Provides graphical diagnostics of the random effect posterior draws from the (best) model. Availabile diagnostics include the sample path, histograms, cummulative sums, and autocorrelation.
plot_mcmc(
object,
plots = "sample.path",
grps = "all",
vars = "all",
numeric_grp_order = FALSE,
bin_width = NULL
)a list of ggplot graphics, each faceted by group and random effect variable.
Type of plots specified in the plots argument.
an object of class pglmmObj
a character string or a vector of character strings specifying which graphical diagnostics to provide. Options include a sample path plot (default, "sample.path"), autocorrelation plots ("autocorr"), histograms ("histogram"), cumulative sum plots ("cumsum"), and all four possible plot options ("all"). While the "all" option will produce all four possible plots, subsets of the types of plots (e.g. sample path plots and autocorrelation plots only) can be specified with a vector of the relevant character strings (e.g. c("sample.path","autocorr"))
a character string or a vector of character strings specifying which groups should have diagnostics provided. The names of the groups match the input group factor levels. Default is set to 'all' for all groups.
a character string or a vector of character strings specifying which variables
should have diagnostics provided. Default is set to
'all', which picks all variables with non-zero random effects.
Tip: can find the names of the random effect variables in
the output sigma matrix found in the pglmmObj object, run sigma(object).
if TRUE, specifies that the groups factor should be converted to numeric values. This option could be used to ensure that the organization of the groups is in the proper numeric order (e.g. groups with levels 1-10 are ordered 1-10, not 1, 10, 2-9).
optional binwidth argument for geom_histogram from the ggplot2
package. Default set to NULL, which specifies the default geom_histogram binwidth.
This argument only applies if the "histogram" plot type is selected.