latentnet (version 2.0)

mcmc.diagnostics.ergmm: Conduct MCMC diagnostics on an ERGMM fit

Description

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(x,which.diags=c("cor","acf","trace","raftery"),
                                 burnin=FALSE,
                                 which.vars=NULL,
                                 vertex.i=c(1),...)

Arguments

x

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 "raftery" produces the Raftery-Lewis statistics.

burnin

If TRUE, generates an mcmc.list object for the burnin (if stored) instead of the main sampling run.

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 to include.

...

Additional arguments. None are supported at the moment.

Value

• mcmc.diagnostics.ergmm returns a table of Raftery-Lewis diagnostics.

Details

The plots produced are a trace of the sampled output and a density estimate for each variable in the chain.

Autocorrelation with lags 0 and 1 and a Raftery-Lewis diagnostic is prited.

See Also

ergmm, ergmm.object, raftery.diag, autocorr, plot.mcmc.list

Examples

#
data(sampson)
#
# test the mcmc.diagnostics function
#
gest <- ergmm(samplike ~ latent(d=2))
summary(gest)
#
# Plot the traces and densities
#
mcmc.diagnostics(gest)