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m2_nimble_gen.R
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##########################################
# Model 2: site covariates for capture #
##########################################
# and
# not assuming a rectangular survy design but rather
# using a vector of observations with indexing based on length vector
dM2_nb_vec <- nimbleFunction (
run = function (x = double(1),
mut = double(),
g0 = double(),
g1 = double(),
rt = double(),
J_i = double(1),
R = integer(),
w = double(1),
log = logical(0, default = 0)) {
mu <- exp(mut)
pt <- g0 + g1 * w
p <- expit(pt)
r <- exp(rt)
xtot <- sum(x)
ptot <- double(R)
x_row <- integer(R)
x_miss_row <- integer(R)
for (i in 1:R) {
spots_in <- (sum(J_i[1:i]) - J_i[i] + 1):(sum(J_i[1:i]) - J_i[i] + J_i[i])
ptot[i] <- 1 - (1 - p[i])^J_i[i]
x_row[i] <- sum(x[spots_in])
x_miss_row[i] <- x[spots_in] %*% seq(0, J_i[i] - 1)
}
x_sumj <- sum(x_miss_row[1:R])
x_logfact <- sum(lfactorial(x))
logProb <- (-x_logfact)
for (i in 1:R) {
spots_in <- (sum(J_i[1:i]) - J_i[i] + 1):(sum(J_i[1:i]) - J_i[i] + J_i[i])
x_vec <- seq(0, J_i[i] - 1)
prob <- double(1)
for (j in 1:J_i[i]) {
prob[j] <- pow(1 - p[i], j - 1) * p[i]
}
term1 <- lgamma(r + x_row[i]) - lgamma(r)
term2 <- r * log(r) + x_row[i] * log(mu)
term3 <- sum(x[spots_in] * log(prob))
term4 <- -(x_row[i] + r) * log(r + mu * ptot[i])
logProb <- logProb + term1 + term2 + term3 + term4
}
if (log) return(logProb)
else return(exp(logProb))
returnType(double())
})
rM2_nb_vec <- nimbleFunction(
run = function(n = integer(),
mut = double(),
g0 = double(),
g1 = double(),
rt = double(),
J_i = double(1),
R = integer(),
w = double(1)) {
mu <- exp(mut)
pt <- g0 + g1 * w
p <- expit(pt)
r <- exp(rt)
J_tot <- sum(J_i)
prob <- double(J_tot + 1 * R)
retain <- logical(J_tot + 1 * R)
ans <- integer(J_tot + 1 * R)
for (i in 1:R) {
for (j in 1:J_i[i]) {
prob[sum(J_i[1:i]) - J_i[i] + i - 1 + j] <- pow(1 - p[i], j - 1) * p[i]
retain[sum(J_i[1:i]) - J_i[i] + i - 1 + j] <- TRUE
}
prob[sum(J_i[1:i]) - J_i[i] + i - 1 + j + 1] <- 1 - sum(prob[(sum(J_i[1:i]) - J_i[i] + i - 1 + 1):(sum(J_i[1:i]) - J_i[i] + i - 1 + J_i[i])])
}
n <- integer(R)
for (i in 1:R) {
n[i] <- rnbinom(n = 1, size = r, mu = mu)
if (n[i] > 0) {
ans[(sum(J_i[1:i]) - J_i[i] + i - 1):(sum(J_i[1:i]) - J_i[i] + i - 1 + J_i[i]) + 1] <- rmulti(n = 1, size = n[i], prob = prob[(sum(J_i[1:i]) - J_i[i] + i - 1):(sum(J_i[1:i]) - J_i[i] + i - 1 + J_i[i]) + 1])
}
}
return(ans[retain])
returnType(double(1))
})
registerDistributions(list(
dM2_nb_vec = list(
BUGSdist = "dM2_nb_vec(mut, g0, g1, rt, J_i, R, z)",
Rdist = "dM2_nb_vec(mut, g0, g1, rt, J_i, R, z)",
discrete = TRUE,
types = c('value = double(1)',
'mut = double()',
'g0 = double()',
'g1 = double()',
'rt = double()',
'J_i = double(1)',
'R = integer()',
'z = double(1)'
),
mixedSizes = FALSE,
pqAvail = FALSE
)), verbose = FALSE
)