R: Conduct MCMC diagnostics on an ergm or stergm fit

mcmc.diagnostics {tergm} | R Documentation

## Conduct MCMC diagnostics on an ergm or stergm fit

### Description

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

### Usage

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

### Arguments

`object` | A stergm object. See documentation for `stergm`.
`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.

### 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/software/bugs/the-bugs-project-winbugs/coda-readme/.

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

### 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.

### 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`, `stergm`,`network` package, `coda` package, `summary.ergm`

[Package tergm version 3.4.1 Index]