Nothing
print.rjags <- function(x, digits = 3,
intervals = c(0.025, 0.25, 0.5, 0.75, 0.975), ...)
{
x <- x$BUGSoutput
sims.matrix <- x$sims.matrix
mu.vect <- apply(sims.matrix, 2, mean)
sd.vect <- apply(sims.matrix, 2, sd)
int.matrix <- apply(sims.matrix, 2, quantile, intervals)
if (x$n.chains>1) {
n.eff <- x$summary[, "n.eff"]
Rhat <- x$summary[, "Rhat"]
} else {
n.eff <- Rhat <- NULL
}
summaryMatrix <- t(rbind(mu.vect, sd.vect, int.matrix, Rhat, n.eff))
rownameMatrix <- rownames(summaryMatrix)
dev.idx <- match("deviance", rownameMatrix)
if(any(!is.na(dev.idx))){
summaryMatrix <- rbind(summaryMatrix[-dev.idx,], summaryMatrix[dev.idx,])
rownames(summaryMatrix) <- c(rownameMatrix[-dev.idx], rownameMatrix[dev.idx])
}
if (!is.null(x$model.file))
cat("Inference for Bugs model at \"", x$model.file, "\", ",
sep = "")
if (!is.null(x$program))
cat("fit using ", x$program, ",", sep = "")
cat("\n ", x$n.chains, " chains, each with ", x$n.iter, " iterations (first ",
x$n.burnin, " discarded)", sep = "")
if (x$n.thin > 1)
cat(", n.thin =", x$n.thin)
cat("\n n.sims =", x$n.sims, "iterations saved\n")
print(round(summaryMatrix, digits), ...)
if (x$n.chains > 1) {
cat("\nFor each parameter, n.eff is a crude measure of effective sample size,")
cat("\nand Rhat is the potential scale reduction factor (at convergence, Rhat=1).\n")
}
if (x$isDIC) {
msgDICRule <- ifelse(x$DICbyR, "(using the rule, pD = var(deviance)/2)",
"(using the rule, pD = Dbar-Dhat)")
cat(paste("\nDIC info ", msgDICRule, "\n", sep = ""))
if (length(x$DIC) == 1) {
cat("pD =", fround(x$pD, 1), "and DIC =", fround(x$DIC,
1))
}
else if (length(x$DIC) > 1) {
print(round(x$DIC, 1))
}
cat("\nDIC is an estimate of expected predictive error (lower deviance is better).\n")
}
invisible(x)
#print(x$BUGSoutput,...)
}
#function (x, digits.summary = 1, ...)
#{
# if (!is.null(x$model.file))
# cat("Inference for Bugs model at \"", x$model.file, "\", ",
# sep = "")
# if (!is.null(x$program))
# cat("fit using ", x$program, ",", sep = "")
# cat("\n ", x$n.chains, " chains, each with ", x$n.iter, " iterations (first ",
# x$n.burnin, " discarded)", sep = "")
# if (x$n.thin > 1)
# cat(", n.thin =", x$n.thin)
# cat("\n n.sims =", x$n.sims, "iterations saved\n")
# print(round(x$summary, digits.summary), ...)
# if (x$n.chains > 1) {
# cat("\nFor each parameter, n.eff is a crude measure of effective sample size,")
# cat("\nand Rhat is the potential scale reduction factor (at convergence, Rhat=1).\n")
# }
# if (x$isDIC) {
# msgDICRule <- ifelse(x$DICbyR, "(using the rule, pD = var(deviance)/2)",
# "(using the rule, pD = Dbar-Dhat)")
# cat(paste("\nDIC info ", msgDICRule, "\n", sep = ""))
# if (length(x$DIC) == 1) {
# cat("pD =", fround(x$pD, 1), "and DIC =", fround(x$DIC,
# 1))
# }
# else if (length(x$DIC) > 1) {
# print(round(x$DIC, 1))
# }
# cat("\nDIC is an estimate of expected predictive error (lower deviance is better).\n")
# }
# invisible(x)
#}
#
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