Setup observation model and data
Usage
enw_obs(family = c("negbin", "poisson"), 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.- 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
#>
#> $data$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
#>
#> $data$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
#> 1: sqrt_phi One over the square root of the reporting overdispersion
#> distribution mean sd
#> 1: Zero truncated normal 0 1
#>
#> $inits
#> function (data, priors)
#> {
#> priors <- enw_priors_as_data_list(priors)
#> fn <- function() {
#> init <- list(sqrt_phi = numeric(0), phi = numeric(0))
#> if (data$model_obs == 1) {
#> init$sqrt_phi <- array(max(abs(rnorm(1, priors$sqrt_phi_p[1],
#> priors$sqrt_phi_p[2]/10)), 1e-04))
#> init$phi <- 1/(init$sqrt_phi^2)
#> }
#> return(init)
#> }
#> return(fn)
#> }
#> <bytecode: 0x565393585dc8>
#> <environment: 0x565392a39d18>
#>