R: Conduct MCMC diagnostics on a model fit

mcmc.diagnostics {ergm} | R Documentation

## Conduct MCMC diagnostics on a model fit

### Description

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

### Usage

    mcmc.diagnostics(object, ...)

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

### Arguments

`object` | A model fit object to be diagnosed.
`...` | Additional arguments, to be passed to plotting functions.
`center` | Logical: If TRUE, center the samples on the observed statistics.
`esteq` | Logical: If TRUE, for statistics corresponding to curved ERGM terms, summarize the curved statistics by their estimating equation values (evaluated at the MLE of any curved parameters) (i.e., η'_{I}(\hat{θ})\cdot g_{I}(y) for I being indices of the canonical parameters in question), rather than the canonical (sufficient) vectors of the curved statistics (g_{I}(y)).
`vars.per.page` | Number of rows (one variable per row) per plotting page. Ignored if `latticeExtra` package is not installed.

### Details

A pair of plots are produced for each statistic:a trace of the sampled output statistic values on the left and density estimate for each variable in the MCMC chain on the right. Diagnostics printed to the console include correlations and convergence diagnostics.

For `ergm()` specifically, recent changes in the estimation algorithm mean that these plots can no longer be used to ensure that the mean statistics from the model match the observed network statistics. For that functionality, please use the GOF command: `gof(object, GOF=~model)`.

In fact, an ergm output `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 https://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 `ergm`.

### 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.

### Methods (by class)

- `ergm`:

### References

Markov University of Washington, 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

    ## Not run:
    #
    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()
    #

    ## End(Not run)

[Package ergm version 3.10.4 Index]