posteriorSamples.Rd
Draw samples from the empirical Bayes posterior predictive distribution derived from unmarked models or ranef objects
# S4 method for class 'unmarkedRanef'
posteriorSamples(object, nsims=100, ...)
# S4 method for class 'unmarkedFit'
posteriorSamples(object, nsims=100, ...)
unmarkedPostSamples
object containing the draws from the
posterior predictive distribution. The draws are in the @samples
slot.
# Simulate data under N-mixture model
set.seed(4564)
R <- 20
J <- 5
N <- rpois(R, 10)
y <- matrix(NA, R, J)
y[] <- rbinom(R*J, N, 0.5)
# Fit model
umf <- unmarkedFramePCount(y=y)
fm <- pcount(~1 ~1, umf, K=50)
# Estimates of conditional abundance distribution at each site
(re <- ranef(fm))
#> Mean Mode 2.5% 97.5%
#> [1,] 5.456813 5 4 8
#> [2,] 12.263316 12 10 15
#> [3,] 11.974866 12 10 15
#> [4,] 12.512518 12 10 15
#> [5,] 7.515203 7 6 10
#> [6,] 6.910344 7 5 9
#> [7,] 11.772003 12 10 14
#> [8,] 11.590102 11 9 14
#> [9,] 11.957508 12 10 15
#> [10,] 5.239116 5 4 7
#> [11,] 12.811799 13 10 16
#> [12,] 6.871755 7 5 9
#> [ reached getOption("max.print") -- omitted 8 rows ]
#Draw from the posterior predictive distribution
(ppd <- posteriorSamples(re, nsims=100))
#> , , 1
#>
#> [,1]
#> [1,] 8
#> [2,] 12
#> [3,] 11
#> [4,] 15
#> [5,] 7
#> [6,] 6
#> [7,] 11
#> [8,] 11
#> [9,] 13
#> [10,] 5
#> [11,] 13
#> [12,] 7
#> [13,] 11
#> [14,] 13
#> [15,] 8
#> [16,] 9
#> [17,] 7
#> [18,] 7
#> [19,] 11
#> [20,] 12
#>
#> , , 2
#>
#> [,1]
#> [1,] 4
#> [2,] 10
#> [3,] 12
#> [4,] 11
#> [5,] 9
#> [6,] 5
#> [7,] 10
#> [8,] 10
#> [9,] 11
#> [10,] 6
#> [11,] 11
#> [12,] 7
#> [13,] 10
#> [14,] 14
#> [15,] 4
#> [16,] 10
#> [17,] 9
#> [18,] 7
#> [19,] 11
#> [20,] 12
#>
#> , , 3
#>
#> [,1]
#> [1,] 4
#> [2,] 11
#> [3,] 10
#> [4,] 11
#> [5,] 6
#> [6,] 7
#> [7,] 10
#> [8,] 10
#> [9,] 11
#> [10,] 5
#>
#> [ reached getOption("max.print") -- omitted 10 row(s) and 97 matrix slice(s) ]