Nothing
predictMCMC.svabu <-
function(object, se.fit = FALSE, n.iter = 1000,
raw = FALSE, vcov.type, ...)
{
require(dclone)
predfun <- c("model {",
" for (i in 1:n) {",
" N[i] ~ dpois(lambda[i])",
" Y[i] ~ dbin(p[i], N[i])",
" }",
" tmp[1:(np1+np2)] ~ dmnorm(param[], prec[,])",
" beta[1:np1] <- tmp[1:np1]",
" theta[(np1+1):(np1+np2)] <- tmp[(np1+1):(np1+np2)]",
"}")
class(predfun) <- "custommodel"
## only sta and det not zif: if present only
nps <- sapply(object$coefficients, length)
np <- sum(nps[1:2])
vcv <- vcov(object, model="full", vcov.type)[1:np, 1:np]
prec <- make.symmetric(solve(vcv))
param <- coef(object)[1:np]
inits <- list(N = object$y + 1)
prdat <- list(Y=object$y, n=length(object$y),
lambda=fitted(object), p=object$detection.probabilities,
np1=nps[1], np2=nps[2],
param = param, prec = prec)
prval <- jags.fit(prdat, "N", predfun, inits,
n.chains=1, n.iter=n.iter, ...)
rval <- if (se.fit) {
list(fit = coef(prval), se.fit = dcsd(prval))
} else coef(prval)
if (raw)
return(prval) else return(rval)
}
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