Conduct MCMC diagnostics on an ergm or stergm fit
This function prints diagnistic information and creates simple diagnostic plots for the MCMC sampled
statistics produced from a
1 2 3 4 5 6
A stergm object. See documentation for
Logical: If TRUE, ; center the samples on the observed statistics.
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.
Number of rows (one variable per row)
per plotting page. Ignored
Additional arguments, to be passed to plotting functions.
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
This matrix is actually an object of class
can be used directly in the
coda package to assess MCMC
convergence. Hence all MCMC diagnostic methods available
coda are available directly. See the examples and
More information can be found by looking at the documentation of
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.
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
It is based on the the
in turn, is based on the FORTRAN program
gibbsit written by
Steven Lewis which is available from the Statlib archive.
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.