#' Convert to bugs object
#'
#' Function converting results from Markov chain simulations, that might not be
#' from BUGS, to bugs object. Used mainly to display results with
#' \code{\link{plot.bugs}}.
#'
#' This function takes a 3-way array of simulations and makes it into a
#' \code{\link{bugs}} object that can be conveniently displayed using
#' \code{print} and \code{plot} and accessed using \code{attach.bugs}. If the
#' third dimension of sims() has names, the resulting bugs object will respect
#' that naming convention. For example, if the parameter names are
#' \dQuote{alpha[1]}, \dQuote{alpha[2]}, ..., \dQuote{alpha[8]}, \dQuote{mu},
#' \dQuote{tau}, then \code{as.bugs.array} will know that alpha is a vector of
#' length 8, and mu and tau are scalar parameters. These will all be plotted
#' appropriately by \code{plot} and attached appropriately by
#' \code{attach.bugs}.
#'
#' If \code{DIC=TRUE} then DIC can be either already passed to argument
#' \code{DICOutput} or calculated from deviance values in \code{sims.array}.
#'
#' @param sims.array 3-way array of simulation output, with dimensions n.keep,
#' n.chains, and length of combined parameter vector.
#' @param model.file file containing the model written in \pkg{MultiBUGS} code
#' @param program the program used
#' @param DIC logical; whether DIC should be calculated, see also argument
#' \code{DICOutput} and details
#' @param DICOutput DIC value
#' @param n.iter number of total iterations per chain used for generating
#' \code{sims.array}
#' @param n.burnin length of burn in, i.e. number of iterations to discarded at
#' the beginning for generating \code{sims.array}
#' @param n.thin thinning rate, a positive integer, used for generating
#' \code{sims.array}
#' @return A \code{\link{bugs}} object is returned
#' @author Jouni Kerman, \email{kerman@@stat.columbia.edu} with modification by
#' Andrew Gelman, \cr \email{gelman@@stat.columbia.edu}, packaged by Uwe
#' Ligges, \email{ligges@@statistik.tu-dortmund.de}.
#' @seealso \code{\link{bugs}}
#' @keywords interface manip
#' @export as.bugs.array
as.bugs.array <- function(sims.array,
model.file = NULL,
program = NULL,
DIC = FALSE,
DICOutput = NULL,
n.iter = NULL,
n.burnin = 0,
n.thin = 1){
## Jouni Kerman's function to convert a 3-way array to a Bugs
## object 'sims.array' is supposed to be a 3-way array with
## n.sims*n.chains*n.parameters simulations, and the 3rd
## component of dimnames(x) should have the parameter names.
d <- dim(sims.array)
n.keep <- d[1]
n.chains <- d[2]
n.parameters <- d[3]
n.sims <- n.keep * n.chains
if (is.null(n.iter)){
n.iter <- (n.keep + n.burnin) * n.thin
}
parameter.names <- dimnames(sims.array)[[3]]
if (is.null(parameter.names)){
parameter.names <- paste("P", 1:n.parameters, sep = "")
dimnames(sims.array)[[3]] <- parameter.names
}
parameters.to.save <- unique(sapply(strsplit(parameter.names, "\\["), "[", 1))
sims <- matrix(NA, n.sims, n.parameters)
root.long <- character(n.parameters)
indexes.long <- vector(n.parameters, mode = "list")
for (i in 1:n.parameters){
temp <- decode.parameter.name(parameter.names[i])
root.long[i] <- temp$root
indexes.long[[i]] <- temp$indexes
}
n.roots <- length(parameters.to.save)
left.bracket.short <- as.vector(regexpr("[[]", parameters.to.save))
right.bracket.short <- as.vector(regexpr("[]]", parameters.to.save))
root.short <- ifelse(left.bracket.short == -1,
parameters.to.save,
substring(parameters.to.save, 1, left.bracket.short - 1))
dimension.short <- rep(0, n.roots)
indexes.short <- vector(n.roots, mode = "list")
n.indexes.short <- vector(n.roots, mode = "list")
long.short <- vector(n.roots, mode = "list")
length.short <- numeric(n.roots)
for (j in 1:n.roots){
long.short[[j]] <- (1:n.parameters)[root.long == root.short[j]]
length.short[j] <- length(long.short[[j]])
if (length.short[j] == 0){
stop(paste("parameter", root.short[[j]], "is not in the model"))
} else if (length.short[j] > 1){
dimension.short[j] <- length(indexes.long[[long.short[[j]][1]]])
n.indexes.short[[j]] <- numeric(dimension.short[j])
for (k in 1:dimension.short[j]){
n.indexes.short[[j]][k] <-
length(unique(unlist(lapply(indexes.long[long.short[[j]]],
.subset,
k))))
}
length.short[j] <- prod(n.indexes.short[[j]])
### bug reported by S. McKay Curtis on February 22, 2010, we
### cannot check that safely: if (length(long.short[[j]]) !=
### length.short[j]){ stop(paste('error in parameter',
### root.short[[j]], 'in parameters.to.save')) }
indexes.short[[j]] <- as.list(numeric(length.short[j]))
for (k in 1:length.short[j]){
indexes.short[[j]][[k]] <- indexes.long[[long.short[[j]][k]]]
}
}
}
rank.long <- unlist(long.short)
# ----- yes, it's inefficient to do this, but for now I'm
# just letting this be as it is:
for (k in 1:n.parameters){
sims[, k] <- as.vector(sims.array[, , k])
}
# ----
dimnames(sims) <- list(NULL, parameter.names)
summary <- monitor(sims.array, n.chains, keep.all = TRUE)
last.values <- as.list(numeric(n.chains))
for (i in 1:n.chains){
n.roots.0 <- if (DIC){
n.roots - 1
} else {
n.roots
}
last.values[[i]] <- as.list(numeric(n.roots.0))
names(last.values[[i]]) <- root.short[1:n.roots.0]
for (j in 1:n.roots.0){
if (dimension.short[j] <= 1){
last.values[[i]][[j]] <- sims.array[n.keep, i, long.short[[j]]]
names(last.values[[i]][[j]]) <- NULL
} else {
last.values[[i]][[j]] <-
aperm(array(sims.array[n.keep, i, long.short[[j]]],
rev(n.indexes.short[[j]])),
dimension.short[j]:1)
}
}
}
sims <- sims[sample(n.sims), , drop = FALSE]
sims.list <-
summary.mean <- summary.sd <- summary.median <- vector(n.roots, mode = "list")
names(sims.list) <- names(summary.mean) <- names(summary.sd) <- names(summary.median) <- root.short
for (j in 1:n.roots){
if (length.short[j] == 1){
sims.list[[j]] <- sims[, long.short[[j]]]
summary.mean[[j]] <- summary[long.short[[j]], "mean"]
summary.sd[[j]] <- summary[long.short[[j]], "sd"]
summary.median[[j]] <- summary[long.short[[j]], "50%"]
} else {
temp2 <- dimension.short[j]:1
sims.list[[j]] <- aperm(array(sims[, long.short[[j]]],
c(n.sims, rev(n.indexes.short[[j]]))),
c(1, (dimension.short[j] + 1):2))
summary.mean[[j]] <- aperm(array(summary[long.short[[j]], "mean"],
rev(n.indexes.short[[j]])),
temp2)
summary.sd[[j]] <- aperm(array(summary[long.short[[j]], "sd"],
rev(n.indexes.short[[j]])),
temp2)
summary.median[[j]] <- aperm(array(summary[long.short[[j]], "50%"],
rev(n.indexes.short[[j]])),
temp2)
}
}
summary <- summary[rank.long, ]
all <- list(n.chains = n.chains,
n.iter = n.iter,
n.burnin = n.burnin,
n.thin = n.thin,
n.keep = n.keep,
n.sims = n.sims,
sims.array = sims.array[, , rank.long, drop = FALSE],
sims.list = sims.list,
sims.matrix = sims[, rank.long],
summary = summary,
mean = summary.mean,
sd = summary.sd,
median = summary.median,
root.short = root.short,
long.short = long.short,
dimension.short = dimension.short,
indexes.short = indexes.short,
last.values = last.values,
program = program,
model.file = model.file)
if (DIC && is.null(DICOutput)){
## calculate DIC from deviance
deviance <- all$sims.array[, , dim(sims.array)[3], drop = FALSE]
dim(deviance) <- dim(deviance)[1:2]
pD <- numeric(n.chains)
DIC <- numeric(n.chains)
for (i in 1:n.chains){
pD[i] <- var(deviance[, i])/2
DIC[i] <- mean(deviance[, i]) + pD[i]
}
all <- c(all, list(isDIC = TRUE,
DICbyR = TRUE,
pD = mean(pD),
DIC = mean(DIC)))
} else if (DIC && !is.null(DICOutput)){
## use DIC from BUGS
all <- c(all,
list(isDIC = TRUE,
DICbyR = FALSE,
pD = DICOutput[nrow(DICOutput), 4],
DIC = DICOutput[nrow(DICOutput), 3]))
} else {
all <- c(all, isDIC = FALSE)
}
class(all) <- "bugs"
all
}
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