mcmc.diagnostics.ergm {ergm} | R Documentation |
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.
## S3 method for class 'ergm': mcmc.diagnostics (object, sample = "sample", smooth = TRUE, r = 0.0125, digits = 6, maxplot = 1000, verbose = TRUE, center = TRUE, main = "Summary of MCMC samples", xlab = "Iterations", ylab = "", ...)
object |
An object. See documentation for ergm . |
sample |
The component of object on which the
diagnosis is based. The two usuals ones are thetasample from the
auxilary 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 |
verbose |
If this is TRUE , print out more information about the MCMC runs including lag correlations. |
center |
logical; should the samples be centered on the observed statistics. |
main |
Figure title for the diagnostic plots. |
xlab |
X-axis label for diagnostic plots |
ylab |
Y-axis label for diagnostic plots |
... |
Additional arguments, to be passed to lower-level functions in the future. |
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, an object
contains the matrix of
statistics from the MCMC run as component $sample
.
This matrix is actually an object of class mcmc
and
can be used directly in the CODA
package to assess MCMC
convergence. Hence all MCMC diagnostic methods available
in coda
are available directly. See the examples and
http://www.mrc-bsu.cam.ac.uk/bugs/classic/coda04/readme.shtml.
More information can be found by looking at the documentation of
ergm
.
mcmc.diagnostics.ergm
returns a matrix of
Raftery-Lewis diagnostics.
burn in
iterations to be discarded
(total over all chains).dependence factor
) of the extent to
which auto-correlation inflates the required sample
size. Values of I
larger than 5 indicate strong
autocorrelation which may be due to a poor choice of
starting value, high posterior correlations, or
stickiness
of the MCMC algorithm.
Mark S. Handcock, handcock@stat.washington.edu
based on the coda
package and also ideas
from mcgibbsit
by
Gregory R. Warnes gregory_r_warnes@groton.pfizer.com.
It is based on the the R function
raftery.diag
in coda
.
raftery.diag
, in turn, is based on the
FORTRAN program gibbsit
written by Steven Lewis which is available
from the Statlib archive.
Warnes, G.W. (2000). Multi-Chain and Parallel Algorithms for Markov Chain Monte Carlo. Dissertation, Department of Biostatistics, University of Washington,
Raftery, A.E. and Lewis, S.M. (1992). One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statistical Science, 7, 493-497.
Raftery, A.E. and Lewis, S.M. (1995). The number of iterations, convergence diagnostics and generic Metropolis algorithms. In Practical Markov Chain Monte Carlo (W.R. Gilks, D.J. Spiegelhalter and S. Richardson, eds.). London, U.K.: Chapman and Hall.
ergm
, network
,
coda
, mcgibbsit
,
summary.ergm
# data(florentine) # # test the mcmc.diagnostics function # gest <- ergm(flomarriage ~ edges + kstar(2)) summary(gest) # # Plot the probabilities first # mcmc.diagnostics(gest) # # Use coda directly # library(coda) # plot(gest$sample, ask=FALSE) ergm.raftery.diag(gest$sample, r=0.1) # # A full range of diagnostics are available # using codamenu() #