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\name{jags.samples}
\alias{jags.samples}
\title{Generate posterior samples}
\description{
Function to extract random samples from the posterior distribution
of the parameters of a \code{jags} model.
}
\usage{
jags.samples(model, variable.names, n.iter, thin = 1,
type="trace", force.list=FALSE, ...)
}
\arguments{
\item{model}{a jags model object}
\item{variable.names}{a character vector giving the names of variables
to be monitored}
\item{n.iter}{number of iterations to monitor}
\item{thin}{thinning interval for monitors}
\item{type}{type of monitor (can be vectorised)}
\item{force.list}{option to consistently return a named list of monitor
types even if a single monitor type is requested}
\item{...}{optional arguments passed to the update method for jags
model objects}
}
\details{
The \code{jags.samples} function creates monitors for the given
variables, runs the model for \code{n.iter} iterations and returns
the monitored samples.
}
\value{
A list of \code{mcarray} objects, with one element for each
element of the \code{variable.names} argument. If more than
one type of monitor is requested (or if force.list is TRUE)
then the return value will be a (named) list of lists of
\code{mcarray} objects, with one element for each monitor type.
}
\examples{
data(LINE)
LINE$recompile()
LINE.samples <- jags.samples(LINE, c("alpha","beta","sigma"),
n.iter=1000)
LINE.samples
LINE.samples <- jags.samples(LINE, c("alpha","beta","sigma"),
force.list=TRUE, n.iter=1000)
LINE.samples
LINE.samples <- jags.samples(LINE, c("alpha","alpha"),
n.iter=1000, type=c("trace","mean"))
LINE.samples$trace
LINE.samples$mean
}
\author{Martyn Plummer}
\seealso{\code{\link{jags.model}}, \code{\link{coda.samples}}}
\keyword{models}
|
