Setup observation model and data
Usage
enw_obs(family = c("negbin", "poisson"), observation_indicator = NULL, data)Arguments
- family
Character string, the observation model to use in the likelihood; enforced by
base::match.arg(). By default this is a negative binomial ("negbin") with Poisson ("poisson") also being available. Support for additional observation models is planned, please open an issue with suggestions.- observation_indicator
A character string, the name of the column in the data that indicates whether an observation is observed or not (using a logical variable) and therefore whether or not it should be used in the likelihood. This variable should be present in the data input to
enw_preprocess_data(). It can be generated usingflag_observationinenw_complete_dates()or it can be created directly usingenw_flag_observed_observations(). If either of these approaches are used then the variable will be name.observed. Default isNULL.- data
Output from
enw_preprocess_data().
See also
Model modules
enw_expectation(),
enw_fit_opts(),
enw_missing(),
enw_reference(),
enw_report()
Examples
enw_obs(data = enw_example("preprocessed"))
#> $family
#> [1] "negbin"
#>
#> $data
#> $data$n
#> [1] 630
#>
#> $data$t
#> [1] 41
#>
#> $data$s
#> [1] 41
#>
#> $data$g
#> [1] 1
#>
#> $data$groups
#> [1] 1
#>
#> $data$st
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
#> [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
#>
#> $data$ts
#> 1
#> [1,] 1
#> [2,] 2
#> [3,] 3
#> [4,] 4
#> [5,] 5
#> [6,] 6
#> [7,] 7
#> [8,] 8
#> [9,] 9
#> [10,] 10
#> [11,] 11
#> [12,] 12
#> [13,] 13
#> [14,] 14
#> [15,] 15
#> [16,] 16
#> [17,] 17
#> [18,] 18
#> [19,] 19
#> [20,] 20
#> [21,] 21
#> [22,] 22
#> [23,] 23
#> [24,] 24
#> [25,] 25
#> [26,] 26
#> [27,] 27
#> [28,] 28
#> [29,] 29
#> [30,] 30
#> [31,] 31
#> [32,] 32
#> [33,] 33
#> [34,] 34
#> [35,] 35
#> [36,] 36
#> [37,] 37
#> [38,] 38
#> [39,] 39
#> [40,] 40
#> [41,] 41
#>
#> $data$sl
#> [1] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 19 18 17
#> [26] 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
#>
#> $data$csl
#> [1] 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
#> [20] 400 420 440 459 477 494 510 525 539 552 564 575 585 594 602 609 615 620 624
#> [39] 627 629 630
#>
#> $data$lsl
#> [1] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 19 18 17
#> [26] 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
#>
#> $data$clsl
#> [1] 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
#> [20] 400 420 440 459 477 494 510 525 539 552 564 575 585 594 602 609 615 620 624
#> [39] 627 629 630
#>
#> $data$nsl
#> [1] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 19 18 17
#> [26] 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
#>
#> $data$cnsl
#> [1] 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
#> [20] 400 420 440 459 477 494 510 525 539 552 564 575 585 594 602 609 615 620 624
#> [39] 627 629 630
#>
#> $data$sg
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [39] 1 1 1
#>
#> $data$dmax
#> [1] 20
#>
#> $data$sdmax
#> [1] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
#> [26] 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
#>
#> $data$csdmax
#> [1] 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380
#> [20] 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700 720 740 760
#> [39] 780 800 820
#>
#> $data$obs
#> 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
#> [1,] 21 12 3 4 3 0 1 2 4 3 0 0 2 0 0 0 1 2 1 0
#> [2,] 22 12 4 5 0 1 10 2 5 3 0 1 0 3 1 0 1 0 0 0
#> [3,] 28 15 3 3 0 1 3 2 3 2 1 0 2 3 0 3 0 0 0 0
#> [4,] 19 13 0 0 0 4 2 2 2 1 1 1 0 1 0 0 1 0 0 0
#> [5,] 20 7 1 3 10 3 0 4 3 3 2 0 1 2 0 1 2 1 0 0
#> [6,] 9 6 6 0 4 5 4 0 1 4 0 0 1 1 1 2 0 0 0 2
#> [7,] 3 16 4 4 1 1 2 0 0 0 0 1 1 1 1 0 0 0 1 0
#> [8,] 36 19 10 4 2 3 0 3 2 3 0 2 1 1 2 2 0 0 1 0
#> [9,] 28 18 8 4 1 2 3 6 1 5 2 2 3 0 2 0 1 0 0 0
#> [10,] 34 19 2 1 5 2 4 3 7 3 1 0 4 3 3 1 2 0 0 1
#> [11,] 30 12 4 1 10 6 0 2 2 1 2 1 4 0 2 3 0 0 4 0
#> [12,] 31 8 4 9 8 2 5 2 1 1 2 4 1 3 1 0 1 2 2 2
#> [13,] 8 4 14 8 6 5 1 3 0 4 1 2 4 2 0 1 2 2 2 0
#> [14,] 9 6 2 3 0 0 0 0 1 2 4 1 0 0 0 0 0 0 0 0
#> [15,] 35 11 6 4 4 1 0 2 2 2 2 0 0 1 4 1 0 0 0 0
#> [16,] 51 28 25 3 5 2 3 5 5 7 1 0 0 4 5 5 1 1 0 0
#> [17,] 47 37 9 2 2 3 4 4 4 3 0 2 0 10 4 3 0 0 0 0
#> [18,] 36 20 2 4 11 8 8 3 5 2 0 2 4 4 0 2 2 0 0 1
#> [19,] 38 16 3 15 14 7 5 5 0 0 5 0 5 1 6 0 0 3 1 0
#> [20,] 7 5 5 11 7 5 1 3 1 6 3 3 4 1 1 7 2 3 2 0
#> [21,] 13 13 8 6 1 3 2 0 0 2 0 2 0 5 3 0 0 0 0 1
#> [22,] 51 43 6 4 4 3 1 6 4 5 5 4 0 4 5 0 2 2 0 0
#> [23,] 51 43 18 5 6 1 2 8 7 7 6 1 0 4 3 1 3 0 0 0
#> [24,] 45 21 6 2 2 11 17 5 7 4 1 0 5 3 0 2 2 0 0 0
#> [25,] 47 31 5 4 20 6 1 9 3 1 0 2 1 5 2 0 0 0 0 0
#> [26,] 40 15 6 23 14 13 8 9 0 1 3 3 2 0 1 1 0 0 0 0
#> [27,] 13 14 27 14 7 7 0 0 0 7 1 4 2 1 0 0 0 0 0 0
#> [28,] 14 23 11 3 1 1 0 0 0 1 0 2 2 0 0 0 0 0 0 0
#> [29,] 78 43 23 11 5 1 0 5 2 2 1 4 0 0 0 0 0 0 0 0
#> [30,] 80 53 17 15 7 3 14 12 13 13 6 0 0 0 0 0 0 0 0 0
#> [31,] 89 48 28 8 1 14 13 13 10 12 1 0 0 0 0 0 0 0 0 0
#> [32,] 86 44 9 3 27 13 7 11 4 0 0 0 0 0 0 0 0 0 0 0
#> [33,] 79 36 7 16 19 13 8 8 3 0 0 0 0 0 0 0 0 0 0 0
#> [34,] 22 24 35 18 10 4 7 5 0 0 0 0 0 0 0 0 0 0 0 0
#> [35,] 23 32 22 10 8 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [36,] 96 85 30 18 10 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [37,] 92 86 23 18 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [38,] 84 87 27 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [39,] 98 61 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [40,] 69 43 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [41,] 45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $data$flat_obs
#> [1] 21 12 3 4 3 0 1 2 4 3 0 0 2 0 0 0 1 2 1 0 22 12 4 5 0
#> [26] 1 10 2 5 3 0 1 0 3 1 0 1 0 0 0 28 15 3 3 0 1 3 2 3 2
#> [51] 1 0 2 3 0 3 0 0 0 0 19 13 0 0 0 4 2 2 2 1 1 1 0 1 0
#> [76] 0 1 0 0 0 20 7 1 3 10 3 0 4 3 3 2 0 1 2 0 1 2 1 0 0
#> [101] 9 6 6 0 4 5 4 0 1 4 0 0 1 1 1 2 0 0 0 2 3 16 4 4 1
#> [126] 1 2 0 0 0 0 1 1 1 1 0 0 0 1 0 36 19 10 4 2 3 0 3 2 3
#> [151] 0 2 1 1 2 2 0 0 1 0 28 18 8 4 1 2 3 6 1 5 2 2 3 0 2
#> [176] 0 1 0 0 0 34 19 2 1 5 2 4 3 7 3 1 0 4 3 3 1 2 0 0 1
#> [201] 30 12 4 1 10 6 0 2 2 1 2 1 4 0 2 3 0 0 4 0 31 8 4 9 8
#> [226] 2 5 2 1 1 2 4 1 3 1 0 1 2 2 2 8 4 14 8 6 5 1 3 0 4
#> [251] 1 2 4 2 0 1 2 2 2 0 9 6 2 3 0 0 0 0 1 2 4 1 0 0 0
#> [276] 0 0 0 0 0 35 11 6 4 4 1 0 2 2 2 2 0 0 1 4 1 0 0 0 0
#> [301] 51 28 25 3 5 2 3 5 5 7 1 0 0 4 5 5 1 1 0 0 47 37 9 2 2
#> [326] 3 4 4 4 3 0 2 0 10 4 3 0 0 0 0 36 20 2 4 11 8 8 3 5 2
#> [351] 0 2 4 4 0 2 2 0 0 1 38 16 3 15 14 7 5 5 0 0 5 0 5 1 6
#> [376] 0 0 3 1 0 7 5 5 11 7 5 1 3 1 6 3 3 4 1 1 7 2 3 2 0
#> [401] 13 13 8 6 1 3 2 0 0 2 0 2 0 5 3 0 0 0 0 1 51 43 6 4 4
#> [426] 3 1 6 4 5 5 4 0 4 5 0 2 2 0 0 51 43 18 5 6 1 2 8 7 7
#> [451] 6 1 0 4 3 1 3 0 0 45 21 6 2 2 11 17 5 7 4 1 0 5 3 0 2
#> [476] 2 0 47 31 5 4 20 6 1 9 3 1 0 2 1 5 2 0 0 40 15 6 23 14 13
#> [501] 8 9 0 1 3 3 2 0 1 1 13 14 27 14 7 7 0 0 0 7 1 4 2 1 0
#> [526] 14 23 11 3 1 1 0 0 0 1 0 2 2 0 78 43 23 11 5 1 0 5 2 2 1
#> [551] 4 0 80 53 17 15 7 3 14 12 13 13 6 0 89 48 28 8 1 14 13 13 10 12 1
#> [576] 86 44 9 3 27 13 7 11 4 0 79 36 7 16 19 13 8 8 3 22 24 35 18 10 4
#> [601] 7 5 23 32 22 10 8 2 1 96 85 30 18 10 3 92 86 23 18 4 84 87 27 4 98
#> [626] 61 12 69 43 45
#>
#> $data$flat_obs_lookup
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
#> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
#> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
#> [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
#> [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
#> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
#> [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
#> [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
#> [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
#> [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
#> [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
#> [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
#> [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
#> [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
#> [289] 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306
#> [307] 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324
#> [325] 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342
#> [343] 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360
#> [361] 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378
#> [379] 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396
#> [397] 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414
#> [415] 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
#> [433] 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450
#> [451] 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468
#> [469] 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486
#> [487] 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504
#> [505] 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522
#> [523] 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540
#> [541] 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
#> [559] 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
#> [577] 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594
#> [595] 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612
#> [613] 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630
#>
#> $data$latest_obs
#> 1
#> [1,] 59
#> [2,] 70
#> [3,] 69
#> [4,] 47
#> [5,] 63
#> [6,] 46
#> [7,] 36
#> [8,] 91
#> [9,] 86
#> [10,] 95
#> [11,] 84
#> [12,] 89
#> [13,] 69
#> [14,] 28
#> [15,] 75
#> [16,] 151
#> [17,] 134
#> [18,] 114
#> [19,] 124
#> [20,] 77
#> [21,] 59
#> [22,] 149
#> [23,] 166
#> [24,] 133
#> [25,] 137
#> [26,] 139
#> [27,] 97
#> [28,] 58
#> [29,] 175
#> [30,] 233
#> [31,] 237
#> [32,] 204
#> [33,] 189
#> [34,] 125
#> [35,] 98
#> [36,] 242
#> [37,] 223
#> [38,] 202
#> [39,] 171
#> [40,] 112
#> [41,] 45
#>
#> $data$model_obs
#> [1] 1
#>
#>
#> $priors
#> variable description
#> <char> <char>
#> 1: sqrt_phi One over the square root of the reporting overdispersion
#> distribution mean sd
#> <char> <num> <num>
#> 1: Zero truncated normal 0 0.5
#>
#> $inits
#> function (data, priors)
#> {
#> priors <- enw_priors_as_data_list(priors)
#> fn <- function() {
#> init <- list(sqrt_phi = numeric(0), phi = numeric(0))
#> if (data$model_obs == 1) {
#> init$sqrt_phi <- array(abs(rnorm(1, priors$sqrt_phi_p[1],
#> priors$sqrt_phi_p[2]/10)))
#> init$phi <- 1/(init$sqrt_phi^2)
#> }
#> return(init)
#> }
#> return(fn)
#> }
#> <bytecode: 0x55eda0f65110>
#> <environment: 0x55eda0f69d18>
#>