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# _____________________________________________________________________________
# Implementation of the full conditionals for parameters N, p and f.
# _____________________________________________________________________________
# To calculate the summation in the probability distribution
summation_term <- function(N, f, p, propotns, x) {
j <- 0:min(c(x, N))
ind <- lgamma(N + x - j + 1) - lgamma(N - j + 1) - lgamma(j + 1) -
lgamma(x - j + 1) + j * (log(p + (1 - p) * f * propotns) -
log(1 - p) - log(1 - f * propotns)) - log(N + 0.005 + x - j)
u <- max(ind)
z <- sum(exp(ind - u))
return(u + log(z))
}
# To calculate the full conditional for N.
fullcond_N <- function(N, f, p, propotns, x) {
# summation
b <- mapply(summation_term, x = x, propotns = propotns, MoreArgs = list(N =
N, p = p, f = f))
# multiplying by the prior
fc <- sum(log(N) + N * (log(p) + log(1 - propotns * f) - log(p + (1 - p) *
propotns * f))) + sum(b)
return(fc)
}
# To calculate the full conditional for p.
fullcond_p <- function(N, f, p, propotns, x) {
# summation
b <- mapply(summation_term, x = x, propotns = propotns, N = N, MoreArgs =
list(p = p, f = f))
# multiplying by the prior alpha0 <- 1 beta0 <- 1 (alpha0 - 1) * log(p) + (beta0 - 1) *log(1 - p) +
fc <- sum(x * log(1 - p) + N * log(p) - (N + x) * (log(p + (1 - p) *
propotns * f))) + sum(b)
return(fc)
}
# To calculate the full conditional for f.
fullcond_f <- function(N, f, p, propotns, x) {
# summation
b <- mapply(summation_term, x = x, propotns = propotns, N = N, MoreArgs =
list(p = p, f = f))
# multiplying by the prior theta0 <- 1 gamma0 <- 1 (theta0 - 1) * log(f) + (gamma0 - 1) *log(1 - f) +
fc <- sum(x * log(f) + N * log(1 - propotns * f) - (N + x) * (log(p +
(1 - p) * propotns * f))) + sum(b)
return(fc)
}
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