Hello, I have a dataset with the following variables:  ID (person's ID number), LPA_Daily_min (daily light physical activity in minutes), ST_win (screen time in hours/day), Errors (number of errors on a cognitive test), Female (gender=female), Location (1-5), and Income_thresh (0, no; 1, yes). The aim is to predict  Errors from  LPA_Daily_min using regression while adjusting for ST_win , Female , Location , and Income_thresh . Data are below:   data forSas; input ID LPA_Daily_min ST_win Errors Female Location Income_thresh; datalines; 1 139.5225 5.5 3 1 1 1 2 139.5225 6.8571 4 0 1 1 3 62.8618266 5.9643 7 0 1 0 4 118.5685101 6.8214 4 0 1 0 5 78.705 6.9286 1 0 1 1 6 88.9264899 7.2857 0 0 1 0 7 65.9281734 8.7857 5 0 1 1 8 10.7325 10.6071 3 0 1 0 9 53.6625 7.25 1 1 1 1 10 64.9060101 7.8571 6 1 1 0 11 114.48 7.6429 2 1 1 0 12 56.2178367 8 0 0 1 1 13 63.3728367 8.0714 7 1 1 1 14 96.5925 6.1429 2 1 1 0 15 107.8360101 7.2143 1 0 1 1 16 50.085 6.7857 4 0 1 1 17 17.3764899 8.8929 1 0 1 1 18 35.775 6.3214 0 1 1 1 19 60.8175 6 4 0 1 0 20 38.8414899 8 1 0 1 1 21 10.7325 6.6429 1 0 2 1 22 96.5925 8.2143 3 1 2 0 23 55.7068266 6.0714 0 1 2 1 24 96.5925 7.2857 5 0 2 0 25 32.1975 7.5714 4 0 2 0 26 55.1956734 6.5714 3 1 2 0 27 51.1071633 6.1429 10 0 2 1 28 47.5296633 7.5357 0 0 2 0 29 60.8175 6.9643 8 0 2 0 30 74.6164899 7.1429 2 1 2 0 31 57.7510101 6.5 0 1 2 1 32 88.9264899 5.9286 8 1 2 1 33 96.5925 4.5 1 0 2 0 34 75.1275 7.5357 2 0 2 0 35 28.62 6.8214 7 1 2 0 36 94.5481734 7.0357 2 0 2 0 37 96.5925 5.8571 9 0 2 1 38 139.5225 4.9286 2 1 2 1 39 96.5925 7.3571 9 0 2 1 40 93.5260101 7.8571 6 1 2 1 41 92.5039899 5.4286 1 0 2 1 42 48.5518266 8.4643 7 0 2 1 43 101.1921633 5.2143 2 1 2 1 44 118.5685101 5.9643 16 0 2 0 45 71.0389899 6.3571 9 0 2 1 46 44.9743266 4.9286 0 0 2 1 47 63.8839899 6.2857 2 0 2 0 48 121.635 6.3571 1 1 2 1 49 93.015 6.4643 3 1 2 1 50 25.0425 8.2143 4 1 2 1 51 17.8875 7.2143 0 0 2 1 52 96.5925 4.6429 6 0 2 0 53 55.1956734 5.1786 12 0 2 0 54 28.1089899 7.6786 5 0 2 0 55 89.4375 5.0714 1 0 2 0 56 31.6864899 4.8214 0 1 2 0 57 71.0389899 8.7143 4 0 2 1 58 101.1921633 7.5357 17 0 2 1 59 93.5260101 5.25 8 1 2 1 60 83.8156734 7.7857 8 0 2 1 61 127.2568266 5.7857 3 0 2 1 62 75.1275 5 3 0 2 1 63 101.7031734 7.6429 0 0 2 1 64 50.085 9.7857 13 0 2 1 65 139.5225 6.8214 0 1 2 0 66 106.3028367 7.2857 1 1 2 1 67 85.86 6.5357 0 0 2 1 68 113.9689899 8.3929 14 0 2 1 69 12.7768266 6 2 0 2 0 70 29.6421633 7.2857 1 1 2 1 71 55.7068266 6.8929 2 0 2 1 72 60.8175 7.5714 0 1 2 1 73 54.1735101 6 0 1 2 0 74 78.705 4.0714 2 1 2 0 75 34.7528367 6.6071 1 0 2 1 76 32.7085101 4.6786 27 0 2 1 77 50.085 8.1786 6 1 2 1 78 100.6810101 7.8214 7 0 3 1 79 139.5225 5.5 2 1 3 1 80 38.8414899 8.7857 1 1 3 1 81 81.2603367 8 10 0 3 1 82 39.8635101 8.6429 3 1 3 1 83 59.7953367 3.4286 1 0 3 0 84 78.705 7.3571 0 1 3 1 85 17.8875 7.2143 6 0 3 0 86 60.8175 7.7857 0 0 3 0 87 110.3914899 7.5714 7 0 3 1 88 30.6643266 9.6429 3 0 3 1 89 25.5535101 7.5 1 0 3 1 90 56.7289899 7.4286 24 0 3 1 91 38.3303367 7.8571 0 0 3 1 92 46.5075 6.5 1 0 3 1 93 106.8139899 7.25 0 0 3 0 94 113.9689899 7.5 3 1 3 1 95 128.79 6.25 13 1 3 1 96 53.1514899 8.8214 0 1 3 1 97 94.5481734 5.5 5 1 3 0 98 32.7085101 10.4286 3 1 3 1 99 88.4153367 8.25 1 1 3 1 100 26.0646633 7.3929 2 1 3 1 101 16.8653367 5.4643 7 0 3 1 102 88.9264899 5.8214 5 1 3 1 103 30.6643266 6.6071 2 0 3 1 104 40.3746633 7.1071 3 0 3 1 105 17.8875 9 2 0 3 1 106 78.705 8.75 10 1 3 0 107 63.8839899 7.1071 2 0 3 1 108 110.3914899 6.5 2 1 3 1 109 121.1239899 7.2857 1 1 3 1 110 57.7510101 7.7857 3 0 3 1 111 35.775 6.8571 2 0 3 1 112 114.48 7.7143 2 1 3 1 113 50.5960101 8.8571 0 0 3 0 114 36.2860101 7.2857 8 0 3 1 115 79.2160101 6.0714 4 0 3 0 116 89.4375 7.8214 2 1 3 1 117 88.4153367 6.9643 1 1 3 1 118 19.9318266 7.7143 2 0 3 1 119 96.5925 8.4286 4 0 3 1 120 96.5925 8.2857 5 0 3 1 121 81.7714899 8.5357 0 1 3 1 122 139.5225 8.8571 1 0 3 1 123 45.4853367 8.8571 0 1 3 1 124 50.085 7.3214 7 0 3 0 125 96.5925 7.6429 3 0 3 1 126 45.9964899 5.7857 3 1 3 0 127 96.5925 8.3214 0 1 3 1 128 65.4171633 7.5714 3 0 3 1 129 71.0389899 6.2857 7 1 3 1 130 96.5925 7.2143 1 0 3 1 131 95.0593266 7.5 0 0 3 1 132 87.9043266 10.6071 2 0 3 1 133 78.705 8.2143 4 0 3 1 134 84.8378367 5.3571 2 1 3 0 135 88.9264899 7.8214 0 0 3 0 136 61.8396633 7.3929 1 1 3 0 137 30.6643266 7.1786 14 0 3 0 138 60.8175 8.6786 5 0 3 0 139 53.6625 6.8929 2 0 3 1 140 14.8210101 8.9286 0 1 3 0 141 54.6846633 7.8571 0 0 3 1 142 68.4835101 7.9643 0 1 3 1 143 64.9060101 7.8929 2 0 3 1 144 121.635 6.9643 1 1 3 1 145 28.62 2.9286 8 0 3 1 146 54.1735101 8.75 4 1 3 0 147 17.8875 7.75 3 0 3 1 148 58.7731734 7.5 3 1 3 1 149 101.7031734 4.25 25 0 3 1 150 42.4189899 8.5714 24 0 3 1 151 99.1478367 5.3571 1 1 3 0 152 113.9689899 5.5714 3 1 3 0 153 59.7953367 7.7857 1 0 3 1 154 48.0406734 7 3 1 3 1 155 96.5925 7.25 10 0 3 1 156 72.0610101 7.6429 1 1 3 1 157 60.8175 3.7857 6 1 3 1 158 47.0185101 4.6786 5 1 3 0 159 17.8875 2.9286 6 0 3 1 160 121.635 6.75 6 1 3 0 161 118.0575 6.7143 6 1 3 1 162 124.7014899 3.1786 4 0 3 1 163 63.8839899 7.6429 10 0 3 1 164 85.86 6.9286 3 0 3 1 165 119.5906734 7.2143 3 0 4 1 166 28.1089899 5.3571 4 1 4 1 167 36.7971633 6.9286 5 1 4 0 168 68.4835101 4 11 0 4 1 169 62.3506734 5.0357 0 0 4 1 170 42.93 6.8571 3 1 4 0 171 40.3746633 6.2857 5 0 4 0 172 12.7768266 6.6071 3 0 4 1 173 46.5075 7.3571 0 0 4 1 174 121.635 7.3929 6 0 4 1 175 94.0371633 6.4286 5 0 4 1 176 50.5960101 7.25 0 1 4 0 177 38.8414899 7.8929 5 1 4 0 178 93.5260101 6.4286 6 1 4 0 179 96.5925 7.2857 7 0 4 0 180 105.7918266 5.9643 5 0 4 1 181 96.0814899 7.2857 9 0 4 1 182 10.7325 6.4286 3 1 4 1 183 50.5960101 9.5357 5 0 4 0 184 131.8564899 6.7143 4 1 4 1 185 60.3064899 3.9286 2 1 4 0 186 70.5278367 5.5714 10 0 4 0 187 26.0646633 9.2143 8 0 4 0 188 73.0831734 8.7143 1 1 4 1 189 139.5225 10.6071 9 1 4 1 190 93.5260101 4.5714 2 0 4 0 191 139.5225 7.8929 3 1 5 1 192 33.7306734 7.5357 1 1 5 0 193 103.7475 9.1071 6 1 5 0 194 33.2196633 7.8929 8 1 5 0 195 83.3046633 9.5 2 0 5 1 196 128.79 7.8571 7 1 5 0 197 96.5925 6.5 14 0 5 1 198 96.5925 6.5714 10 0 5 0 199 88.9264899 6.6786 10 0 5 0 200 114.48 8.3214 12 0 5 0 201 111.4135101 6.4643 8 1 5 0 202 88.9264899 7.7857 8 0 5 0 203 62.8618266 8.2143 5 0 5 0 204 103.7475 6.6071 7 1 5 0 205 113.9689899 6.6429 9 1 5 1 206 139.5225 10.5714 10 1 5 1 207 22.9981734 7.4643 0 1 5 0 208 42.93 8.8929 38 1 5 1 209 96.5925 6 18 0 5 1 210 42.93 8.1429 11 0 5 0 211 90.9706734 7.25 20 0 5 1 212 105.2806734 8.3571 5 1 5 0 213 67.9725 6.8929 4 0 5 0 214 58.2621633 7.7143 13 0 5 1 215 20.9539899 9.1429 0 0 5 0 216 32.7085101 7.4286 4 0 5 1 217 139.5225 6.75 3 1 5 0 218 82.7935101 7.7143 0 1 5 0 219 25.5535101 9.5 1 1 5 0 220 53.1514899 8 1 1 5 0 221 82.7935101 8.75 1 0 5 0 222 67.9725 7.1071 4 0 5 0 223 57.7510101 7.4286 0 1 5 0 224 54.6846633 5.9286 1 1 5 1 225 61.8396633 7.0357 8 0 5 0 226 73.5943266 7.5714 4 0 5 0 227 10.7325 7.7857 4 1 5 0 228 37.8193266 7.0714 19 0 5 0 229 12.7768266 4 1 1 5 0 230 28.62 6.5357 2 0 5 0 231 85.86 7.6071 2 1 5 0 232 79.7271633 6.6429 7 0 5 0 233 103.7475 7.1429 5 0 5 0 234 39.3525 7.9643 10 1 5 0 235 96.0814899 7.5 3 1 5 1 236 39.3525 2.9286 3 0 5 0 237 96.5925 4 1 1 5 0 238 73.0831734 6.1071 5 1 5 0 239 45.9964899 7.1071 3 1 5 0 240 94.0371633 7.5 2 0 5 1 241 33.2196633 6.75 9 1 5 1 242 59.7953367 6.5714 0 1 5 0 243 113.9689899 5.5357 13 1 5 0 244 69.5056734 8.0357 22 1 5 0 245 54.6846633 7.1071 1 0 5 1 246 27.0868266 6.8571 8 0 5 0 247 10.7325 10.6071 3 1 5 1 248 32.1975 9.1786 3 0 5 0 249 96.5925 7.8571 5 1 5 1 250 96.0814899 6.7143 1 1 5 0 251 60.8175 7.9643 0 0 5 0 252 121.635 8.5357 3 1 5 0 253 67.9725 6.0714 3 0 5 0 254 121.635 6.25 4 1 5 1 255 50.085 8.6429 2 0 5 0 256 39.8635101 6.2857 5 0 5 0 257 71.55 8.4286 12 0 5 1 258 108.8581734 6.5 2 1 5 0 259 96.5925 7.1071 4 0 5 0 260 30.6643266 7.25 4 0 5 0 261 69.5056734 2.9286 2 1 5 1 262 56.7289899 7.8571 3 0 5 0 263 100.6810101 6.9643 1 1 5 0 264 103.7475 8.4643 9 1 5 1 265 95.5703367 5.9286 8 1 5 0 266 55.1956734 8.3214 9 0 5 1 267 96.5925 6.2857 0 0 5 1 268 88.9264899 7.2857 0 0 5 1 269 25.0425 7.25 7 1 5 0 270 89.9485101 7.7857 0 1 5 0 271 113.9689899 4.2143 16 0 5 1 272 53.1514899 6.6429 9 0 5 1 273 71.0389899 9.6071 0 1 5 1 274 39.3525 7.6786 5 1 5 1 275 8.1771633 8.0357 5 1 5 1 276 96.5925 6.3571 0 0 5 1 277 59.7953367 7.5 9 1 5 0 278 96.5925 5.9643 7 0 5 1 279 82.7935101 6.6429 10 1 5 0 280 64.9060101 7.3214 21 1 5 1 281 32.1975 8.3571 1 1 5 0 282 60.3064899 7.2143 3 0 5 1 283 96.0814899 5.7857 6 0 5 1 284 82.7935101 7.4643 9 1 5 0 285 53.6625 7.0714 13 1 5 0 286 54.6846633 8.7143 0 0 5 1 287 85.3489899 7.3929 2 1 5 0 288 106.3028367 4 26 1 5 1 289 92.5039899 6.1429 4 1 5 0 290 85.86 6.6786 9 0 5 1 291 121.1239899 5.9286 6 1 5 1 292 71.0389899 7.3214 2 0 5 0 293 64.9060101 8.6786 9 1 5 1 294 60.8175 8.7857 0 0 5 0 295 52.1293266 6.5 7 0 5 0 296 139.5225 7.6429 6 1 5 0 297 53.6625 5.0714 3 1 5 1 298 85.86 7.5357 2 0 5 0 299 35.2639899 6.7143 6 1 5 0 300 71.0389899 7.3929 3 0 5 0 301 35.775 7 4 0 5 0 302 83.3046633 6.2857 3 0 5 0 303 60.8175 5.75 29 1 5 1 304 93.5260101 6.3214 8 0 5 0 305 41.9078367 6.9286 0 1 5 0 306 60.8175 7.2143 3 1 5 1 307 71.0389899 7.5714 8 1 5 0 308 48.0406734 6.6429 2 0 5 0 309 65.9281734 7.9286 1 1 5 1 310 96.0814899 7.5357 9 0 5 0 311 63.8839899 7.4643 6 1 5 1 312 56.7289899 8.75 4 0 5 0 313 65.9281734 8.2143 0 0 5 0 314 60.8175 6.9286 4 0 5 0 315 65.9281734 3.6786 13 1 5 1 316 56.7289899 7.6429 1 0 5 0 317 81.2603367 10.6071 1 0 5 1 318 60.8175 6.3571 7 0 5 0 319 113.9689899 5.9286 7 0 5 1 320 108.8581734 6.75 7 1 5 1 321 22.9981734 5.8929 6 1 5 0 322 26.0646633 7.3571 5 0 5 0 323 41.3968266 8.5357 2 1 5 0 324 85.86 7.1071 3 1 5 0 325 60.8175 7.1429 1 0 5 1 326 139.5225 6.6071 3 1 5 0 327 124.1903367 2.9286 2 1 5 0 328 73.0831734 8.5 5 1 5 0 329 78.705 8.0357 10 0 5 1 330 101.1921633 7.6786 18 0 5 1 331 42.93 8.9643 3 0 5 0 332 68.4835101 8.0714 7 0 5 0 333 38.8414899 7 7 0 5 1 334 93.015 6.4643 5 0 5 0 335 10.7325 10.6071 1 1 5 0 336 45.9964899 6.7143 0 0 5 0 337 116.5243266 6.7857 5 1 5 0 338 103.7475 7.9643 9 0 5 1 339 45.4853367 7.7143 8 0 5 0 340 93.015 8.6429 1 1 5 0 341 60.3064899 7.5357 22 0 5 0 342 75.6385101 7.5 1 0 5 0 343 53.1514899 4.6429 14 0 5 1 344 50.085 8.7857 7 0 5 0 345 63.8839899 5.75 2 1 5 0 346 53.1514899 5.3571 8 1 5 1 347 14.8210101 7.4286 3 0 5 0 348 94.0371633 5.25 4 1 5 1 349 60.8175 9.25 2 1 5 0 350 28.62 8.4286 1 0 5 0 351 93.015 6.9643 2 1 5 0 352 29.1310101 9.0357 7 0 5 0 353 54.6846633 8 4 1 5 0 354 28.1089899 8.1071 0 0 5 0 355 78.705 8.8214 3 1 5 0 356 45.9964899 5.3214 5 0 5 0 357 76.1496633 7.6429 2 1 5 0 358 63.3728367 7.2857 3 1 5 0 359 30.1531734 7.0714 3 0 5 1 360 124.7014899 8.4643 7 0 5 0 361 96.0814899 5.4286 4 0 5 1 362 25.0425 6.7857 1 1 5 0 363 27.0868266 8.3929 1 1 5 0 364 40.8856734 7 1 1 5 0 365 67.9725 6.8571 6 0 5 0 366 35.2639899 8.8571 0 1 5 0 367 53.6625 8.5714 3 0 5 0 368 53.6625 8.2143 5 1 5 0 369 98.6368266 6.2857 2 1 5 0 370 90.9706734 7.1071 10 1 5 0 371 75.1275 5.6429 1 0 5 1 372 52.6403367 8.5357 5 0 5 1 373 75.1275 7.5714 0 1 5 0 374 28.1089899 8.1071 3 0 5 0 375 75.1275 7.5 5 1 5 0 376 27.0868266 8.6786 12 0 5 1 377 51.1071633 6.9286 3 1 5 1 378 96.5925 9.0357 12 0 5 0 379 82.2825 7.75 11 1 5 1 380 71.55 8.25 30 0 5 1 381 40.3746633 9 0 1 5 0 382 67.9725 5.4286 2 1 5 0 383 39.3525 5.7143 5 0 5 0 384 82.7935101 7.8214 8 1 5 0 385 96.0814899 7.1429 2 1 5 0 386 121.1239899 6.9286 19 1 5 1 387 81.7714899 9.2857 16 0 5 1 388 83.3046633 4.5714 2 1 5 0 389 78.1939899 5.3571 6 0 5 1 390 85.3489899 7.2857 4 0 5 0 391 42.93 8 7 0 5 1 392 45.9964899 9.1429 0 0 5 1 393 114.48 6.3571 1 0 5 0 394 65.4171633 7.6429 6 0 5 1 395 96.5925 6.4286 2 1 5 0 396 58.2621633 6.9286 3 0 5 0 397 73.5943266 7.6071 2 1 5 1 398 53.6625 5.5357 4 0 5 0 399 60.8175 7.7143 19 0 5 0 400 114.9910101 7.0357 15 1 5 1 401 74.6164899 8 1 0 5 1 402 114.9910101 4.1786 2 1 5 1 403 63.3728367 6.6786 10 1 5 1 404 70.0168266 8.1429 12 1 5 0 405 73.5943266 6.3571 1 0 5 0 406 37.3081734 7.4643 12 1 5 1 407 65.4171633 5.4643 2 1 5 0 408 50.085 7.3929 0 1 5 0 409 27.0868266 6.6786 0 1 5 0 410 63.8839899 7.6786 12 1 5 1 411 20.9539899 8.3571 6 1 5 0 412 33.7306734 5.8571 0 1 5 0 413 52.1293266 8.7143 1 0 5 1 414 12.7768266 7.8571 2 0 5 1 415 26.0646633 8.6071 4 1 5 1 416 78.1939899 9.1786 2 1 5 0 417 81.2603367 6.1429 5 0 5 0 418 60.8175 7.2857 1 1 5 0 419 85.86 8.9286 4 0 5 0 420 48.0406734 7.4286 4 0 5 0 421 45.9964899 9.0714 5 0 5 1 422 39.8635101 7.2143 8 0 5 1 423 67.4614899 7.8571 3 0 5 0 424 96.0814899 7 5 1 5 0 425 93.5260101 8.2857 7 1 5 0 426 84.8378367 7.8571 2 0 5 0 427 85.3489899 5.5 9 0 5 0 428 106.8139899 6.5714 15 0 5 0 429 78.705 7.7143 3 0 5 0 430 82.2825 6.6786 2 1 5 0 431 55.7068266 8.8571 1 1 5 1 432 47.5296633 8.0714 1 1 5 1 433 81.2603367 8.1429 6 1 5 1 434 60.8175 6.7857 6 0 5 1 435 12.7768266 8.6429 5 1 5 1 436 75.1275 6.5 1 1 5 0 437 44.4631734 5.9643 7 1 5 0 438 76.6606734 4.5714 2 0 5 0 439 60.8175 7.2857 4 1 5 0 440 81.7714899 8.1786 1 0 5 0 441 88.9264899 5.4286 6 0 5 1 442 96.5925 7.0714 10 0 5 1 443 82.7935101 3.8214 5 1 5 0 444 111.4135101 6.1429 5 0 5 0 ; run; Here are the the distribution, mean, and variance for Errors : Errors Frequency Percent Cumulative Frequency Cumulative Percent 0 55 12.39 55 12.39 1 60 13.51 115 25.90 2 57 12.84 172 38.74 3 53 11.94 225 50.68 4 35 7.88 260 58.56 5 37 8.33 297 66.89 6 25 5.63 322 72.52 7 27 6.08 349 78.60 8 19 4.28 368 82.88 9 17 3.83 385 86.71 10 15 3.38 400 90.09 11 3 0.68 403 90.77 12 8 1.80 411 92.57 13 6 1.35 417 93.92 14 4 0.90 421 94.82 15 2 0.45 423 95.27 16 3 0.68 426 95.95 17 1 0.23 427 96.17 18 2 0.45 429 96.62 19 3 0.68 432 97.30 20 1 0.23 433 97.52 21 1 0.23 434 97.75 22 2 0.45 436 98.20 24 2 0.45 438 98.65 25 1 0.23 439 98.87 26 1 0.23 440 99.10 27 1 0.23 441 99.32 29 1 0.23 442 99.55 30 1 0.23 443 99.77 38 1 0.23 444 100.00   Analysis Variable : Errors Mean Variance 4.9797297 27.7264505   My instinct was to use a model with a Poisson distribution; however, I realize I need a different distribution due to the overdispersion of Errors . Here was my Poisson model:   proc genmod data=forSAS plots=none namelen=65; class Female Location Income_thresh; model Errors=LPA_Daily_min ST_win Female Location Income_thresh / dist=Poisson;run; I do not know whether to instead use zero-inflated Poisson, negative binomial, or a zero-inflated negative binomial distribution. Does anyone know how I can determine which distribution is most suitable, and how to justify that (e.g., in a manuscript for publication), like a citation, significance test, and/or threshold number of a certain statistic? Thank you. ... View more

Hi everyone, I have a dataset where each row has the following variables: Ppt_ID (ID number), child_gender (Boy or Girl), and Data_days (Days of data). See below sample data:   Data Days; Input Ppt_ID child_gender $ Data_days; datalines; 1 Boy 22.6429 2 Boy 22.4286 3 Girl 15.28571 4 Boy 14.5714 5 Girl 17.21429 6 Boy 18.5714 7 Boy 21.5 8 Girl 14.42857 9 Boy 16.7143 10 Boy 20.2857 11 Boy 15.3571 12 Boy 12.8571 13 Girl 20.21429 14 Girl 20.07143 15 Boy 15.2143 16 Boy 19.0714 17 Girl 15.57143 18 Boy 13.8571 19 Boy 15.9286 20 Boy 17.5 21 Girl 17.57143 22 Boy 14.7143 23 Girl 17 24 Girl 15.14286 25 Girl 16.21429 26 Girl 16.71429 27 Boy 14 28 Girl 17.14286 29 Girl 14.35714 30 Girl 13.35714 31 Boy 14.5714 32 Boy 16.7143 33 Girl 15.64286 34 Girl 16.35714 35 Girl 13.35714 36 Girl 16.28571 37 Boy 16.4286 38 Girl 15.78571 39 Girl 16 40 Boy 15.5714 ; run;   I want to output the cumulative frequencies and percents by each child_gender , but in descending order within each group according to the outcome variable, Data_days . The code I have to produce frequencies by child_gender by descending Data_days is below:   proc sort data=Days;by child_gender;run; proc freq data=Days NOPRINT;by child_gender;table Data_days/ out=gender_days;run; proc sort data=gender_days;by child_gender descending Data_days;run; proc print data=gender_days;run;   The code produces the following, where Data_days is in descending order, by child_gender : child_gender Data_days COUNT PERCENT Boy 22.6429 1 5 Boy 22.4286 1 5 Boy 21.5 1 5 Boy 20.2857 1 5 Boy 19.0714 1 5 Boy 18.5714 1 5 Boy 17.5 1 5 Boy 16.7143 2 10 Boy 16.4286 1 5 Boy 15.9286 1 5 Boy 15.5714 1 5 Boy 15.3571 1 5 Boy 15.2143 1 5 Boy 14.7143 1 5 Boy 14.5714 2 10 Boy 14 1 5 Boy 13.8571 1 5 Boy 12.8571 1 5 Girl 20.2143 1 5 Girl 20.0714 1 5 Girl 17.5714 1 5 Girl 17.2143 1 5 Girl 17.1429 1 5 Girl 17 1 5 Girl 16.7143 1 5 Girl 16.3571 1 5 Girl 16.2857 1 5 Girl 16.2143 1 5 Girl 16 1 5 Girl 15.7857 1 5 Girl 15.6429 1 5 Girl 15.5714 1 5 Girl 15.2857 1 5 Girl 15.1429 1 5 Girl 14.4286 1 5 Girl 14.3571 1 5 Girl 13.3571 2 10   What I want to output is the following, where the cumulative count and percent are calculated beginning at the highest Data_days value, again by child_gender - that is, each cell is summing up the Count (or Percent) across all the previous rows and the current row: child_gender Data_days COUNT PERCENT Cumulative_count Cumulative_percent Boy 22.6429 1 5 1 5 Boy 22.4286 1 5 2 10 Boy 21.5 1 5 3 15 Boy 20.2857 1 5 4 20 Boy 19.0714 1 5 5 25 Boy 18.5714 1 5 6 30 Boy 17.5 1 5 7 35 Boy 16.7143 2 10 9 45 Boy 16.4286 1 5 10 50 Boy 15.9286 1 5 11 55 Boy 15.5714 1 5 12 60 Boy 15.3571 1 5 13 65 Boy 15.2143 1 5 14 70 Boy 14.7143 1 5 15 75 Boy 14.5714 2 10 17 85 Boy 14 1 5 18 90 Boy 13.8571 1 5 19 95 Boy 12.8571 1 5 20 100 Girl 20.2143 1 5 1 5 Girl 20.0714 1 5 2 10 Girl 17.5714 1 5 3 15 Girl 17.2143 1 5 4 20 Girl 17.1429 1 5 5 25 Girl 17 1 5 6 30 Girl 16.7143 1 5 7 35 Girl 16.3571 1 5 8 40 Girl 16.2857 1 5 9 45 Girl 16.2143 1 5 10 50 Girl 16 1 5 11 55 Girl 15.7857 1 5 12 60 Girl 15.6429 1 5 13 65 Girl 15.5714 1 5 14 70 Girl 15.2857 1 5 15 75 Girl 15.1429 1 5 16 80 Girl 14.4286 1 5 17 85 Girl 14.3571 1 5 18 90 Girl 13.3571 2 10 20 100   Could someone help me with this? Thank you. ... View more

Hello, I've created a graph from my data that is an overlaid histogram (HISTOGRAM statement). Here is my code: proc sgplot data=data; styleattrs datacolors=(Blue Green Red VLIP DEYBR Yellow) backcolor=white wallcolor=white; where race ~=''; format race $race.;histogram Week / group=race transparency=0.5; xaxis label='#Weeks' LABELATTRS=(size=14) VALUEATTRS=(size=13); keylegend / title='Weeks by race' TITLEATTRS=(size=16) valueattrs=(size=12); yaxis LABELATTRS=(size=14) VALUEATTRS=(size=13) grid GRIDATTRS=(color=LIGGR); run; Here is the graph that is produced:   I'd like to produce the same exact graph, except with lines instead of vertical bars (e.g., sgplot type "series"). I've tried a series graph, but a "y" variable is required, and there is only one variable in this graph (other than the "by" group) with the percent on the y-axis. I've Googled around and can't seem to find a solution. Can you help? Thanks. ... View more

Hello, I have a dataset where each participant had a program installed on their phones for several days (long format). The program measured the person's usage of a certain phone app in Duration in seconds ( Duration_s ) several times a day per person. Each row indicates the participant ID ( Ppt_ID ), the start datetime of the app usage ( Start ), the end datetime of the app usage ( End ), and Duration_s . See below the code:   Data Duration; Input Ppt_ID Start :DATETIME16. End :DATETIME16. Duration_s; format Start DATETIME16. End DATETIME16.; datalines; 1 26May20:17:00:47 26May20:17:00:47 0 1 26May20:17:00:47 26May20:17:01:11 24 1 26May20:19:38:10 26May20:19:38:10 0 1 26May20:19:38:10 26May20:19:38:12 2 1 26May20:20:08:41 26May20:20:09:51 70 1 26May20:20:09:51 26May20:20:10:19 28 1 26May20:20:28:25 26May20:20:28:26 1 1 26May20:20:28:26 26May20:20:28:41 15 1 26May20:20:44:08 26May20:20:44:11 3 1 26May20:21:55:30 26May20:21:55:32 2 1 26May20:21:59:03 26May20:21:59:07 4 1 26May20:23:43:05 26May20:23:43:05 0 1 26May20:23:43:05 26May20:23:43:49 44 1 27May20:00:20:01 27May20:00:20:14 13 1 27May20:00:31:22 27May20:00:31:25 3 1 27May20:06:05:52 27May20:06:06:04 12 1 27May20:18:11:33 27May20:18:11:52 19 1 27May20:19:15:36 27May20:19:15:50 14 1 27May20:19:17:09 27May20:19:17:11 2 1 28May20:00:14:34 28May20:00:15:39 65 1 28May20:00:34:06 28May20:00:34:06 0 1 28May20:00:34:06 28May20:00:34:09 3 1 28May20:16:09:20 28May20:16:09:21 1 1 28May20:16:09:21 28May20:16:09:37 16 1 28May20:16:09:37 28May20:16:09:43 6 1 28May20:16:10:37 28May20:16:12:59 142 1 28May20:16:12:59 28May20:16:13:11 12 1 28May20:16:15:08 28May20:16:15:29 21 1 28May20:16:17:39 28May20:16:17:42 3 1 28May20:16:17:42 28May20:16:18:41 59 1 28May20:16:20:01 28May20:16:20:25 24 1 28May20:16:22:00 28May20:16:22:16 16 1 28May20:16:27:12 28May20:16:27:30 18 1 13Jul20:21:30:23 13Jul20:21:30:23 0 1 13Jul20:21:30:23 13Jul20:21:32:13 110 1 13Jul20:22:02:51 13Jul20:22:02:55 4 1 13Jul20:22:02:55 13Jul20:22:02:57 2 1 13Jul20:22:05:15 13Jul20:22:05:21 6 1 13Jul20:22:23:14 13Jul20:22:23:14 0 1 13Jul20:22:23:14 13Jul20:22:23:18 4 1 13Jul20:22:23:24 13Jul20:22:24:52 88 1 13Jul20:22:26:41 13Jul20:22:27:14 33 1 13Jul20:22:35:45 13Jul20:22:50:24 879 1 13Jul20:22:59:23 13Jul20:22:59:30 7 1 13Jul20:22:59:37 13Jul20:22:59:51 14 1 13Jul20:23:00:08 13Jul20:23:00:20 12 1 13Jul20:23:00:25 13Jul20:23:23:37 1392 1 13Jul20:23:23:37 13Jul20:23:34:12 635 1 14Jul20:20:15:06 14Jul20:20:38:29 1403 1 14Jul20:20:38:29 14Jul20:20:43:17 288 1 14Jul20:20:55:03 14Jul20:20:57:49 166 1 14Jul20:21:04:56 14Jul20:21:06:07 71 1 14Jul20:21:06:07 14Jul20:21:06:16 9 1 14Jul20:21:10:07 14Jul20:21:12:29 142 1 14Jul20:22:32:18 14Jul20:22:34:05 107 1 14Jul20:22:40:38 14Jul20:22:40:41 3 1 14Jul20:22:40:41 14Jul20:22:40:48 7 1 14Jul20:22:49:27 14Jul20:22:49:28 1 1 14Jul20:22:49:28 14Jul20:22:49:30 2 1 14Jul20:22:49:39 14Jul20:22:49:49 10 1 14Jul20:23:28:04 14Jul20:23:28:06 2 1 14Jul20:23:28:10 14Jul20:23:32:09 239 1 14Jul20:23:44:08 14Jul20:23:44:35 27 1 14Jul20:23:44:35 14Jul20:23:48:08 213 1 15Jul20:16:46:31 15Jul20:16:46:53 22 1 15Jul20:16:46:53 15Jul20:16:48:53 120 1 15Jul20:16:51:33 15Jul20:16:56:04 271 1 15Jul20:16:56:04 15Jul20:16:57:02 58 1 15Jul20:19:44:30 15Jul20:19:45:32 62 1 15Jul20:19:45:32 15Jul20:19:45:32 0 1 15Jul20:21:31:33 15Jul20:21:31:37 4 1 15Jul20:22:50:46 15Jul20:22:51:41 55 1 15Jul20:22:51:44 15Jul20:22:52:06 22 1 15Jul20:23:25:45 15Jul20:23:27:33 108 1 16Jul20:22:00:07 16Jul20:22:02:11 124 1 16Jul20:22:02:17 16Jul20:22:03:19 62 1 16Jul20:22:03:23 16Jul20:22:03:32 9 1 16Jul20:22:03:35 16Jul20:22:03:38 3 1 16Jul20:22:03:43 16Jul20:22:03:48 5 1 16Jul20:22:03:53 16Jul20:22:33:24 1771 1 16Jul20:22:33:24 16Jul20:22:38:59 335 1 16Jul20:22:38:59 16Jul20:22:39:30 31 1 16Jul20:22:39:35 16Jul20:22:40:38 63 1 16Jul20:23:39:08 16Jul20:23:39:44 36 1 16Jul20:23:40:24 16Jul20:23:47:26 422 1 16Jul20:23:47:26 16Jul20:23:58:03 637 1 17Jul20:23:20:34 17Jul20:23:31:14 640 1 17Jul20:23:31:14 17Jul20:23:31:19 5 1 17Jul20:23:31:25 17Jul20:23:34:11 166 1 17Jul20:23:34:15 17Jul20:23:34:33 18 1 17Jul20:23:34:33 17Jul20:23:34:34 1 1 17Jul20:23:34:34 17Jul20:23:35:01 27 1 17Jul20:23:35:04 17Jul20:23:41:41 397 1 17Jul20:23:41:41 17Jul20:23:42:23 42 1 17Jul20:23:42:42 17Jul20:23:42:43 1 1 17Jul20:23:42:43 17Jul20:23:46:38 235 1 17Jul20:23:46:38 17Jul20:23:46:41 3 1 17Jul20:23:46:41 17Jul20:23:47:08 27 1 17Jul20:23:47:08 17Jul20:23:57:19 611 1 17Jul20:23:57:19 17Jul20:23:57:20 1 1 17Jul20:23:58:24 18Jul20:00:47:45 2961 1 18Jul20:19:09:16 18Jul20:19:13:38 262 1 18Jul20:19:48:19 18Jul20:19:52:05 226 1 18Jul20:19:52:05 18Jul20:19:54:23 138 1 18Jul20:19:52:36 18Jul20:19:52:36 0 1 18Jul20:19:52:36 18Jul20:20:04:02 686 1 18Jul20:20:04:02 18Jul20:20:20:53 1011 1 18Jul20:20:20:53 18Jul20:20:34:00 787 1 18Jul20:22:29:25 18Jul20:22:30:00 35 1 18Jul20:22:30:00 18Jul20:22:30:06 6 1 18Jul20:23:12:58 18Jul20:23:12:59 1 1 18Jul20:23:12:59 18Jul20:23:13:04 5 1 18Jul20:23:13:11 18Jul20:23:15:27 136 1 18Jul20:23:15:27 18Jul20:23:15:46 19 1 18Jul20:23:15:59 18Jul20:23:16:08 9 1 18Jul20:23:16:08 18Jul20:23:22:01 353 1 18Jul20:23:22:01 18Jul20:23:23:23 82 1 18Jul20:23:23:23 18Jul20:23:25:24 121 1 18Jul20:23:40:30 18Jul20:23:40:47 17 1 18Jul20:23:40:47 18Jul20:23:48:52 485 1 19Jul20:18:12:03 19Jul20:18:12:03 0 1 19Jul20:18:12:09 19Jul20:18:12:13 4 1 19Jul20:18:12:24 19Jul20:18:13:15 51 1 19Jul20:19:16:24 19Jul20:19:16:34 10 1 19Jul20:19:16:34 19Jul20:19:16:34 0 1 19Jul20:19:16:37 19Jul20:19:16:39 2 1 19Jul20:19:16:41 19Jul20:19:16:41 0 1 19Jul20:19:16:41 19Jul20:19:16:58 17 1 19Jul20:21:19:30 19Jul20:21:19:30 0 1 19Jul20:21:19:30 19Jul20:21:19:32 2 1 19Jul20:21:19:38 19Jul20:21:20:52 74 1 19Jul20:22:15:25 19Jul20:22:40:01 1476 1 19Jul20:23:22:05 19Jul20:23:22:32 27 1 19Jul20:23:22:32 19Jul20:23:22:54 22 2 10Jun20:20:29:43 10Jun20:20:32:50 187 2 10Jun20:20:32:50 10Jun20:20:33:05 15 2 10Jun20:20:33:05 10Jun20:20:35:48 163 2 10Jun20:20:38:00 10Jun20:20:40:48 168 2 10Jun20:20:40:48 10Jun20:20:40:51 3 2 10Jun20:20:40:51 10Jun20:20:40:56 5 2 10Jun20:20:41:09 10Jun20:20:43:24 135 2 10Jun20:20:43:24 10Jun20:20:44:12 48 2 10Jun20:20:44:15 10Jun20:20:52:23 488 2 10Jun20:20:52:23 10Jun20:20:55:07 164 2 10Jun20:20:55:07 10Jun20:21:06:00 653 2 10Jun20:21:29:41 10Jun20:21:50:23 1242 2 10Jun20:21:50:23 10Jun20:21:50:24 1 2 10Jun20:21:50:30 10Jun20:21:51:28 58 2 10Jun20:21:51:28 10Jun20:21:53:40 132 2 11Jun20:14:41:40 11Jun20:14:42:40 1 2 11Jun20:23:31:08 11Jun20:23:34:25 197 2 11Jun20:23:34:25 11Jun20:23:36:01 96 2 11Jun20:23:36:01 11Jun20:23:36:03 2 2 11Jun20:23:36:05 11Jun20:23:39:43 218 2 11Jun20:23:39:43 11Jun20:23:41:51 128 2 11Jun20:23:41:51 11Jun20:23:42:15 24 2 11Jun20:23:42:23 11Jun20:23:44:44 141 2 11Jun20:23:44:44 11Jun20:23:47:03 139 2 11Jun20:23:47:03 11Jun20:23:47:04 1 2 11Jun20:23:48:00 11Jun20:23:48:28 28 2 11Jun20:23:48:28 11Jun20:23:48:55 27 2 11Jun20:23:48:55 11Jun20:23:53:05 250 2 11Jun20:23:53:05 11Jun20:23:58:36 331 2 11Jun20:23:58:36 12Jun20:00:04:12 336 2 12Jun20:21:52:43 12Jun20:21:58:06 323 2 12Jun20:21:58:06 12Jun20:22:00:48 162 2 12Jun20:22:00:48 12Jun20:22:01:11 23 2 12Jun20:22:01:11 12Jun20:22:03:08 117 2 12Jun20:22:04:51 12Jun20:22:12:45 474 2 12Jun20:22:12:45 12Jun20:22:13:38 53 2 12Jun20:22:13:59 12Jun20:22:14:11 12 2 12Jun20:22:14:11 12Jun20:22:23:22 551 2 12Jun20:22:23:22 12Jun20:22:23:59 37 2 12Jun20:22:23:59 12Jun20:22:24:28 29 2 12Jun20:22:24:28 12Jun20:22:25:41 73 2 12Jun20:22:25:41 12Jun20:22:56:15 1834 2 12Jun20:22:56:15 12Jun20:22:56:23 8 2 12Jun20:22:56:23 12Jun20:23:27:15 1852 2 12Jun20:23:27:15 12Jun20:23:29:08 113 2 12Jun20:23:29:08 12Jun20:23:44:16 908 2 12Jun20:23:44:16 12Jun20:23:45:58 102 2 12Jun20:23:46:55 13Jun20:00:20:55 2040 2 13Jun20:21:59:55 13Jun20:21:59:55 0 2 13Jun20:22:00:00 13Jun20:22:00:01 1 2 13Jun20:22:00:01 13Jun20:22:00:09 8 2 13Jun20:22:00:11 13Jun20:22:00:14 3 2 13Jun20:22:00:14 13Jun20:22:00:26 12 2 13Jun20:22:00:35 13Jun20:22:00:36 1 2 13Jun20:22:00:36 13Jun20:22:00:51 15 2 13Jun20:22:00:51 13Jun20:22:00:56 5 2 13Jun20:22:00:56 13Jun20:22:00:57 1 2 13Jun20:22:00:57 13Jun20:22:13:35 758 2 13Jun20:22:13:35 13Jun20:22:13:38 3 2 13Jun20:22:13:38 13Jun20:22:13:49 11 2 13Jun20:22:13:49 13Jun20:22:15:42 113 2 13Jun20:22:15:42 13Jun20:22:15:54 12 2 13Jun20:22:15:54 13Jun20:22:34:14 1100 2 13Jun20:22:34:33 13Jun20:22:38:58 265 2 13Jun20:22:39:37 13Jun20:22:39:44 7 2 13Jun20:22:40:26 13Jun20:22:54:30 844 2 13Jun20:22:54:30 13Jun20:22:54:34 4 2 13Jun20:22:54:34 13Jun20:23:34:57 2423 2 14Jun20:00:04:17 14Jun20:00:08:24 247 2 14Jun20:21:10:26 14Jun20:21:11:37 71 2 14Jun20:21:11:48 14Jun20:21:15:17 209 2 14Jun20:21:15:17 14Jun20:21:33:48 1111 2 14Jun20:21:33:48 14Jun20:21:45:01 673 2 14Jun20:21:45:01 14Jun20:21:45:44 43 2 14Jun20:21:45:44 14Jun20:21:47:04 80 2 14Jun20:21:47:04 14Jun20:22:08:04 1260 2 14Jun20:22:08:04 14Jun20:22:10:27 143 2 14Jun20:22:10:27 14Jun20:22:23:59 812 2 14Jun20:22:23:59 14Jun20:22:39:50 951 2 14Jun20:22:42:20 14Jun20:22:59:23 1023 2 14Jun20:23:00:06 14Jun20:23:34:47 2081 2 14Jun20:23:34:50 14Jun20:23:49:57 907 2 14Jun20:23:49:57 14Jun20:23:59:16 559 2 14Jun20:23:59:16 15Jun20:00:18:12 1136 2 15Jun20:23:03:40 15Jun20:23:03:59 19 2 15Jun20:23:03:59 15Jun20:23:04:14 15 2 15Jun20:23:04:14 15Jun20:23:09:14 300 2 15Jun20:23:09:14 15Jun20:23:10:21 67 2 15Jun20:23:11:21 15Jun20:23:14:50 209 2 15Jun20:23:14:50 15Jun20:23:18:04 194 2 15Jun20:23:18:04 15Jun20:23:18:11 7 2 15Jun20:23:18:11 15Jun20:23:23:00 289 2 15Jun20:23:23:00 15Jun20:23:32:54 594 2 15Jun20:23:32:54 15Jun20:23:33:24 30 2 15Jun20:23:33:24 15Jun20:23:35:49 145 2 15Jun20:23:35:49 15Jun20:23:37:27 98 2 15Jun20:23:38:34 15Jun20:23:40:16 102 2 15Jun20:23:40:16 15Jun20:23:44:19 243 2 15Jun20:23:44:19 15Jun20:23:44:23 4 2 15Jun20:23:54:59 15Jun20:23:55:03 4 2 15Jun20:23:55:32 16Jun20:00:13:29 1077 Run; I want to calculate the number of days the program was installed per person. Normally I would do proc means by Ppt_ID , and calculate days_installed as maximum End datetime per person - minimum Start datetime per person. However, there are times when the person uninstalled the program and re-installed the program several days later (as can be seen in the gap in datetimes between rows 33 and 34). The calculation I proposed would not account for the large gaps where the program was not in use. What I would like to do is the following:   1) Where a person has continuous program usage (e.g., Ppt_ID #2), meaning that each Start datetime starts <1  day from the previous row's End datetime, calculate the total days of program installation (per person) using the method I described above: last End datetime - first Start datetime for that person. 2) Where a person has >=1 days between an End datetime and the next row's Start datetime (e.g., Ppt_ID #1), minus that gap in days from the total amount of days installed. In other words, per person, add the time for each set of rows that are <1 day from each other, and then add the amount of time for the next set of days that are <1 day from each other.   An example would be for Ppt_ID #1, the first "chunk" of time would be 26MAY20:17:00:47 ( Start , row 1) to 28MAY20:16:27:30 ( End , row 33); and the second "chunk" of time would be 13JUL20:21:30:23 ( Start , row 34) to 19JUL20:23:22:54 ( End , row 134). The total amount of time for this person would be ((28MAY20:16:27:30 - 26MAY20:17:00:47) + (19JUL20:23:22:54 - 13JUL20:21:30:23)) / 86400, or 8.055023148 days for  Ppt_ID #1. For ID 2, there are no gaps >=1 day, so the number of days would just be (16JUN20:00:13:29 ( End , row 235) - 10JUN20:20:29:43 ( Start , row 135)) / 86400, or 5.155393519 days for  Ppt_ID #2.   Could someone assist? Thanks. ... View more

Hello, I've created two graphs from my data, one an overlaid histogram (HISTOGRAM statement) and one a stacked histogram (SGPANEL statement). Here is my code:   proc sgplot data=Surveys;styleattrs datacolors=(blue CXFF00FF); where gender~='';styleattrs backcolor=white wallcolor=white; format gender $gender.;histogram Weeks / group=gender transparency=0.5; xaxis label='Weeks' LABELATTRS=(size=14) VALUEATTRS=(size=13); keylegend / title='Gender' TITLEATTRS=(size=16) valueattrs=(size=12); yaxis LABELATTRS=(size=14) VALUEATTRS=(size=13) grid GRIDATTRS=(color=LIGGR);run; proc sgpanel data=Surveys; styleattrs datacolors=(blue CXFF00FF) datacontrastcolors=(blue CXFF00FF); where gender~=''; styleattrs backcolor=white wallcolor=white; format gender $gender.;panelby gender / rows=2 layout=rowlattice; histogram Weeks; COLAXIS label='Weeks' LABELATTRS=(size=14) VALUEATTRS=(size=13); rowaxis LABELATTRS=(size=14) VALUEATTRS=(size=13) grid GRIDATTRS=(color=LIGGR); keylegend / title='Gender' TITLEATTRS=(size=16) valueattrs=(size=12); run; Here are the graphs that come out:     The overlaid histogram (first graph) comes out the way I need with regard to the colors of the bars for each group (blue for male and a purplish color, CXFF00FF, for females). However, I can't figure out how to color the SGPANEL(second graph) the same way, despite using " styleattrs datacolors=(blue CXFF00FF) datacontrastcolors=(blue CXFF00FF) ". I've Googled around and can't seem to find a solution. Can you help? Thanks. ... View more