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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().

Value

A list as required by stan.

See also

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: 0x5592747c6e90>
#> <environment: 0x5592747cbc20>
#>