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

Value

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

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.

Examples

Run this code


# \donttest{
#
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)
# }