tests/testthat/_snaps/enw_missing.md

In epiforecasts/epinowcast: Flexible Hierarchical Nowcasting

enw_missing produces the expected model components

Code
  miss <- enw_missing(formula = ~ 1 + rw(week), data = pobs)
  miss$inits <- NULL
  miss
Output
  $formula
  [1] "~1 + rw(week)"

  $data
  $data$miss_fintercept
  [1] 1

  $data$miss_fnrow
  [1] 41

  $data$miss_findex
   [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

  $data$miss_fnindex
  [1] 41

  $data$miss_fncol
  [1] 5

  $data$miss_rncol
  [1] 1

  $data$miss_fdesign
     cweek1 cweek2 cweek3 cweek4 cweek5
  1       0      0      0      0      0
  2       0      0      0      0      0
  3       0      0      0      0      0
  4       0      0      0      0      0
  5       0      0      0      0      0
  6       0      0      0      0      0
  7       0      0      0      0      0
  8       1      0      0      0      0
  9       1      0      0      0      0
  10      1      0      0      0      0
  11      1      0      0      0      0
  12      1      0      0      0      0
  13      1      0      0      0      0
  14      1      0      0      0      0
  15      1      1      0      0      0
  16      1      1      0      0      0
  17      1      1      0      0      0
  18      1      1      0      0      0
  19      1      1      0      0      0
  20      1      1      0      0      0
  21      1      1      0      0      0
  22      1      1      1      0      0
  23      1      1      1      0      0
  24      1      1      1      0      0
  25      1      1      1      0      0
  26      1      1      1      0      0
  27      1      1      1      0      0
  28      1      1      1      0      0
  29      1      1      1      1      0
  30      1      1      1      1      0
  31      1      1      1      1      0
  32      1      1      1      1      0
  33      1      1      1      1      0
  34      1      1      1      1      0
  35      1      1      1      1      0
  36      1      1      1      1      1
  37      1      1      1      1      1
  38      1      1      1      1      1
  39      1      1      1      1      1
  40      1      1      1      1      1
  41      1      1      1      1      1

  $data$miss_rdesign
    fixed rw__week
  1     0        1
  2     0        1
  3     0        1
  4     0        1
  5     0        1
  attr(,"assign")
  [1] 1 2

  $data$miss_st
  [1] 22

  $data$miss_cst
  [1] 22

  $data$missing_reference
   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

  $data$obs_by_report
          0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
   [1,] 381 362 343 324 305 286 267 248 229 210 191 172 153 134 115  96  77  58
   [2,] 401 382 363 344 325 306 287 268 249 230 211 192 173 154 135 116  97  78
   [3,] 421 402 383 364 345 326 307 288 269 250 231 212 193 174 155 136 117  98
   [4,] 441 422 403 384 365 346 327 308 289 270 251 232 213 194 175 156 137 118
   [5,] 461 442 423 404 385 366 347 328 309 290 271 252 233 214 195 176 157 138
   [6,] 481 462 443 424 405 386 367 348 329 310 291 272 253 234 215 196 177 158
   [7,] 501 482 463 444 425 406 387 368 349 330 311 292 273 254 235 216 197 178
   [8,] 521 502 483 464 445 426 407 388 369 350 331 312 293 274 255 236 217 198
   [9,] 541 522 503 484 465 446 427 408 389 370 351 332 313 294 275 256 237 218
  [10,] 561 542 523 504 485 466 447 428 409 390 371 352 333 314 295 276 257 238
  [11,] 581 562 543 524 505 486 467 448 429 410 391 372 353 334 315 296 277 258
  [12,] 601 582 563 544 525 506 487 468 449 430 411 392 373 354 335 316 297 278
  [13,] 621 602 583 564 545 526 507 488 469 450 431 412 393 374 355 336 317 298
  [14,] 641 622 603 584 565 546 527 508 489 470 451 432 413 394 375 356 337 318
  [15,] 661 642 623 604 585 566 547 528 509 490 471 452 433 414 395 376 357 338
  [16,] 681 662 643 624 605 586 567 548 529 510 491 472 453 434 415 396 377 358
  [17,] 701 682 663 644 625 606 587 568 549 530 511 492 473 454 435 416 397 378
  [18,] 721 702 683 664 645 626 607 588 569 550 531 512 493 474 455 436 417 398
  [19,] 741 722 703 684 665 646 627 608 589 570 551 532 513 494 475 456 437 418
  [20,] 761 742 723 704 685 666 647 628 609 590 571 552 533 514 495 476 457 438
  [21,] 781 762 743 724 705 686 667 648 629 610 591 572 553 534 515 496 477 458
  [22,] 801 782 763 744 725 706 687 668 649 630 611 592 573 554 535 516 497 478
         18  19
   [1,]  39  20
   [2,]  59  40
   [3,]  79  60
   [4,]  99  80
   [5,] 119 100
   [6,] 139 120
   [7,] 159 140
   [8,] 179 160
   [9,] 199 180
  [10,] 219 200
  [11,] 239 220
  [12,] 259 240
  [13,] 279 260
  [14,] 299 280
  [15,] 319 300
  [16,] 339 320
  [17,] 359 340
  [18,] 379 360
  [19,] 399 380
  [20,] 419 400
  [21,] 439 420
  [22,] 459 440

  $data$model_miss
  [1] 1

  $data$miss_obs
  [1] 22


  $priors
         variable
  1:     miss_int
  2: miss_beta_sd
                                                                          description
  1:          Intercept on the logit scale for the proportion missing reference dates
  2: Standard deviation of scaled pooled logit missing reference date\n       effects
              distribution mean sd
  1:                Normal    0  1
  2: Zero truncated normal    0  1

enw_missing returns an empty model when required

Code
  miss <- enw_missing(formula = ~0, data = pobs)
  miss$inits <- NULL
  miss
Output
  $formula
  [1] "~0"

  $data
  $data$miss_fintercept
  [1] 0

  $data$miss_fnrow
  [1] 0

  $data$miss_findex
  numeric(0)

  $data$miss_fnindex
  [1] 0

  $data$miss_fncol
  [1] 0

  $data$miss_rncol
  [1] 0

  $data$miss_fdesign
  numeric(0)

  $data$miss_rdesign
  numeric(0)

  $data$missing_reference
  numeric(0)

  $data$obs_by_report
  numeric(0)

  $data$miss_st
  numeric(0)

  $data$miss_cst
  numeric(0)

  $data$model_miss
  [1] 0

  $data$miss_obs
  [1] 0


  $priors
         variable
  1:     miss_int
  2: miss_beta_sd
                                                                          description
  1:          Intercept on the logit scale for the proportion missing reference dates
  2: Standard deviation of scaled pooled logit missing reference date\n       effects
              distribution mean sd
  1:                Normal    0  1
  2: Zero truncated normal    0  1

epiforecasts/epinowcast documentation built on Feb. 3, 2025, 4:17 p.m.