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

In latentnet: Latent Position and Cluster Models for Statistical Networks

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(
  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.

See Also

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

Examples

#
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)

latentnet documentation built on Sept. 11, 2025, 9:09 a.m.