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,
      which = c("plots", "texts", "summary", "autocorrelation", "crosscorrelation",
        "burnin"),
      ...
    )

### 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 negated estimating function values (evaluated at the MLE of any curved parameters) (i.e., η'_{I}(\hat{θ})\cdot (g_{I}(Y)-g_{I}(y)) for I being indices of the canonical parameters in question), rather than the canonical (sufficient) vectors of the curved statistics relative to the observed (g_{I}(Y)-g_{I}(y)).
`vars.per.page` | Number of rows (one variable per row) per plotting page. Ignored if `latticeExtra` package is not installed.
`which` | A character vector specifying which diagnostics to plot and/or print. Defaults to all of the below if meaningful: `"plots"` Traceplots and density plots of sample values for all statistic or estimating function elements. `"texts"` Shorthand for the following text diagnostics. `"summary"` Summary of network statistic or estimating function elements as produced by `coda::summary.mcmc.list()`. `"autocorrelation"` Autocorrelation of each of the network statistic or estimating function elements. `"crosscorrelation"` Cross-correlations between each pair of the network statistic or estimating function elements. `"burnin"` Burn-in diagnostics, in particular, the Geweke test. Partial matching is supported. (E.g., `which=c("auto","cross")` will print autocorrelation and cross-correlations.)

### 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`.

### Methods (by class)

- `ergm`:

### References

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 4.1.2 Index]