mcmc.diagnostics.ergmm {latentnet}R Documentation

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, sample = "sample",
                      smooth=TRUE, r = 0.0125, digits = 6,
                      maxplot = 1000, verbose = TRUE, 
                      mcmc.title = "Summary of MCMC samples", ...)
ergmm.raftery.diag(data, q = 0.025, rmargin = 0.005, s = 0.95, converge.eps = 0.001)

Arguments

object An object. See documentation for ergmm.
sample The component of object on which the diagnosis is based. The two usual ones are thetasample from the auxiliary sample of the natural parameter and sample the (default) sample of the sufficient statistics from the model.
smooth Draw a smooth line through trace plots
r Percentile of the distribution to estimate
digits Number of digits to print
maxplot Maximum number of statistics to plot
data an 'mcmc' object, typically the component of object on which the diagnosis is based.
q the quantile to be estimated.
rmargin the desired margin of error of the estimate.
s the probability of obtaining an estimate in the interval (q-r,q+r).
converge.eps Precision required for estimate of time to convergence.
verbose If this is TRUE, print out more information about the MCMC runs including lag correlations.
mcmc.title Figure title for the diagnostic plots.
... Additional arguments, to be passed to lower-level functions in the future.

Details

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

The Raftery-Lewis diagnostic is a run length control diagnostic based on a criterion of accuracy of estimation of the quantile q. It is intended for use on a short pilot run of a Markov chain. The number of iterations required to estimate the quantile q to within an accuracy of +/- r with probability p is calculated. Separate calculations are performed for each variable within each chain.

In fact, 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 MCMC diagnostic methods available in coda may be available directly. See the examples and the coda package.

This function depends on the coda package. More information can be found by looking at the documentation of ergmm.

Value

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

See Also

ergmm, network, coda

Examples

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

[Package latentnet version 0.7-3 Index]