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# View of /branches/Matrix-for-R-2.3.x/man/mcmcsamp.Rd

Tue Sep 19 20:00:40 2006 UTC (14 years, 5 months ago) by bates
File size: 3564 byte(s)
Fix error in example in mcmcsamp.Rd with new version of lattice
\name{mcmcsamp}
\docType{genericFunction}
\alias{mcmcsamp}
\alias{mcmcsamp,lmer-method}
\alias{mcmcsamp,glmer-method}
\title{Generate an MCMC sample}
\description{
This generic function generates a sample from the posterior
distribution of the parameters of a fitted model using Markov Chain
Monte Carlo methods.
}
\usage{
mcmcsamp(object, n, verbose, \dots)
}
\arguments{
\item{object}{An object of a suitable class - usually an
}
\item{n}{integer - number of samples to generate. Defaults to 1.}
\item{verbose}{logical - if \code{TRUE} verbose output is printed.
Defaults to \code{FALSE}.}
\item{\dots}{Some methods for this generic function may take
additional, optional arguments.  The method for
\code{\linkS4class{lmer}} objects takes the optional argument
\code{saveb} which, if \code{TRUE}, causes the values of the random
effects in each sample to be saved.  Note that this can result in
very large objects being saved.  Use with caution. A second optional
argument is \code{trans} which, if \code{TRUE} (the default), returns
a sample of transformed parameters.  All variances are expressed on
the logarithm scale and any covariances are converted to Fisher's "z"
transformation of the corresponding correlation.}
}
\value{
An object of (S3) class \code{"mcmc"} suitable for use with the
functions in the "coda" package.
}
\section{Methods}{
\describe{
\item{object = "lmer"}{generate MCMC samples from the posterior
distribution of the parameters of a linear mixed model.  The prior
on the fixed effects parameters is taken to be locally uniform.
The prior on the variance-covariance matrices of the random
effects is taken to be the locally non-informative prior described
in Box and Tiao (1973). Conditional on the current values of the
random effects these are sampled from a Wishart distribution.}
\item{object = "glmer"}{generate MCMC samples from the posterior
distribution of the parameters of a generalized linear mixed
model.  The prior on the fixed effects parameters is taken to be
locally uniform. The prior on the variance-covariance matrices of
the random effects is taken to be the locally non-informative
prior described in Box and Tiao (1973). Conditional on the current
values of the random effects these are sampled from a Wishart
distribution.}
}
}
\examples{
require("lattice", quietly = TRUE, character = TRUE)
data(sleepstudy)
(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
set.seed(101)
samp1 <- mcmcsamp(fm1, n = 1000)
frm <-
data.frame(vals = c(samp1), iter = rep(1:nrow(samp1), ncol(samp1)),
par = factor(rep(1:ncol(samp1), each = nrow(samp1)),labels = colnames(samp1)))
densityplot(~ vals | par, frm, plot.points = FALSE,
scales = list(relation = 'free', x = list(axs='i')))
xyplot(vals ~ iter | par, frm, layout = c(1, ncol(samp1)),
scales = list(x = list(axs = "i"), y = list(relation = "free")),
main = "Trace plot", xlab = "Iteration number", ylab = "",
type = "l")
qqmath(~ vals | par, frm, type = 'l',
scales = list(y = list(relation = 'free')))
if (require("coda", quietly = TRUE, character = TRUE)) {
print(summary(samp1))
print(autocorr.diag(samp1))
}
(eDF <- mean(samp1[,"deviance"]) - deviance(fm1, REML=FALSE)) # potentially useful approximate D.F.
\dontshow{
stopifnot(abs(eDF - 7) < 1)
}
}
\keyword{methods}
\keyword{datagen}