Matrix (version 0.99-3)

mcmcsamp: 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, ...)

Arguments

object

An object of a suitable class - usually an lmer-class{lmer} object.

n

integer - number of samples to generate. Defaults to 1.

verbose

logical - if TRUE verbose output is printed. Defaults to FALSE.

...

Some methods for this generic function may take additional, optional arguments. The method for lmer-class{lmer} objects takes the optional argument saveb which, if TRUE

Value

• An object of (S3) class "mcmc" suitable for use with the functions in the "coda" package.

Examples

require("lattice", quietly = TRUE, character = TRUE)
(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
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 = 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))
}