ergm (version 3.1.3)

mcmc.diagnostics: Conduct MCMC diagnostics on an ergm fit

Description

This function prints diagnistic information and creates simple diagnostic plots for the MCMC sampled statistics produced from a fit.

Usage

## S3 method for class 'ergm':
mcmc.diagnostics(object, 
                                  center=TRUE,
                                  curved=TRUE,
                                  vars.per.page=3,
                                  \dots)

Arguments

object

An ergm object. See documentation for ergm.

center

Logical: If TRUE, ; center the samples on the observed statistics.

curved

Logical: If TRUE, summarize the curved statistics (evaluated at the MLE of any non-linear parameters), rather than the raw components of the curved statistics.

vars.per.page

Number of rows (one variable per row) per plotting page. Ignored if latticeExtra package is not installed.

...

Additional arguments, to be passed to plotting functions.

Value

• mcmc.diagnostics.ergm returns some degeneracy information, if it is included in the original object. The function is mainly used for its side effect, which is to produce plots and summary output based on those plots.

Details

The plots produced are a trace of the sampled output and a density estimate for each variable in the chain. The diagnostics printed include correlations and convergence diagnostics. In fact, an object contains the matrix of statistics from the MCMC run as component $sample. This matrix is actually an object of class mcmc and can be used directly in the coda package to assess MCMC convergence. Hence all MCMC diagnostic methods available in coda are available directly. See the examples and http://www.mrc-bsu.cam.ac.uk/bugs/classic/coda04/readme.shtml.

More information can be found by looking at the documentation of ergm.

References

Raftery, A.E. and Lewis, S.M. (1992). One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science, 7, 493-497.

Raftery, A.E. and Lewis, S.M. (1995). The number of iterations, convergence diagnostics and generic Metropolis algorithms. In Practical Markov Chain Monte Carlo (W.R. Gilks, D.J. Spiegelhalter and S. Richardson, eds.). London, U.K.: Chapman and Hall.

This function is based on the coda package It is based on the the R function raftery.diag in coda. raftery.diag, in turn, is based on the FORTRAN program gibbsit written by Steven Lewis which is available from the Statlib archive.

See Also

ergm, network package, coda package, summary.ergm

Examples

#
data(florentine)
#
# test the mcmc.diagnostics function
#
gest <- ergm(flomarriage ~ edges + kstar(2))
summary(gest)

#
# Plot the probabilities first
#
mcmc.diagnostics(gest)
#
# Use coda directly
#
library(coda)
#
plot(gest$sample, ask=FALSE)
#
# A full range of diagnostics is available 
# using codamenu()
#