1 0 137 70 38 33.2 0.17 22 0 0
4 132 86 31 28 0.419 63 0 1 3 158 70 30 35.5 0.344 35 1 0
0 123 88 37 35.2 0.197 29 0 0 0 84 82 31 38.2 0.233 23 0 0
0 135 68 42 42.3 0.365 24 1 1 1 139 62 41 40.7 0.536 21 0 0
0 173 78 32 46.5 1.159 58 0 0 2 83 65 28 36.8 0.629 24 0 1
2 89 90 30 33.5 0.292 42 0 1 4 99 68 38 32.8 0.145 33 0 0
4 125 70 18 28.9 1.144 45 1 0 2 81 72 15 30.1 0.547 25 0 1
6 154 74 32 29.3 0.839 39 0 0 2 117 90 19 25.2 0.313 21 0 1
3 84 72 32 37.2 0.267 28 0 1 7 94 64 25 33.3 0.738 41 0 0
3 96 78 39 37.3 0.238 40 0 0 12 84 72 31 29.7 0.297 46 1 0
3 99 54 19 25.6 0.154 24 0 0 3 163 70 18 31.6 0.268 28 1 0
9 145 88 34 30.3 0.771 53 1 1 6 129 90 7 19.6 0.582 60 0 1
2 68 70 32 25 0.187 25 0 1 3 87 60 18 21.8 0.444 21 0 0
2 122 60 18 29.8 0.717 22 0 1 1 77 56 30 33.3 1.251 24 0 1
0 127 80 37 36.3 0.804 23 0 0 3 128 72 25 32.4 0.549 27 1 0
10 90 85 32 34.9 0.825 56 1 1 4 84 90 23 39.5 0.159 25 0 0
1 88 78 29 32 0.365 29 0 0 8 186 90 35 34.5 0.423 37 1 1
5 187 76 27 43.6 1.034 53 1 0 4 131 68 21 33.1 0.16 28 0 1
1 116 70 28 27.4 0.204 21 0 1 3 84 68 30 31.9 0.591 25 0 0
1 88 62 24 29.9 0.422 23 0 0 1 84 64 23 36.9 0.471 28 0 1
11 103 68 40 46.2 0.126 42 0 0 6 99 60 19 26.9 0.497 32 0 0
1 99 72 30 38.6 0.412 21 0 1 3 111 58 31 29.5 0.43 22 0 1
2 98 60 17 34.7 0.198 22 0 0 1 143 86 30 30.1 0.892 23 0 1
1 119 44 47 35.5 0.28 25 0 1 6 108 44 20 24 0.813 35 0 0
3 176 86 27 33.3 1.154 52 1 0 11 111 84 40 46.8 0.925 45 1 0
2 112 78 50 39.4 0.175 24 0 0 2 82 52 22 28.5 1.699 25 0 1
6 123 72 45 33.6 0.733 34 0 0 1 89 24 19 27.8 0.559 21 0 1
1 108 88 19 27.1 0.4 24 0 0 1 124 60 32 35.8 0.514 21 0 0
1 181 78 42 40 1.258 22 1 1 1 92 62 25 19.5 0.482 25 0 1
0 152 82 39 41.5 0.27 27 0 1 3 174 58 22 32.9 0.593 36 1 1
6 105 80 28 32.5 0.878 26 0 0 11 138 74 26 36.1 0.557 50 1 1
2 68 62 13 20.1 0.257 23 0 0 9 112 82 24 28.2 1.282 50 1 1
0 94 70 27 43.5 0.347 21 0 1 4 90 88 47 37.7 0.362 29 0 1
4 94 65 22 24.7 0.148 21 0 1 0 102 78 40 34.5 0.238 24 0 1
1 128 82 17 27.5 0.115 22 0 1 7 97 76 32 40.9 0.871 32 1 0
1 100 74 12 19.5 0.149 28 0 0 3 103 72 30 27.6 0.73 27 0 0
0 179 50 36 37.8 0.455 22 1 0 11 136 84 35 28.3 0.26 42 1 0
1 117 60 23 33.8 0.466 27 0 1 2 155 52 27 38.7 0.24 25 1 0
2 101 58 35 21.8 0.155 22 0 0 1 112 80 45 34.8 0.217 24 0 1
4 145 82 18 32.5 0.235 70 1 1 10 111 70 27 27.5 0.141 40 1 0
6 98 58 33 34 0.43 43 0 1 6 165 68 26 33.6 0.631 49 0 1
10 68 106 23 35.5 0.285 47 0 0 3 123 100 35 57.3 0.88 22 0 0
0 162 76 36 49.6 0.364 26 1 0 0 95 64 39 44.6 0.366 22 0 0
2 129 74 26 33.2 0.591 25 0 0 1 107 50 19 28.3 0.181 29 0 0
7 142 90 24 30.4 0.128 43 1 1 3 169 74 19 29.9 0.268 31 1 0
6 80 80 36 39.8 0.177 28 0 0 2 127 46 21 34.4 0.176 22 0 0
2 93 64 32 38 0.674 23 1 1 5 126 78 27 29.6 0.439 40 0 0
10 129 62 36 41.2 0.441 38 1 0 0 134 58 20 26.4 0.352 21 0 1
7 187 50 33 33.9 0.826 34 1 0 3 173 78 39 33.8 0.97 31 1 0
10 94 72 18 23.1 0.595 56 0 1 1 108 60 46 35.5 0.415 24 0 0
5 117 86 30 39.1 0.251 42 0 1 1 116 78 29 36.1 0.496 25 0 0
0 141 84 26 32.4 0.433 22 0 0 2 174 88 37 44.5 0.646 24 1 1
2 106 56 27 29 0.426 22 0 1 0 126 86 27 27.4 0.515 21 0 1
8 65 72 23 32 0.6 42 0 0 2 99 60 17 36.6 0.453 21 0 1
11 120 80 37 42.3 0.785 48 1 0 3 102 44 20 30.8 0.4 26 0 0
1 109 58 18 28.5 0.219 22 0 0 13 153 88 37 40.6 1.174 39 0 0
12 100 84 33 30 0.488 46 0 1 1 147 94 41 49.3 0.358 27 1 1
3 187 70 22 36.4 0.408 36 1 1 1 121 78 39 39 0.261 28 0 0
3 108 62 24 26 0.223 25 0 1 0 181 88 44 43.3 0.222 26 1 0
1 128 88 39 36.5 1.057 37 1 1 2 88 58 26 28.4 0.766 22 0 0
9 170 74 31 44 0.403 43 1 0 10 101 76 48 32.9 0.171 63 0 1
5 121 72 23 26.2 0.245 30 0 0 1 93 70 31 30.4 0.315 23 0 1
5 86 68 28 30.2 0.364 24 0 0 7 195 70 33 25.1 0.163 55 1 1
5 77 82 41 35.8 0.156 35 0 0 0 165 76 43 47.9 0.259 26 0 0
0 107 60 25 26.4 0.133 23 0 1 5 97 76 27 35.6 0.378 52 1 0
3 83 58 31 34.3 0.336 25 0 0 1 193 50 16 25.9 0.655 24 0 0
3 142 80 15 32.4 0.2 63 0 0 2 128 78 37 43.3 1.224 31 1 1
0 137 40 35 43.1 2.288 33 1 0 9 154 78 30 30.9 0.164 45 0 1
1 189 60 23 30.1 0.398 59 1 0 12 92 62 7 27.6 0.926 44 1 0
1 86 66 52 41.3 0.917 29 0 0 4 99 76 15 23.2 0.223 21 0 0
1 109 60 8 25.4 0.947 21 0 0 11 143 94 33 36.6 0.254 51 1 0
1 149 68 29 29.3 0.349 42 1 0 0 139 62 17 22.1 0.207 21 0 0
2 99 70 16 20.4 0.235 27 0 1 1 100 66 29 32 0.444 42 0 0
4 83 86 19 29.3 0.317 34 0 1 0 101 64 17 21 0.252 21 0 1
1 87 68 34 37.6 0.401 24 0 0 9 164 84 21 30.8 0.831 32 1 0
1 99 58 10 25.4 0.551 21 0 1 0 140 65 26 42.6 0.431 24 1 0
5 108 72 43 36.1 0.263 33 0 1 2 110 74 29 32.4 0.698 27 0 1
1 79 60 42 43.5 0.678 23 0 1 3 148 66 25 32.5 0.256 22 0 0
0 121 66 30 34.3 0.203 33 1 1 3 158 64 13 31.2 0.295 24 0 0
2 105 80 45 33.7 0.711 29 1 0 13 145 82 19 22.2 0.245 57 0 0
1 79 80 25 25.4 0.583 22 0 0 1 71 48 18 20.4 0.323 22 0 1
0 102 86 17 29.3 0.695 27 0 1 0 119 66 27 38.8 0.259 22 0 0
8 176 90 34 33.7 0.467 58 1 1 1 97 68 21 27.2 1.095 22 0 1
4 129 60 12 27.5 0.527 31 0 1 1 97 64 19 18.2 0.299 21 0 0
0 86 68 32 35.8 0.238 25 0 0 2 125 60 20 33.8 0.088 31 0 0
5 123 74 40 34.1 0.269 28 0 0 2 92 76 20 24.2 1.698 28 0 0
3 171 72 33 33.3 0.199 24 1 1 1 199 76 43 42.9 1.394 22 1 0
3 116 74 15 26.3 0.107 24 0 0 2 83 66 23 32.2 0.497 22 0 0
8 154 78 32 32.4 0.443 45 1 1 1 114 66 36 38.1 0.289 21 0 1
1 106 70 28 34.2 0.142 22 0 1 4 127 88 11 34.5 0.598 28 0 0
1 124 74 36 27.8 0.1 30 0 1 1 109 38 18 23.1 0.407 26 0 0
2 123 48 32 42.1 0.52 26 0 1 8 167 106 46 37.6 0.165 43 1 1
7 184 84 33 35.5 0.355 41 1 0 1 96 64 27 33.2 0.289 21 0 0
10 129 76 28 35.9 0.28 39 0 1 6 92 62 32 32 0.085 46 0 0
6 109 60 27 25 0.206 27 0 1 5 139 80 35 31.6 0.361 25 1 0
6 134 70 23 35.4 0.542 29 1 0 3 106 54 21 30.9 0.292 24 0 1
0 131 66 40 34.3 0.196 22 1 1 0 135 94 46 40.6 0.284 26 0 1
5 158 84 41 39.4 0.395 29 1 0 3 112 74 30 31.6 0.197 25 1 0
8 181 68 36 30.1 0.615 60 1 1 2 121 70 32 39.1 0.886 23 0 1
1 168 88 29 35 0.905 52 1 0 1 144 82 46 46.1 0.335 46 1 1
2 101 58 17 24.2 0.614 23 0 1 2 96 68 13 21.1 0.647 26 0 0
3 107 62 13 22.9 0.678 23 1 1 12 121 78 17 26.5 0.259 62 0 0
2 100 64 23 29.7 0.368 21 0 1 4 154 72 29 31.3 0.338 37 0 0
6 125 78 31 27.6 0.565 49 1 1 10 125 70 26 31.1 0.205 41 1 0
2 122 76 27 35.9 0.483 26 0 0 2 114 68 22 28.7 0.092 25 0 1
1 115 70 30 34.6 0.529 32 1 0 7 114 76 17 23.8 0.466 31 0 0
2 115 64 22 30.8 0.421 21 0 0 1 130 60 23 28.6 0.692 21 0 1
1 79 75 30 32 0.396 22 0 1 4 112 78 40 39.4 0.236 38 0 1
7 150 78 29 35.2 0.692 54 1 0 1 91 54 25 25.2 0.234 23 0 0
1 100 72 12 25.3 0.658 28 0 0 12 140 82 43 39.2 0.528 58 1 0
4 110 76 20 28.4 0.118 27 0 0 2 94 76 18 31.6 0.649 23 0 1
2 84 50 23 30.4 0.968 21 0 1 10 148 84 48 37.6 1.001 51 1 1
3 61 82 28 34.4 0.243 46 0 1 4 117 62 12 29.7 0.38 30 1 0
3 99 80 11 19.3 0.284 30 0 0 3 80 82 31 34.2 1.292 27 1 0
4 154 62 31 32.8 0.237 23 0 1 6 103 72 32 37.7 0.324 55 0 1
6 111 64 39 34.2 0.26 24 0 1 0 124 70 20 27.4 0.254 36 1 0
1 143 74 22 26.2 0.256 21 0 0 1 81 74 41 46.3 1.096 32 0 0
4 189 110 31 28.5 0.68 37 0 1 4 116 72 12 22.1 0.463 37 0 1
7 103 66 32 39.1 0.344 31 1 1 8 124 76 24 28.7 0.687 52 1 1
1 71 78 50 33.2 0.422 21 0 1 0 137 84 27 27.3 0.231 59 0 1
9 112 82 32 34.2 0.26 36 1 1 4 148 60 27 30.9 0.15 29 1 0
1 136 74 50 37.4 0.399 24 0 1 9 145 80 46 37.9 0.637 40 1 1
1 93 56 11 22.5 0.417 22 0 0 1 107 72 30 30.8 0.821 24 0 1
12 151 70 40 41.8 0.742 38 1 1 1 97 70 40 38.1 0.218 30 0 1
5 144 82 26 32 0.452 58 1 1 2 112 86 42 38.4 0.246 28 0 1
2 99 52 15 24.6 0.637 21 0 1 1 109 56 21 25.2 0.833 23 0 1
1 120 80 48 38.9 1.162 41 0 1 7 187 68 39 37.7 0.254 41 1 0
3 129 92 49 36.4 0.968 32 1 0 7 179 95 31 34.2 0.164 60 0 0
6 80 66 30 26.2 0.313 41 0 0 2 105 58 40 34.9 0.225 25 0 0
3 191 68 15 30.9 0.299 34 0 1 0 95 80 45 36.5 0.33 26 0 1
4 99 72 17 25.6 0.294 28 0 0 0 137 68 14 24.8 0.143 21 0 1
1 97 70 15 18.2 0.147 21 0 0 0 100 88 60 46.8 0.962 31 0 0
1 167 74 17 23.4 0.447 33 1 0 0 180 90 26 36.5 0.314 35 1 1
2 122 70 27 36.8 0.34 27 0 0 1 90 62 12 27.2 0.58 24 0 0
3 120 70 30 42.9 0.452 30 0 0 6 154 78 41 46.1 0.571 27 0 0
2 56 56 28 24.2 0.332 22 0 0 0 177 60 29 34.6 1.072 21 1 1
3 124 80 33 33.2 0.305 26 0 0 8 85 55 20 24.4 0.136 42 0 1
12 88 74 40 35.3 0.378 48 0 1 9 152 78 34 34.2 0.893 33 1 1
0 198 66 32 41.3 0.502 28 1 1 0 188 82 14 32 0.682 22 1 0
5 139 64 35 28.6 0.411 26 0 1 7 168 88 42 38.2 0.787 40 1 1
2 197 70 99 34.7 0.575 62 1 0 2 142 82 18 24.7 0.761 21 0 0
8 126 74 38 25.9 0.162 39 0 1 3 158 76 36 31.6 0.851 28 1 1
3 130 78 23 28.4 0.323 34 1 0 2 100 54 28 37.8 0.498 24 0 1
1 164 82 43 32.8 0.341 50 0 1 4 95 60 32 35.4 0.284 28 0 1
2 122 52 43 36.2 0.816 28 0 0 4 85 58 22 27.8 0.306 28 0 0
0 151 90 46 42.1 0.371 21 1 1 6 144 72 27 33.9 0.255 40 0 1
3 111 90 12 28.4 0.495 29 0 0 1 107 68 19 26.5 0.165 24 0 1
6 115 60 39 33.7 0.245 40 1 0 5 105 72 29 36.9 0.159 28 0 0
7 194 68 28 35.9 0.745 41 1 0 4 184 78 39 37 0.264 31 1 0
0 95 85 25 37.4 0.247 24 1 0 7 124 70 33 25.5 0.161 37 0 1
1 111 62 13 24 0.138 23 0 0 7 137 90 41 32 0.391 39 0 0
9 57 80 37 32.8 0.096 41 0 1 2 157 74 35 39.4 0.134 30 0 1
2 95 54 14 26.1 0.748 22 0 0 12 140 85 33 37.4 0.244 41 0 0
0 117 66 31 30.8 0.493 22 0 0 8 100 74 40 39.4 0.661 43 1 1
9 123 70 44 33.1 0.374 40 0 0 0 138 60 35 34.6 0.534 21 1 1
14 100 78 25 36.6 0.412 46 1 0 14 175 62 30 33.6 0.212 38 1 0
0 74 52 10 27.8 0.269 22 0 0 1 133 102 28 32.8 0.234 45 1 0
0 119 64 18 34.9 0.725 23 0 1 5 155 84 44 38.7 0.619 34 0 1
1 128 48 45 40.5 0.613 24 1 1 2 112 68 22 34.1 0.315 26 0 1
1 140 74 26 24.1 0.828 23 0 1 2 141 58 34 25.4 0.699 24 0 0
7 129 68 49 38.5 0.439 43 1 1 0 106 70 37 39.4 0.605 22 0 0
1 118 58 36 33.3 0.261 23 0 1 8 155 62 26 34 0.543 46 1 0
;
/*
In the following program, the PARTITION statement divides the data
into two parts. The training data have Role=0 and holds about 59\%
of the data; the rest of the data is used evaluate the fit. A
stepwise selection method selects the best model based on the
training observations.
*/
proc hplogistic data=Pima;
model Diabetes(event='1') = NPreg Glucose Pressure Triceps BMI Pedigree Age;
partition role=Role(train='0' test='1');
selection method=stepwise;
run;
/*
The following program displays the Partition Fit Statistics table
without partitioning your data set by identifying all of your data
as training data.
*/
data Pima;
set Pima;
Role=0;
run;
proc hplogistic data=Pima;
model Diabetes(event='1') = NPreg Glucose Pressure Triceps BMI Pedigree Age;
partition role=Role(train='0');
run;