This function creates simple diagnostic plots for the MCMC sampled
statistics produced from a fit. It also prints the Raftery-Lewis
diagnostics, indicates if they are sufficient,
and suggests the run length required.
Usage
## S3 method for class 'ergmm'
mcmc.diagnostics(object,which.diags=c("cor","acf","trace","raftery"),
burnin=FALSE,
which.vars=NULL,
vertex.i=c(1),...)
Arguments
object
An object of class ergmm.
which.diags
A list of diagnostics to produce. "cor" is the
correlation matrix of the statistics, "acf" plots the
autocorrelation functions, "trace" produces trace plots and density
estimates, and "raftery" produces the Raftery-Lewis statistics.
burnin
If not FALSE, rather than perform diagnostics on
the sampling run, performs them on the pilot run whose index is given.
which.vars
A named list mapping variable names to the indices
to include. If given, overrides the defaults and all arguments that follow.
vertex.i
A numeric vector of vertices whose latent space
coordinates and random effects to include.
...
Additional arguments. None are supported at the moment.
Details
Produces the plots per which.diags.
Autocorrelation function that is printed if "acf" is requested is for
lags 0 and interval.
Value
mcmc.diagnostics.ergmm returns a table of Raftery-Lewis diagnostics.
#
data(sampson)
#
# test the mcmc.diagnostics function
#
gest <- ergmm(samplike ~ euclidean(d=2),
control=ergmm.control(burnin=1000,interval=5))
summary(gest)
#
# Plot the traces and densities
#
mcmc.diagnostics(gest)