| merMCMC-class {lme4} | R Documentation |
Objects of class "merMCMC" are Markov chain Monte Carlo samples
from the distribution of the parameters of a fitted mixed-effects
model.
Objects can be created by calls of the form new("merMCMC", ...)
or, more commonly, via the mer method for the generic
mcmcsamp function.
Gp:Gp slot of the original
mer objectST:ST slot of the mer objectcall:mer objectdeviance:dims:dims slot of the original
mer objectfixef:nc:dims["nf"]. The
number of columns of random effects in each term.ranef:saveb = TRUE is specified in
the call to mcmcsamp. Consider the size of this
matrix, which could be very large, before setting saveb = TRUE.sigma:numeric(0) if dims["useSc"] is FALSE.signature(object = "merMCMC"): use the
chain to calculate Highest Posterior Density (HPD) intervals of a
given empirical probability content for the model parameters. See
HPDinterval.signature(x = "merMCMC"): transform the
ST and sigma slots to some combination of variances,
covariances, standard deviations and correlations. See
VarCorr for details.signature(x = "merMCMC"): returns the
fixef-effects and variance-covariance parameters from the chain in
the form of a data frame. The type argument for the
VarCorr method can be passed to this method to
select the type of variance-covariance parameters returned.signature(x = "merMCMC"): Same as the
as.data.frame method described above but returning a matrix.signature(from = "merMCMC", to = "data.frame"):
Same as the as.data.frame method.signature(object = "merMCMC"): plot
empirical densities for the parameters from the chain. See also
densityplot.signature(object = "merMCMC"): plot
quantile-quantile plots for the parameters from the sample in the
chain. See also qqmath.signature(object = "merMCMC"): plot
traces of the parameter samples in the chain.
mcmcsamp produces these objects,
lmer, glmer and nlmer
produce the mer objects.
showClass("merMCMC")