# 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))
#Draw from the posterior predictive distribution
(ppd <- posteriorSamples(re, nsims=100))
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