/* BRANDS data set */ data brands; title 'Brand Choice Data'; input p1-p5 f1-f5; datalines; 5.99 5.99 5.99 5.99 4.99 12 19 22 33 14 5.99 5.99 3.99 3.99 4.99 34 26 8 27 5 5.99 3.99 5.99 3.99 4.99 13 37 15 27 8 5.99 3.99 3.99 5.99 4.99 49 1 9 37 4 3.99 5.99 5.99 3.99 4.99 31 12 6 18 33 3.99 5.99 3.99 5.99 4.99 4 29 16 42 9 3.99 3.99 5.99 5.99 4.99 37 10 5 35 13 3.99 3.99 3.99 3.99 4.99 16 14 5 51 14 ; /* CHOCS data set */ data chocs; title 'Chocolate Candy Data'; input subj choose dark soft nuts @@; datalines; 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1 2 0 0 0 0 2 0 0 0 1 2 0 0 1 0 2 0 0 1 1 2 0 1 0 0 2 1 1 0 1 2 0 1 1 0 2 0 1 1 1 3 0 0 0 0 3 0 0 0 1 3 0 0 1 0 3 0 0 1 1 3 0 1 0 0 3 0 1 0 1 3 1 1 1 0 3 0 1 1 1 4 0 0 0 0 4 0 0 0 1 4 0 0 1 0 4 0 0 1 1 4 1 1 0 0 4 0 1 0 1 4 0 1 1 0 4 0 1 1 1 5 0 0 0 0 5 1 0 0 1 5 0 0 1 0 5 0 0 1 1 5 0 1 0 0 5 0 1 0 1 5 0 1 1 0 5 0 1 1 1 6 0 0 0 0 6 0 0 0 1 6 0 0 1 0 6 0 0 1 1 6 0 1 0 0 6 1 1 0 1 6 0 1 1 0 6 0 1 1 1 7 0 0 0 0 7 1 0 0 1 7 0 0 1 0 7 0 0 1 1 7 0 1 0 0 7 0 1 0 1 7 0 1 1 0 7 0 1 1 1 8 0 0 0 0 8 0 0 0 1 8 0 0 1 0 8 0 0 1 1 8 0 1 0 0 8 1 1 0 1 8 0 1 1 0 8 0 1 1 1 9 0 0 0 0 9 0 0 0 1 9 0 0 1 0 9 0 0 1 1 9 0 1 0 0 9 1 1 0 1 9 0 1 1 0 9 0 1 1 1 10 0 0 0 0 10 0 0 0 1 10 0 0 1 0 10 0 0 1 1 10 0 1 0 0 10 1 1 0 1 10 0 1 1 0 10 0 1 1 1 ; /* DIABETES data set */ data diabetes; title 'Diabetes Data'; input patient relwt glufast glutest instest sspg group; label relwt = 'Relative weight' glufast = 'Fasting Plasma Glucose' glutest = 'Test Plasma Glucose' instest = 'Plasma Insulin during Test' sspg = 'Steady State Plasma Glucose' group = 'Clinical Group'; datalines; 1 0.81 80 356 124 55 1 2 0.95 97 289 117 76 1 3 0.94 105 319 143 105 1 4 1.04 90 356 199 108 1 5 1.00 90 323 240 143 1 6 0.76 86 381 157 165 1 7 0.91 100 350 221 119 1 8 1.10 85 301 186 105 1 9 0.99 97 379 142 98 1 10 0.78 97 296 131 94 1 11 0.90 91 353 221 53 1 12 0.73 87 306 178 66 1 13 0.96 78 290 136 142 1 14 0.84 90 371 200 93 1 15 0.74 86 312 208 68 1 16 0.98 80 393 202 102 1 17 1.10 90 364 152 76 1 18 0.85 99 359 185 37 1 19 0.83 85 296 116 60 1 20 0.93 90 345 123 50 1 21 0.95 90 378 136 47 1 22 0.74 88 304 134 50 1 23 0.95 95 347 184 91 1 24 0.97 90 327 192 124 1 25 0.72 92 386 279 74 1 26 1.11 74 365 228 235 1 27 1.20 98 365 145 158 1 28 1.13 100 352 172 140 1 29 1.00 86 325 179 145 1 30 0.78 98 321 222 99 1 31 1.00 70 360 134 90 1 32 1.00 99 336 143 105 1 33 0.71 75 352 169 32 1 34 0.76 90 353 263 165 1 35 0.89 85 373 174 78 1 36 0.88 99 376 134 80 1 37 1.17 100 367 182 54 1 38 0.85 78 335 241 175 1 39 0.97 106 396 128 80 1 40 1.00 98 277 222 186 1 41 1.00 102 378 165 117 1 42 0.89 90 360 282 160 1 43 0.98 94 291 94 71 1 44 0.78 80 269 121 29 1 45 0.74 93 318 73 42 1 46 0.91 86 328 106 56 1 47 0.95 85 334 118 122 1 48 0.95 96 356 112 73 1 49 1.03 88 291 157 122 1 50 0.87 87 360 292 128 1 51 0.87 94 313 200 233 1 52 1.17 93 306 220 132 1 53 0.83 86 319 144 138 1 54 0.82 86 349 109 83 1 55 0.86 96 332 151 109 1 56 1.01 86 323 158 96 1 57 0.88 89 323 73 52 1 58 0.75 83 351 81 42 1 59 0.99 98 478 151 122 2 60 1.12 100 398 122 176 1 61 1.09 110 426 117 118 1 62 1.02 88 439 208 244 2 63 1.19 100 429 201 194 2 64 1.06 80 333 131 136 1 65 1.20 89 472 162 257 2 66 1.05 91 436 148 167 2 67 1.18 96 418 130 153 1 68 1.01 95 391 137 248 1 69 0.91 82 390 375 273 1 70 0.81 84 416 146 80 1 71 1.10 90 413 344 270 2 72 1.03 100 385 192 180 1 73 0.97 86 393 115 85 1 74 0.96 93 376 195 106 1 75 1.10 107 403 267 254 1 76 1.07 112 414 281 119 1 77 1.08 94 426 213 177 2 78 0.95 93 364 156 159 1 79 0.74 93 391 221 103 1 80 0.84 90 356 199 59 1 81 0.89 99 398 76 108 1 82 1.11 93 393 490 259 1 83 1.19 85 425 143 204 2 84 1.18 89 318 73 220 1 85 1.06 96 465 237 111 2 86 0.95 111 558 748 122 2 87 1.06 107 503 320 253 2 88 0.98 114 540 188 211 2 89 1.16 101 469 607 271 2 90 1.18 108 486 297 220 2 91 1.20 112 568 232 276 2 92 1.08 105 527 480 233 2 93 0.91 103 537 622 264 2 94 1.03 99 466 287 231 2 95 1.09 102 599 266 268 2 96 1.05 110 477 124 60 2 97 1.20 102 472 297 272 2 98 1.05 96 456 326 235 2 99 1.10 95 517 564 206 2 100 1.12 112 503 408 300 2 101 0.96 110 522 325 286 2 102 1.13 92 476 433 226 2 103 1.07 104 472 180 239 2 104 1.10 75 455 392 242 2 105 0.94 92 442 109 157 2 106 1.12 92 541 313 267 2 107 0.88 92 580 132 155 2 108 0.93 93 472 285 194 2 109 1.16 112 562 139 198 2 110 0.94 88 423 212 156 2 111 0.91 114 643 155 100 2 112 0.83 103 533 120 135 2 113 0.92 300 1468 28 455 3 114 0.86 303 1487 23 327 3 115 0.85 125 714 232 279 3 116 0.83 280 1470 54 382 3 117 0.85 216 1113 81 378 3 118 1.06 190 972 87 374 3 119 1.06 151 854 76 260 3 120 0.92 303 1364 42 346 3 121 1.20 173 832 102 319 3 122 1.04 203 967 138 351 3 123 1.16 195 920 160 357 3 124 1.08 140 613 131 248 3 125 0.95 151 857 145 324 3 126 0.86 275 1373 45 300 3 127 0.90 260 1133 118 300 3 128 0.97 149 849 159 310 3 129 1.16 233 1183 73 458 3 130 1.12 146 847 103 339 3 131 1.07 124 538 460 320 3 132 0.93 213 1001 42 297 3 133 0.85 330 1520 13 303 3 134 0.81 123 557 130 152 3 135 0.98 130 670 44 167 3 136 1.01 120 636 314 220 3 137 1.19 138 741 219 209 3 138 1.04 188 958 100 351 3 139 1.06 339 1354 10 450 3 140 1.03 265 1263 83 413 3 141 1.05 353 1428 41 480 3 142 0.91 180 923 77 150 3 143 0.90 213 1025 29 209 3 144 1.11 328 1246 124 442 3 145 0.74 346 1568 15 253 3 ; /* ESR data set */ data esr; title 'ESR Data'; input id fibrin globulin response @@; datalines; 1 2.52 38 0 2 2.56 31 0 3 2.19 33 0 4 2.18 31 0 5 3.41 37 0 6 2.46 36 0 7 3.22 38 0 8 2.21 37 0 9 3.15 39 0 10 2.60 41 0 11 2.29 36 0 12 2.35 29 0 13 5.06 37 1 14 3.34 32 1 15 2.38 37 1 16 3.15 36 0 17 3.53 46 1 18 2.68 34 0 19 2.60 38 0 20 2.23 37 0 21 2.88 30 0 22 2.65 46 0 23 2.09 44 1 24 2.28 36 0 25 2.67 39 0 26 2.29 31 0 27 2.15 31 0 28 2.54 28 0 29 3.93 32 1 30 3.34 30 0 31 2.99 36 0 32 3.32 35 0 ; /* MATCH_11 data set */ data match_11; title 'MATCH_11 Data'; input pair low age lwt race smoke ptd ht ui @@; datalines; 1 0 14 135 1 0 0 0 0 1 1 14 101 3 1 1 0 0 2 0 15 98 2 0 0 0 0 2 1 15 115 3 0 0 0 1 3 0 16 95 3 0 0 0 0 3 1 16 130 3 0 0 0 0 4 0 17 103 3 0 0 0 0 4 1 17 130 3 1 1 0 1 5 0 17 122 1 1 0 0 0 5 1 17 110 1 1 0 0 0 6 0 17 113 2 0 0 0 0 6 1 17 120 1 1 0 0 0 7 0 17 113 2 0 0 0 0 7 1 17 120 2 0 0 0 0 8 0 17 119 3 0 0 0 0 8 1 17 142 2 0 0 1 0 9 0 18 100 1 1 0 0 0 9 1 18 148 3 0 0 0 0 10 0 18 90 1 1 0 0 1 10 1 18 110 2 1 1 0 0 11 0 19 150 3 0 0 0 0 11 1 19 91 1 1 1 0 1 12 0 19 115 3 0 0 0 0 12 1 19 102 1 0 0 0 0 13 0 19 235 1 1 0 1 0 13 1 19 112 1 1 0 0 1 14 0 20 120 3 0 0 0 1 14 1 20 150 1 1 0 0 0 15 0 20 103 3 0 0 0 0 15 1 20 125 3 0 0 0 1 16 0 20 169 3 0 1 0 1 16 1 20 120 2 1 0 0 0 17 0 20 141 1 0 1 0 1 17 1 20 80 3 1 0 0 1 18 0 20 121 2 1 0 0 0 18 1 20 109 3 0 0 0 0 19 0 20 127 3 0 0 0 0 19 1 20 121 1 1 1 0 1 20 0 20 120 3 0 0 0 0 20 1 20 122 2 1 0 0 0 21 0 20 158 1 0 0 0 0 21 1 20 105 3 0 0 0 0 22 0 21 108 1 1 0 0 1 22 1 21 165 1 1 0 1 0 23 0 21 124 3 0 0 0 0 23 1 21 200 2 0 0 0 0 24 0 21 185 2 1 0 0 0 24 1 21 103 3 0 0 0 0 25 0 21 160 1 0 0 0 0 25 1 21 100 3 0 1 0 0 26 0 21 115 1 0 0 0 0 26 1 21 130 1 1 0 1 0 27 0 22 95 3 0 0 1 0 27 1 22 130 1 1 0 0 0 28 0 22 158 2 0 1 0 0 28 1 22 130 1 1 1 0 1 29 0 23 130 2 0 0 0 0 29 1 23 97 3 0 0 0 1 30 0 23 128 3 0 0 0 0 30 1 23 187 2 1 0 0 0 31 0 23 119 3 0 0 0 0 31 1 23 120 3 0 0 0 0 32 0 23 115 3 1 0 0 0 32 1 23 110 1 1 1 0 0 33 0 23 190 1 0 0 0 0 33 1 23 94 3 1 0 0 0 34 0 24 90 1 1 1 0 0 34 1 24 128 2 0 1 0 0 35 0 24 115 1 0 0 0 0 35 1 24 132 3 0 0 1 0 36 0 24 110 3 0 0 0 0 36 1 24 155 1 1 1 0 0 37 0 24 115 3 0 0 0 0 37 1 24 138 1 0 0 0 0 38 0 24 110 3 0 1 0 0 38 1 24 105 2 1 0 0 0 39 0 25 118 1 1 0 0 0 39 1 25 105 3 0 1 1 0 40 0 25 120 3 0 0 0 1 40 1 25 85 3 0 0 0 1 41 0 25 155 1 0 0 0 0 41 1 25 115 3 0 0 0 0 42 0 25 125 2 0 0 0 0 42 1 25 92 1 1 0 0 0 43 0 25 140 1 0 0 0 0 43 1 25 89 3 0 1 0 0 44 0 25 241 2 0 0 1 0 44 1 25 105 3 0 1 0 0 45 0 26 113 1 1 0 0 0 45 1 26 117 1 1 1 0 0 46 0 26 168 2 1 0 0 0 46 1 26 96 3 0 0 0 0 47 0 26 133 3 1 1 0 0 47 1 26 154 3 0 1 1 0 48 0 26 160 3 0 0 0 0 48 1 26 190 1 1 0 0 0 49 0 27 124 1 1 0 0 0 49 1 27 130 2 0 0 0 1 50 0 28 120 3 0 0 0 0 50 1 28 120 3 1 1 0 1 51 0 28 130 3 0 0 0 0 51 1 28 95 1 1 0 0 0 52 0 29 135 1 0 0 0 0 52 1 29 130 1 0 0 0 1 53 0 30 95 1 1 0 0 0 53 1 30 142 1 1 1 0 0 54 0 31 215 1 1 0 0 0 54 1 31 102 1 1 1 0 0 55 0 32 121 3 0 0 0 0 55 1 32 105 1 1 0 0 0 56 0 34 170 1 0 1 0 0 56 1 34 187 2 1 0 1 0 ; /* MATCH_NM data set */ data match_NM; title 'MATCH_NM Data'; input matchset subject agmt fndx chk agp1 agmn nlv liv wt aglp; datalines; 1 1 39 1 1 23 13 0 5 118 39 1 2 39 0 0 16 11 1 3 175 39 1 3 39 0 0 20 12 1 3 135 39 1 4 39 0 1 21 11 0 3 125 40 2 1 38 1 1 . 14 . . 118 39 2 2 38 0 1 20 15 0 2 183 38 2 3 38 0 1 19 11 0 5 218 38 2 4 38 0 1 23 13 0 2 192 37 3 1 38 1 1 22 15 2 2 125 38 3 2 38 0 1 20 14 0 2 123 38 3 3 38 0 1 19 13 3 2 140 37 3 4 38 0 1 18 13 0 2 160 38 4 1 38 1 1 24 14 2 3 150 38 4 2 38 0 1 26 13 1 1 130 38 4 3 38 0 0 23 14 0 4 140 38 4 4 38 0 1 25 16 0 2 130 38 5 1 38 1 1 21 17 0 2 150 38 5 2 38 0 0 20 12 1 2 148 38 5 3 38 0 1 . 13 . . 134 39 5 4 38 0 1 16 14 0 6 138 38 6 1 38 1 1 24 12 1 3 116 39 6 2 38 0 1 19 12 0 2 145 35 6 3 38 0 1 21 10 4 3 195 35 6 4 38 0 1 25 8 0 1 180 38 7 1 37 1 1 . 13 . . 137 37 7 2 37 0 1 20 11 2 2 135 37 7 3 37 0 1 18 10 2 3 155 37 7 4 37 0 1 22 13 2 2 120 38 8 1 36 1 1 . 14 . . 126 36 8 2 36 0 1 20 12 1 2 191 36 8 3 36 0 0 17 10 1 3 185 37 8 4 36 0 0 23 12 0 2 119 37 9 1 35 1 1 23 14 0 3 129 36 9 2 35 0 0 21 11 0 3 170 34 9 3 36 0 1 22 14 0 4 110 36 9 4 35 0 0 24 11 0 2 155 35 10 1 35 1 0 21 12 0 2 105 29 10 2 36 0 1 26 13 1 2 115 36 10 3 36 0 0 22 12 2 3 120 36 10 4 36 0 1 33 16 0 1 150 36 11 1 35 1 1 . 11 . . 135 35 11 2 35 0 0 18 13 2 2 110 35 11 3 35 0 1 19 11 0 3 170 36 11 4 35 0 1 21 12 0 2 145 36 12 1 34 1 0 25 10 1 1 170 34 12 2 35 0 1 27 13 0 4 140 35 12 3 34 0 1 20 11 0 3 240 34 12 4 34 0 0 25 16 1 1 100 35 13 1 33 1 1 . 14 . . 92 33 13 2 33 0 1 21 11 0 1 160 33 13 3 32 0 1 24 12 0 2 155 32 13 4 33 0 1 25 12 1 2 132 33 14 1 33 1 1 28 14 0 5 110 33 14 2 33 0 1 21 12 0 2 145 29 14 3 33 0 1 20 13 1 2 155 29 14 4 33 0 1 21 13 0 1 110 33 15 1 32 1 1 30 13 0 1 129 32 15 2 32 0 1 25 11 0 2 131 32 15 3 32 0 0 20 9 1 2 218 26 15 4 32 0 1 23 16 0 2 115 32 16 1 31 1 1 30 14 1 0 110 30 16 2 30 0 1 21 14 0 3 130 30 16 3 31 0 1 23 11 0 2 97 31 16 4 31 0 1 24 13 0 3 120 31 17 1 68 1 1 22 12 0 3 130 50 17 2 68 0 1 34 14 0 3 150 53 17 3 68 0 0 . 13 . . 123 35 17 4 68 0 0 19 12 0 7 145 46 18 1 64 1 0 30 14 1 3 135 53 18 2 64 0 1 . 14 . . 132 44 18 3 64 0 1 26 11 0 5 205 42 18 4 64 0 1 25 10 0 2 127 50 19 1 63 1 1 21 15 0 5 120 52 19 2 63 0 0 . 12 . . 145 46 19 3 63 0 0 . 14 . . 175 51 19 4 63 0 0 24 11 0 3 144 50 20 1 62 1 0 . 16 . . 163 33 20 2 62 0 1 26 15 0 2 170 39 20 3 62 0 0 32 12 0 2 134 53 20 4 62 0 1 22 12 1 3 155 39 21 1 61 1 1 28 14 0 3 145 53 21 2 61 0 1 26 13 0 1 140 50 21 3 61 0 1 28 15 1 3 120 41 21 4 61 0 1 27 14 0 2 134 45 22 1 61 1 1 22 16 0 4 150 56 22 2 62 0 0 30 11 0 1 117 36 22 3 62 0 1 25 15 1 4 147 52 22 4 62 0 1 26 13 1 3 124 52 23 1 61 1 1 26 17 0 2 129 34 23 2 62 0 1 33 11 0 1 170 54 23 3 61 0 0 25 13 0 3 153 50 23 4 61 0 1 29 13 1 2 130 55 24 1 61 1 0 21 15 0 3 145 53 24 2 61 0 1 18 13 0 5 140 56 24 3 61 0 1 22 17 0 2 155 55 24 4 61 0 1 23 15 1 3 116 43 25 1 60 1 1 28 17 0 2 115 51 25 2 60 0 0 25 11 0 2 175 42 25 3 60 0 0 24 13 0 2 179 50 25 4 60 0 1 33 15 0 3 119 47 26 1 58 1 1 20 12 1 5 153 53 26 2 58 0 0 25 16 0 3 185 55 26 3 58 0 0 . 12 . . 280 42 26 4 58 0 1 24 10 1 0 140 25 27 1 55 1 1 30 16 1 2 126 44 27 2 55 0 0 30 13 0 2 193 50 27 3 55 0 0 . 12 . . 140 55 27 4 55 0 1 24 14 0 6 116 47 28 1 55 1 1 24 14 0 4 140 52 28 2 55 0 0 . 14 . . 138 50 28 3 55 0 1 16 12 2 3 175 47 28 4 55 0 1 26 15 2 4 155 50 29 1 52 1 0 . 12 . . 125 36 29 2 52 0 1 28 12 0 2 113 45 29 3 52 0 0 20 14 2 6 110 40 29 4 52 0 0 25 13 0 3 190 48 30 1 52 1 1 23 14 0 3 114 50 30 2 52 0 1 21 12 0 3 126 43 30 3 52 0 1 23 11 1 2 159 42 30 4 52 0 1 20 11 0 5 170 42 31 1 51 1 0 24 16 0 5 156 52 31 2 51 0 0 24 12 3 4 161 50 31 3 51 0 1 22 13 0 2 150 45 31 4 51 0 1 24 13 0 5 115 51 32 1 49 1 1 . 14 0 . 95 49 32 2 49 0 0 25 12 0 2 235 44 32 3 49 0 1 24 13 0 3 145 44 32 4 49 0 1 25 13 0 3 123 49 33 1 48 1 1 22 11 0 3 145 48 33 2 48 0 0 22 11 0 1 155 48 33 3 48 0 1 . 12 . . 115 48 33 4 48 0 0 19 11 7 0 190 29 34 1 47 1 1 26 14 0 4 120 47 34 2 47 0 0 20 12 0 5 110 47 34 3 47 0 1 24 14 0 2 148 45 34 4 47 0 1 22 13 0 3 120 45 35 1 47 1 1 19 12 0 1 132 47 35 2 47 0 0 23 15 1 3 115 29 35 3 47 0 1 23 13 0 2 125 47 35 4 47 0 1 21 12 1 5 120 39 36 1 46 1 0 27 15 1 11 155 46 36 2 46 0 1 19 11 0 3 170 45 36 3 46 0 1 26 13 0 7 180 46 36 4 46 0 1 15 13 0 1 179 40 37 1 46 1 1 27 12 4 4 137 46 37 2 46 0 1 23 12 0 4 107 46 37 3 46 0 1 22 11 0 6 144 46 37 4 46 0 1 17 13 0 3 89 39 38 1 45 1 1 33 14 0 2 80 45 38 2 45 0 1 25 13 1 1 142 38 38 3 45 0 0 20 11 1 1 150 45 38 4 45 0 1 22 11 0 3 154 46 39 1 45 1 0 . 12 . . 90 45 39 2 45 0 0 23 11 0 2 150 45 39 3 45 0 1 20 12 0 1 102 28 39 4 45 0 1 30 12 0 3 110 45 40 1 45 1 1 18 15 4 4 101 45 40 2 45 0 1 22 17 1 2 109 40 40 3 45 0 1 30 13 0 2 210 40 40 4 45 0 1 22 10 0 5 198 33 41 1 45 1 1 25 16 1 4 124 45 41 2 45 0 0 23 12 3 3 133 45 41 3 45 0 1 23 13 0 3 120 46 41 4 45 0 1 23 12 0 4 165 35 42 1 44 1 1 25 12 0 3 130 44 42 2 44 0 1 27 13 1 3 240 45 42 3 44 0 1 27 14 0 1 125 44 42 4 44 0 1 . 13 . . 183 44 43 1 44 1 1 24 15 0 1 130 44 43 2 44 0 0 22 15 0 1 105 44 43 3 44 0 1 23 12 0 5 123 33 43 4 44 0 1 18 17 1 7 180 44 44 1 43 1 1 27 15 0 2 130 43 44 2 43 0 1 31 12 0 1 104 43 44 3 43 0 1 14 12 1 2 158 21 44 4 43 0 1 20 14 0 6 160 39 45 1 28 1 1 . 12 . . 108 29 45 2 27 0 1 22 12 0 1 127 27 45 3 28 0 1 20 11 0 2 145 27 45 4 28 0 1 23 16 0 2 127 29 46 1 53 1 1 29 12 0 4 132 50 46 2 53 0 1 28 11 0 3 140 49 46 3 53 0 0 . 12 . . 98 43 46 4 53 0 1 26 11 0 1 130 49 47 1 56 1 1 21 17 1 6 130 47 47 2 56 0 0 27 11 0 4 265 42 47 3 56 0 1 26 13 0 4 195 50 47 4 56 0 1 25 12 2 2 125 47 48 1 41 1 1 25 16 1 3 105 27 48 2 41 0 1 20 13 1 4 161 31 48 3 41 0 0 21 14 0 5 135 36 48 4 41 0 1 22 12 0 4 185 41 49 1 41 1 1 40 15 0 1 115 41 49 2 41 0 1 21 16 0 3 140 41 49 3 40 0 1 21 12 0 4 145 40 49 4 41 0 0 26 14 2 3 195 41 50 1 41 1 1 34 13 1 2 138 42 50 2 42 0 1 . 13 . . 118 41 50 3 41 0 0 30 12 1 2 129 41 50 4 41 0 1 21 12 0 2 180 41 ; /* MORTAL data set */ data mortal; title 'Perinatal Mortality Data'; input deaths tbirths cigs age gestpd; datalines; 50 365 1 1 1 9 49 2 1 1 41 188 1 2 1 4 15 2 2 1 24 4036 1 1 2 6 465 2 1 2 14 1508 1 2 2 1 125 2 2 2 ; /* PAIRS data set */ data pairs; title 'Paired Comparison Data'; input a b c d e f g h; datalines; . 17 39 44 7 40 18 23 43 . 25 39 13 17 30 35 21 35 . 51 11 14 48 26 16 21 9 . 3 21 18 11 53 47 49 57 . 39 31 58 20 43 46 39 21 . 40 22 42 30 12 42 29 20 . 17 37 25 34 49 2 38 43 . ; /* PROSTATE data set */ data prostate; title 'Prostate Data'; input case age acid xray size grade nodalinv @@; lacd=log(acid); datalines; 1 66 .48 0 0 0 0 2 68 .56 0 0 0 0 3 66 .50 0 0 0 0 4 56 .52 0 0 0 0 5 58 .50 0 0 0 0 6 60 .49 0 0 0 0 7 65 .46 1 0 0 0 8 60 .62 1 0 0 0 9 50 .56 0 0 1 1 10 49 .55 1 0 0 0 11 61 .62 0 0 0 0 12 58 .71 0 0 0 0 13 51 .65 0 0 0 0 14 67 .67 1 0 1 1 15 67 .47 0 0 1 0 16 51 .49 0 0 0 0 17 56 .50 0 0 1 0 18 60 .78 0 0 0 0 19 52 .83 0 0 0 0 20 56 .98 0 0 0 0 21 67 .52 0 0 0 0 22 63 .75 0 0 0 0 23 59 .99 0 0 1 1 24 64 1.87 0 0 0 0 25 61 1.36 1 0 0 1 26 56 .82 0 0 0 1 27 64 .40 0 1 1 0 28 61 .50 0 1 0 0 29 64 .50 0 1 1 0 30 63 .40 0 1 0 0 31 52 .55 0 1 1 0 32 66 .59 0 1 1 0 33 58 .48 1 1 0 1 34 57 .51 1 1 1 1 35 65 .49 0 1 0 1 36 65 .48 0 1 1 0 37 59 .63 1 1 1 0 38 61 1.02 0 1 0 0 39 53 .76 0 1 0 0 40 67 .95 0 1 0 0 41 53 .66 0 1 1 0 42 65 .84 1 1 1 1 43 50 .81 1 1 1 1 44 60 .76 1 1 1 1 45 45 .70 0 1 1 1 46 56 .78 1 1 1 1 47 46 .70 0 1 0 1 48 67 .67 0 1 0 1 49 63 .82 0 1 0 1 50 57 .67 0 1 1 1 51 51 .72 1 1 0 1 52 64 .89 1 1 0 1 53 68 1.26 1 1 1 1 ; /* Example 1, page 20 */ proc logistic data=esr descending; model response=fibrin globulin; title 'ESR Data'; run; /* Example 2, pp. 28-30 */ /* pp. 28-29 */ data diabet2; set diabetes; grp=(group=1); run; proc logistic data=diabet2; model grp=glutest / plcl plrl waldcl waldrl; title 'Diabetes Data'; run; /* page 30 */ proc logistic data=diabet2; model grp=glutest / plcl plrl waldcl waldrl alpha=.01; run; /* Example 3, pp. 32-34 */ /* pp. 32-33 */ data diabet2; set diabetes; grp=(group=1); run; proc logistic data=diabet2; model grp=glutest / plrl waldrl; units glutest=10 -10; title 'Diabetes Data'; run; /* pp. 33-34 */ proc logistic data=diabet2 outest=stats1 noprint; model grp=glutest; run; proc print data=stats1; run; data stats2(keep=glutest or orcust1 orcust2); set stats1; or=exp(glutest); orcust1=exp(10*glutest); orcust2=exp(-10*glutest); run; proc print data=stats2; run; /* Example 4, pp. 37-44 */ /* pp. 37-38 */ proc logistic data=prostate descending noprint; model nodalinv=lacd xray size; output out=probs predicted=phat; run; proc print data=probs; title 'Prostate Data'; run; /* page 39 */ data probs1; set probs; predicts=(phat>=.5); run; proc freq data=probs1; tables nodalinv*predicts / norow nocol nopercent; run; /* page 40 */ data probs2; set probs; predicts=(phat>=.25); run; proc freq data=probs2; tables nodalinv*predicts / norow nocol nopercent; run; /* pp. 41-42 */ data new1; input acid xray size nodalinv; lacd=log(acid); nodeinv=nodalinv; nodalinv=.; datalines; .42 1 0 0 .66 1 0 0 .85 0 1 1 .52 1 0 0 .61 0 0 0 .75 0 0 1 .67 0 0 0 .67 1 1 1 .49 0 0 0 .45 0 0 0 ; data prostat2; set prostate new1; run; proc logistic data=prostat2 descending noprint; model nodalinv=lacd xray size; output out=probs3 predicted=phat; run; proc print data=probs3; var lacd xray size nodalinv nodeinv phat; run; /* page 43 */ data probs4; set probs3; if phat>=.5 then predicts=1; else predicts=0; run; proc freq data=probs4; tables nodeinv*predicts / norow nocol nopercent; run; /* page 44 */ data probs5; set probs3; brier=(phat-nodeinv)**2; run; proc means data=probs5 noprint; var brier; output out=brier1 n=n mean=; run; proc print data=brier1; var n brier; title2 'Brier Score'; run; /* Example 5, pp. 46-48 */ proc logistic data=prostate descending; model nodalinv=lacd xray size / ctable; title 'Prostate Data'; run; proc logistic data=prostate descending; model nodalinv=lacd xray size / ctable pprob=(.05 to 1.0 by .05); run; proc logistic data=prostate descending; model nodalinv = lacd xray size / ctable pprob=(.05 to 1.0 by .05) pevent= .25 .50; run; /* Example 6, pp. 52-64 */ /* pp. 52-54 */ proc logistic data=prostate descending; model nodalinv=age lacd xray size grade / selection=forward slentry=.25 details; title 'Prostate Data'; run; proc logistic data=prostate descending; model nodalinv=age lacd xray size grade / selection=forward slentry=.25 details stop=2; run; /* pp. 57-58 */ proc logistic data=prostate descending; model nodalinv=age lacd xray size grade / selection=backward details slstay=.10; run; /* pp. 60-63 */ proc logistic data=prostate descending; model nodalinv=age lacd xray size grade / selection=stepwise slentry=.3; run; proc logistic data=prostate descending; model nodalinv=age lacd xray size grade / selection=stepwise start=4; run; proc logistic data=prostate descending; model nodalinv=age lacd xray size grade / selection=stepwise include=4; run; /* page 64 */ proc logistic data=prostate descending; model nodalinv = age lacd xray size grade / selection=score best=2; run; /* Example 7, pp. 69-71 */ data diabet2; set diabetes; grp=(group=1); run; proc logistic data=diabet2; model grp=instest sspg / lackfit rsq; title 'Diabetes Data'; run; proc logistic data=diabet2; model grp=instest / lackfit rsq; run; /* Example 8, pp. 74-78 */ proc logistic data=esr descending; model response=fibrin / influence iplots; title 'ESR Data'; run; /* Example 9, pp. 83-84 */ data diabet2; set diabetes; grp=(group=1); run; proc logistic data=diabet2; model grp=instest / aggregate scale=deviance; title 'Diabetes Data'; run; proc logistic data=diabet2; model grp=instest; run; /* Example 10, pp. 89-91 */ proc logistic data=mortal; model deaths/tbirths=cigs age gestpd / outroc=roc1; title 'Perinatal Mortality Data'; run; proc print data=roc1; title2 'OUTROC= Data Set'; run; goptions cback=white colors=(black) border; axis1 length=2.5in; axis2 order=(0 to 1 by .1) length=2.5in; proc gplot data=roc1; symbol1 i=join v=none; title1; title2 'Perinatal Mortality ROC Curve'; plot _sensit_*_1mspec_ / haxis=axis1 vaxis=axis2; run; /* Example 11, pp. 94-96 */ data diabet3; set diabetes; grp=(group ne 3); run; proc logistic data=diabet3; model grp=glufast; title 'Diabetes Data'; run; proc plot data=diabet3; plot glufast*patient=grp; run; proc means data=diabet3 min max; by grp notsorted; var glufast; run; data diabet4; set diabet3; if glufast=114 then glufast=120; run; proc logistic data=diabet4; model grp=glufast; run; /* Example 12, pp. 100-109 */ /* pp. 100-105 */ goptions cback=white colors=(black); proc format; value gp 3='(3) Overt Diabetic ' 2='(2) Chem. Diabetic' 1='(1) Normal'; run; data diabetes; infile datalines eof=endfile; input patient relwt glufast glutest instest sspg group; label relwt = 'Relative weight' glufast = 'Fasting Plasma Glucose' glutest = 'Test Plasma Glucose' instest = 'Plasma Insulin during Test' sspg = 'Steady State Plasma Glucose' group = 'Clinical Group'; output; return; endfile: do glutest=250 to 1600 by 25; output; end; datalines; 1 0.81 80 356 124 55 1 2 0.95 97 289 117 76 1 3 0.94 105 319 143 105 1 4 1.04 90 356 199 108 1 5 1.00 90 323 240 143 1 6 0.76 86 381 157 165 1 7 0.91 100 350 221 119 1 8 1.10 85 301 186 105 1 9 0.99 97 379 142 98 1 10 0.78 97 296 131 94 1 11 0.90 91 353 221 53 1 12 0.73 87 306 178 66 1 13 0.96 78 290 136 142 1 14 0.84 90 371 200 93 1 15 0.74 86 312 208 68 1 16 0.98 80 393 202 102 1 17 1.10 90 364 152 76 1 18 0.85 99 359 185 37 1 19 0.83 85 296 116 60 1 20 0.93 90 345 123 50 1 21 0.95 90 378 136 47 1 22 0.74 88 304 134 50 1 23 0.95 95 347 184 91 1 24 0.97 90 327 192 124 1 25 0.72 92 386 279 74 1 26 1.11 74 365 228 235 1 27 1.20 98 365 145 158 1 28 1.13 100 352 172 140 1 29 1.00 86 325 179 145 1 30 0.78 98 321 222 99 1 31 1.00 70 360 134 90 1 32 1.00 99 336 143 105 1 33 0.71 75 352 169 32 1 34 0.76 90 353 263 165 1 35 0.89 85 373 174 78 1 36 0.88 99 376 134 80 1 37 1.17 100 367 182 54 1 38 0.85 78 335 241 175 1 39 0.97 106 396 128 80 1 40 1.00 98 277 222 186 1 41 1.00 102 378 165 117 1 42 0.89 90 360 282 160 1 43 0.98 94 291 94 71 1 44 0.78 80 269 121 29 1 45 0.74 93 318 73 42 1 46 0.91 86 328 106 56 1 47 0.95 85 334 118 122 1 48 0.95 96 356 112 73 1 49 1.03 88 291 157 122 1 50 0.87 87 360 292 128 1 51 0.87 94 313 200 233 1 52 1.17 93 306 220 132 1 53 0.83 86 319 144 138 1 54 0.82 86 349 109 83 1 55 0.86 96 332 151 109 1 56 1.01 86 323 158 96 1 57 0.88 89 323 73 52 1 58 0.75 83 351 81 42 1 59 0.99 98 478 151 122 2 60 1.12 100 398 122 176 1 61 1.09 110 426 117 118 1 62 1.02 88 439 208 244 2 63 1.19 100 429 201 194 2 64 1.06 80 333 131 136 1 65 1.20 89 472 162 257 2 66 1.05 91 436 148 167 2 67 1.18 96 418 130 153 1 68 1.01 95 391 137 248 1 69 0.91 82 390 375 273 1 70 0.81 84 416 146 80 1 71 1.10 90 413 344 270 2 72 1.03 100 385 192 180 1 73 0.97 86 393 115 85 1 74 0.96 93 376 195 106 1 75 1.10 107 403 267 254 1 76 1.07 112 414 281 119 1 77 1.08 94 426 213 177 2 78 0.95 93 364 156 159 1 79 0.74 93 391 221 103 1 80 0.84 90 356 199 59 1 81 0.89 99 398 76 108 1 82 1.11 93 393 490 259 1 83 1.19 85 425 143 204 2 84 1.18 89 318 73 220 1 85 1.06 96 465 237 111 2 86 0.95 111 558 748 122 2 87 1.06 107 503 320 253 2 88 0.98 114 540 188 211 2 89 1.16 101 469 607 271 2 90 1.18 108 486 297 220 2 91 1.20 112 568 232 276 2 92 1.08 105 527 480 233 2 93 0.91 103 537 622 264 2 94 1.03 99 466 287 231 2 95 1.09 102 599 266 268 2 96 1.05 110 477 124 60 2 97 1.20 102 472 297 272 2 98 1.05 96 456 326 235 2 99 1.10 95 517 564 206 2 100 1.12 112 503 408 300 2 101 0.96 110 522 325 286 2 102 1.13 92 476 433 226 2 103 1.07 104 472 180 239 2 104 1.10 75 455 392 242 2 105 0.94 92 442 109 157 2 106 1.12 92 541 313 267 2 107 0.88 92 580 132 155 2 108 0.93 93 472 285 194 2 109 1.16 112 562 139 198 2 110 0.94 88 423 212 156 2 111 0.91 114 643 155 100 2 112 0.83 103 533 120 135 2 113 0.92 300 1468 28 455 3 114 0.86 303 1487 23 327 3 115 0.85 125 714 232 279 3 116 0.83 280 1470 54 382 3 117 0.85 216 1113 81 378 3 118 1.06 190 972 87 374 3 119 1.06 151 854 76 260 3 120 0.92 303 1364 42 346 3 121 1.20 173 832 102 319 3 122 1.04 203 967 138 351 3 123 1.16 195 920 160 357 3 124 1.08 140 613 131 248 3 125 0.95 151 857 145 324 3 126 0.86 275 1373 45 300 3 127 0.90 260 1133 118 300 3 128 0.97 149 849 159 310 3 129 1.16 233 1183 73 458 3 130 1.12 146 847 103 339 3 131 1.07 124 538 460 320 3 132 0.93 213 1001 42 297 3 133 0.85 330 1520 13 303 3 134 0.81 123 557 130 152 3 135 0.98 130 670 44 167 3 136 1.01 120 636 314 220 3 137 1.19 138 741 219 209 3 138 1.04 188 958 100 351 3 139 1.06 339 1354 10 450 3 140 1.03 265 1263 83 413 3 141 1.05 353 1428 41 480 3 142 0.91 180 923 77 150 3 143 0.90 213 1025 29 209 3 144 1.11 328 1246 124 442 3 145 0.74 346 1568 15 253 3 ; proc logistic data=diabetes descending; model group=glutest; output out=probs predicted=prob xbeta=logit; format group gp.; run; proc print data=probs (obs=10); var patient group glutest _level_ prob; title1 'Printout of first 10 observations of PROBS'; run; title1; proc sort data=probs; by glutest; run; proc gplot data=probs; plot logit*glutest=_level_ / frame; title2 'Plot of Parallel Logits'; run; plot prob*glutest=_level_ / frame; title2 'Plot of Parallel Probability Lines'; run; data overt(rename=(prob=p3)) chem(rename=(prob=p2)); set probs; if mod(_n_,2)=1 then output overt; else output chem; run; data probs2; merge overt chem; keep glutest prob_3 prob_2 prob_1; prob_3=p3; prob_2=p2-p3; prob_1=1-p2; run; proc sort data=probs2; by glutest; run; symbol1 i=join line=1 value=x height=.75; symbol2 i=join line=2 value=plus height=.75; symbol3 i=join line=3 value=circle height=.75; axis1 label=(angle=-90 rotate=90 'Probability'); axis2 label=('Test Plasma Glucose'); title2 'Individual Density Plots'; footnote1 height=.75 font=zapf 'x x x ' h=1.0 font=zapf ' Overt ' height=.75 font=zapf '+ + + ' h=1.0 font=zapf ' Chemical ' height=.75 font=special 'H H H ' h=1.0 font=zapf ' Normal'; proc gplot data=probs2; plot prob_3*glutest=1 prob_2*glutest=2 prob_1*glutest=3 / overlay vaxis=axis1 haxis=axis2 frame; run; /* pp. 107 */ proc logistic data=diabetes descending outest=parms; model group=glutest; format group gp.; run; data parms; set parms; rename glutest=b1; run; data parms2; set parms; do glutest=250 to 1600 by 25; logit1=intercp1 + b1*glutest; logit2=intercp2 + b1*glutest; p1=exp(logit1)/(1+exp(logit1)); p2=exp(logit2)/(1+exp(logit2)); prob_1=p1; prob_2=p2-p1; prob_3=1-p2; output; end; run; proc gplot data=parms2; plot prob_3*glutest prob_2*glutest prob_1*glutest / overlay frame; run; /* pp. 108-109 */ proc catmod data=diabetes; direct glutest; response logits / out=cat_prob; model group=glutest; run; /* Example 13, pp. 112-116 */ data diffs; set match_11; retain pair1 lwt1 race3 race4 smoke1 ptd1 ht1 ui1 0; drop pair1 lwt1 race race3 race4 smoke1 ptd1 ht1 ui1; select(race); when (1) do; race1=0; race2=0; end; when (2) do; race1=1; race2=0; end; when (3) do; race1=0; race2=1; end; end; if (pair ne pair1) then do; pair1=pair; lwt1=lwt; race3=race1; race4=race2; smoke1=smoke; ptd1=ptd; ht1=ht; ui1=ui; end; else do; lwt=lwt-lwt1; race1=race1-race3; race2=race2-race4; smoke=smoke-smoke1; ptd=ptd-ptd1; ht=ht-ht1; ui=ui-ui1; output; end; title1 'Analysis of 1:1 Matched Case-Control Data'; proc logistic data=diffs; model low=race1 race2 smoke ht ui ptd lwt / noint; race: test race1=0, race2=0; output out=stats difchisq=d_chi difdev=d_dev h=hat predicted=pred; run; title1; options nolabel; proc gplot data=stats; axis1 label=(angle=-90 rotate=90 'Deviance Change'); plot d_dev*pred / frame vaxis=axis1; title2 'Change in Deviance Versus Predicted Probability'; run; axis2 label=(angle=-90 rotate=90 'Chi square Change'); bubble d_chi*pred=hat / frame vaxis=axis2; title2 'Change in Chi-Square Versus Predicted Probability'; run; /* Example 14, pp. 120-124 */ /* pp. 120-121 */ data match_NM; set match_NM; status=2-fndx; run; proc phreg data=match_NM; model status*fndx(0)=chk agp1 agmn nlv liv wt aglp / ties=discrete; strata agmt; run; /* pp. 123-124 */ data match_NM; set match_NM; missing = nmiss( of chk--aglp ); run; proc sql; create table subset (drop=count1) as select *, count(matchset) as count1 from match_NM where missing = 0 group by matchset having count(*) = 4 order by matchset; proc phreg data=subset; model status*fndx(0) = chk agp1 agmn nlv liv wt aglp; strata matchset; run; /* Example 15, pp. 126-129 */ /* pp. 126-128 */ proc genmod data=mortal; model deaths/tbirths=cigs age gestpd / link=logit dist=binomial; make 'parmest' out=parms; title 'Perinatal Mortality Data'; run; data parms1; set parms; if parm='AGE' or parm='CIGS' or parm='GESTPD' then do; oddsrat=exp(estimate); end; run; proc print data=parms1; run; proc genmod data=mortal; model deaths/tbirths = cigs age gestpd age*cigs age*gestpd / link=logit dist=binomial; run; /* pp. 128-129 */ data mortal1; set mortal; agexcigs=age*cigs; agexgest=age*gestpd; run; proc logistic data=mortal1; model deaths/tbirths = cigs age gestpd agexcigs agexgest; run; /* Example 16, pp. 132-134 */ data chocs; title 'Chocolate Candy Data'; input subj choose dark soft nuts @@; t=2-choose; datalines; 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1 2 0 0 0 0 2 0 0 0 1 2 0 0 1 0 2 0 0 1 1 2 0 1 0 0 2 1 1 0 1 2 0 1 1 0 2 0 1 1 1 3 0 0 0 0 3 0 0 0 1 3 0 0 1 0 3 0 0 1 1 3 0 1 0 0 3 0 1 0 1 3 1 1 1 0 3 0 1 1 1 4 0 0 0 0 4 0 0 0 1 4 0 0 1 0 4 0 0 1 1 4 1 1 0 0 4 0 1 0 1 4 0 1 1 0 4 0 1 1 1 5 0 0 0 0 5 1 0 0 1 5 0 0 1 0 5 0 0 1 1 5 0 1 0 0 5 0 1 0 1 5 0 1 1 0 5 0 1 1 1 6 0 0 0 0 6 0 0 0 1 6 0 0 1 0 6 0 0 1 1 6 0 1 0 0 6 1 1 0 1 6 0 1 1 0 6 0 1 1 1 7 0 0 0 0 7 1 0 0 1 7 0 0 1 0 7 0 0 1 1 7 0 1 0 0 7 0 1 0 1 7 0 1 1 0 7 0 1 1 1 8 0 0 0 0 8 0 0 0 1 8 0 0 1 0 8 0 0 1 1 8 0 1 0 0 8 1 1 0 1 8 0 1 1 0 8 0 1 1 1 9 0 0 0 0 9 0 0 0 1 9 0 0 1 0 9 0 0 1 1 9 0 1 0 0 9 1 1 0 1 9 0 1 1 0 9 0 1 1 1 10 0 0 0 0 10 0 0 0 1 10 0 0 1 0 10 0 0 1 1 10 0 1 0 0 10 1 1 0 1 10 0 1 1 0 10 0 1 1 1 ; proc phreg data=chocs outest=betas; strata subj; model t*choose(0)=dark soft nuts; run; data combos; set chocs; if subj=1; keep dark soft nuts; run; data probs; retain sumxbeta 0; set combos end=eof; if _n_=1 then set betas(rename=(dark=b1 soft=b2 nuts=b3)); keep dark soft nuts xbeta; array x[3] dark soft nuts; array b[3] b1-b3; xbeta=0; do j=1 to 3; xbeta=xbeta + x[j]*b[j]; end; xbeta=exp(xbeta); sumxbeta=sumxbeta+xbeta; if eof then call symput('sumxbeta',put(sumxbeta,best12.)); run; proc format; value df 1='dark' 0='milk'; value sf 1='soft' 0='hard'; value nf 1='nuts' 0='no nuts'; run; data probs1; set probs; p=xbeta / &sumxbeta; drop xbeta; format dark df. soft sf. nuts nf.; run; proc sort data=probs1; by descending p; run; proc print data=probs1; run; /* Example 17, pp. 139-144 */ /* pp. 139-143 */ /* global title statement */ title 'Brand Choice Data'; data brands; drop p1-p5 f1-f5 j; input p1-p5 f1-f5; array p[5] p1-p5; array f[5] f1-f5; array pb[5] price1-price5; array brand[5] brand1-brand5; do j=1 to 5; brand[j]=0; pb[j] =0; end; nobs=sum(of f1-f5); ch_set=_n_; do j=1 to 5; price = p[j]; brand[j] = 1; pb[j] = price; freq = f[j]; choose = 1; t = 1; /* choice occurs at time 1 */ output; freq = nobs-f[j]; choose = 0; t = 2; /* nonchoice occurs at time 2 */ output; brand[j] = 0; pb[j] = 0; end; datalines; 5.99 5.99 5.99 5.99 4.99 12 19 22 33 14 5.99 5.99 3.99 3.99 4.99 34 26 8 27 5 5.99 3.99 5.99 3.99 4.99 13 37 15 27 8 5.99 3.99 3.99 5.99 4.99 49 1 9 37 4 3.99 5.99 5.99 3.99 4.99 31 12 6 18 33 3.99 5.99 3.99 5.99 4.99 4 29 16 42 9 3.99 3.99 5.99 5.99 4.99 37 10 5 35 13 3.99 3.99 3.99 3.99 4.99 16 14 5 51 14 ; proc print data=brands(obs=20); run; proc phreg data=brands nosummary; title2 'Discrete Choice with Common Price Effect'; strata ch_set; model t*choose(0)=brand1-brand5 price; freq freq; run; proc phreg data=brands nosummary; title2 'Discrete Choice with Brand by Price Effects'; strata ch_set; model t*choose(0)=brand1-brand5 price1-price5; freq freq; run; /* pp. 143-144 */ proc phreg data=brands noprint outest=stats1; strata ch_set; model t*choose(0)=brand1-brand5 price; freq freq; run; proc phreg data=brands noprint outest=stats2; strata ch_set; model t*choose(0)=brand1-brand5 price1-price5; freq freq; run; data modtest(keep=chi_sq p_value); merge stats1 stats2(rename=(_lnlike_=lnlike2)); chi_sq = -2*(_lnlike_-lnlike2); p_value = 1-probchi(chi_sq,3); run; proc print data=modtest; title2 'Chi-Square Test for Comparing Models'; run; /* Example 18, pp. 146-149 */ goptions cback=white colors=(black); data ld50; infile datalines eof=endfile; input dose n y; p_hat=y/n; log_dose=log(dose); output; return; endfile: do dose=1 to 17 by .5; log_dose=log(dose); output; end; datalines; 0 49 0 2.6 50 6 3.8 48 16 5.1 46 24 7.7 49 42 10.2 50 44 ; proc probit data=ld50 log optc; model y/n = dose / d=normal inversecl lackfit; output out=new p=prob; title1 'Probit Analysis for LD50 Data'; run; title1; proc sort data=new; by log_dose; run; symbol1 i=none value=diamond height=.75; symbol2 i=join l=2 value=star height=.75; proc gplot data=new; plot p_hat*log_dose=1 prob*log_dose=2 / frame overlay vref=.5; title2 'Probit Plot of Observed and Fitted Probabilities'; run; /* Example 19, pp. 152-154 */ data pairs2; retain i x1-x8 0 resp 1; array item{*} a b c d e f g h; array x{*} x1-x8; set pairs; i+1; do j=1 to 8; if item{j} ne . then do; count=item{j}; x{i}=1; x{j}=-1; output; x{i}=0; x{j}=0; end; end; drop i j a b c d e f g h; run; proc print data=pairs2; title2 'PAIRS2 Data Set'; run; proc logistic data=pairs2 outest=parms; model resp=x1-x8 / noint; freq count; run; proc transpose data=parms out=parms1; run; proc sort data=parms1; where _name_ ne '_LNLIKE_'; by descending estimate; run; proc print data=parms1; title2 'Final Ranking of Items'; run;