| mcmcsamp {lme4} | R Documentation |
This generic function generates a sample from the posterior distribution of the parameters of a fitted model using Markov Chain Monte Carlo methods.
mcmcsamp(object, n, verbose, ...)
object |
An object of a suitable class - usually an
lmer or glmer 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 objects takes the optional argument
saveb which, if 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 trans which, if 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. A third optional
argument is deviance which, if TRUE, saves the
conditional likelihood, expressed on the deviance scale, at each
iteration of the chain. Default is FALSE. |
An object of (S3) class "mcmc" suitable for use with the
functions in the "coda" package.
(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
set.seed(101); samp0 <- mcmcsamp(fm1, n = 1000) # default deviance = FALSE
set.seed(101); samp1 <- mcmcsamp(fm1, n = 1000, deviance = TRUE)
colnames(samp1) # has "deviance"
if (require("coda", quietly = TRUE, character.only = TRUE)) {
densityplot(samp1)
qqmath(samp1)
xyplot(samp1, scales = list(x = list(axs = 'i')))
print(summary(samp1))
print(autocorr.diag(samp1))
}
## potentially useful approximate D.F. :
(eDF <- mean(samp1[,"deviance"]) - deviance(fm1, REML=FALSE))