https://github.com/cran/LearnBayes
Tip revision: 3b0412e613a3efcfd5e679868e78d0a55f16cb75 authored by Jim Albert on 08 November 2008, 00:00:00 UTC
version 2.0
version 2.0
Tip revision: 3b0412e
rwmetrop.Rd
\name{rwmetrop}
\alias{rwmetrop}
\title{Random walk Metropolis algorithm of a posterior distribution}
\description{
Simulates iterates of a random walk Metropolis chain for an arbitrary real-valued
posterior density defined by the user
}
\usage{
rwmetrop(logpost,proposal,start,m,par)
}
\arguments{
\item{logpost}{function defining the log posterior density}
\item{proposal}{a list containing var, an estimated variance-covariance matrix, and scale, the Metropolis scale factor}
\item{start}{vector containing the starting value of the parameter}
\item{m}{the number of iterations of the chain}
\item{par}{data that is used in the function logpost}
}
\value{
\item{par}{a matrix of simulated values where each row corresponds to a value of the vector parameter}
\item{accept}{the acceptance rate of the algorithm}
}
\author{Jim Albert}
\examples{
data=c(6,2,3,10)
varcov=diag(c(1,1))
proposal=list(var=varcov,scale=2)
start=array(c(1,1),c(1,2))
m=1000
s=rwmetrop(logctablepost,proposal,start,m,data)
}
\keyword{models}