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## how to generate missing values
## this is not predicting !!!
#generateMissing <-
#function(x, ...)
#{
# if (!inherits(x, "pva"))
# stop("'x' must be of class 'pva'")
# i <- which(is.na(x@observations))
# if (!length(i))
# stop("no missing observations")
# obs.error <- x@model@obs.error
# node <- switch(obs.error,
# "none" = "x",
# "normal" = "y",
# "poisson" = "O")
# dcd <- x@dcdata
# dcd@model <- x@model@predmodel
# Params <- paste(node, "[",i,",1]",sep="")
# f <- jags.fit(dcd@data,
# params=Params,
# model=dcd@model, ...)
# pred <- as.matrix(f)
# if (length(i) > 1)
# pred <- pred[,match(Params, colnames(pred))]
# colnames(pred) <- paste("value", i, sep="_")
# if (obs.error != "poisson")
# pred <- exp(pred)
# attr(pred, "index") <- i
# pred
#}
## how to generate latent log abundances (not prediction)
generateLatent <-
function(x, ...)
{
if (!inherits(x, "pva"))
stop("'x' must be of class 'pva'")
if (!any(is.na(x@observations)) && x@model@obs.error == "none") {
warning("no latent variable in model")
pred <- matrix(log(x@observations), nrow=1)
} else {
if (x@model@obs.error == "none")
warning("no latent variable in model")
dcd <- x@dcdata
dcd@model <- x@model@genmodel
f <- jags.fit(dcd@data,
params="x",
model=dcd@model, ...)
pred <- as.matrix(f)
}
colnames(pred) <- paste("value", 1:ncol(pred), sep="_")
pred
}
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