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model_code.R
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model_code.R
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# model_code.R
# Author: Benjamin R. Goldstein
# Date: 2/23/2022
# This script contains NIMBLE code specifying four N-mixture models,
# representing the four considered (B-P, B-NB, BB-P, BB-NB).
# Only the first model is commented thoroughly.
BP_nmix_code_ebd <- nimbleCode({
# Logit-linked observation probabilities for each observation.
# Observations are provided as a vector, with a second vector indicating
# where the obs for each site begin and end
for (o in 1:nobs) {
logit(p[o]) <- p_coeffs[1] +
sum(p_coeffs[2:npcov] * p_dat[o, 1:(npcov-1)])
}
# Loop over sites with at least one observation
for (s in 1:(nsite - nSite1Obs)) {
# Log-linked mean abundance
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
# Data are distributed according to the custom distribution
y[index_start[s]:index_end[s]] ~
dNmixture_v(lambda = lambda[s],
prob = p[index_start[s]:index_end[s]],
Nmin = 0,
Nmax = K,
len = length(index_start[s]:index_end[s]))
}
# Sites with only one observation are processed separately
for (s in (nsite - nSite1Obs + 1):nsite) {
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
y[index_start[s]] ~ dpois(lambda = lambda[s] * p[index_start[s]])
}
})
BNB_nmix_code_ebd <- nimbleCode({
for (o in 1:nobs) {
logit(p[o]) <- p_coeffs[1] +
sum(p_coeffs[2:npcov] * p_dat[o, 1:(npcov-1)])
}
theta <- exp(ltheta)
for (s in 1:(nsite - nSite1Obs)) {
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
y[index_start[s]:index_end[s]] ~
dNmixture_BNB_v(lambda = lambda[s],
theta = theta,
prob = p[index_start[s]:index_end[s]],
Nmin = 0,
Nmax = K,
len = length(index_start[s]:index_end[s]))
}
for (s in (nsite - nSite1Obs + 1):nsite) {
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
y[index_start[s]] ~ dNmixture_BNB_oneObs(lambda = lambda[s],
prob = p[index_start[s]],
theta = theta,
Nmin = 0,
Nmax = K,
len = 1)
}
})
BBP_nmix_code_ebd <- nimbleCode({
for (o in 1:nobs) {
logit(p[o]) <- p_coeffs[1] +
sum(p_coeffs[2:npcov] * p_dat[o, 1:(npcov-1)])
}
s_bb <- exp(log_s)
for (s in 1:(nsite - nSite1Obs)) {
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
y[index_start[s]:index_end[s]] ~
dNmixture_BBP_v(lambda = lambda[s],
prob = p[index_start[s]:index_end[s]],
s = s_bb,
Nmin = 0,
Nmax = K,
len = length(index_start[s]:index_end[s]))
}
for (s in (nsite - nSite1Obs + 1):nsite) {
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
y[index_start[s]] ~ dNmixture_BBP_oneObs(lambda = lambda[s],
prob = p[index_start[s]],
s = s_bb,
Nmin = 0,
Nmax = K,
len = 1)
}
})
BBNB_nmix_code_ebd <- nimbleCode({
for (o in 1:nobs) {
logit(p[o]) <- p_coeffs[1] +
sum(p_coeffs[2:npcov] * p_dat[o, 1:(npcov-1)])
}
s_bb <- exp(log_s)
theta <- exp(ltheta)
for (s in 1:(nsite - nSite1Obs)) {
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
y[index_start[s]:index_end[s]] ~
dNmixture_BBNB_v(lambda = lambda[s],
theta = theta,
prob = p[index_start[s]:index_end[s]],
s = s_bb,
Nmin = 0,
Nmax = K,
len = length(index_start[s]:index_end[s]))
}
for (s in (nsite - nSite1Obs + 1):nsite) {
log(lambda[s]) <-
abund_coeffs[1] + # intercept
sum(abund_coeffs[2:nlcov] * # data columns
abund_dat[index_start[s], 1:(nlcov-1)])
y[index_start[s]] ~ dNmixture_BBNB_oneObs(lambda = lambda[s],
theta = theta,
prob = p[index_start[s]],
s = s_bb,
Nmin = 0,
Nmax = K,
len = 1)
}
})