R: Conduct Goodness-of-Fit Diagnostics on a Exponential Family...

gof.ergmm {latentnet} | R Documentation

## Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Mixed Model Fit

### Description

`gof` calculates p-values for geodesic distance, degree, and reachability summaries to diagnose the goodness-of-fit of exponential family random graph mixed models. See `ergmm` for more information on these models.

### Usage

    ## S3 method for class 'ergmm'
    gof(
      object,
      ...,
      nsim = 100,
      GOF = ~idegree + odegree + distance,
      verbose = FALSE
    )

### Arguments

`object` | an `ergmm` object (returned by `ergmm`).
`...` | Additional arguments, to be passed to lower-level functions in the future.
`nsim` | The number of simulations to use for the MCMC p-values. This is the size of the sample of graphs to be randomly drawn from the distribution specified by the object on the set of all graphs.
`GOF` | formula; an formula object, of the form `~ <model terms>` specifying the statistics to use to diagnosis the goodness-of-fit of the model. They do not need to be in the model formula specified in `formula`, and typically are not. Examples are the degree distribution ("degree"), minimum geodesic distances ("dist"), and shared partner distributions ("espartners" and "dspartners"). For the details on the possible `<model terms>`, see `ergm-terms`.
`verbose` | Provide verbose information on the progress of the simulation.

### Details

A sample of graphs is randomly drawn from the posterior of the `ergmm`.

A plot of the summary measures is plotted. More information can be found by looking at the documentation of `ergm`.

### Value

`gof` and `gof.ergmm` return an object of class `gof`. This is a list of the tables of statistics and p-values. This is typically plotted using `plot.gof`.

### See Also

`ergmm`, `ergmm (object)`, `ergm`, `network`, `simulate.ergmm`, `plot.gof`

### Examples

    #
    data(sampson)
    #
    # test the gof.ergm function
    #
    samplike.fit <- ergmm(samplike ~ euclidean(d=2,G=3),
                          control=ergmm.control(burnin=1000,interval=5))
    samplike.fit
    summary(samplike.fit)

    #
    # Plot the probabilities first
    #
    monks.gof <- gof(samplike.fit)
    monks.gof
    #
    # Place all three on the same page
    # with nice margins
    #
    par(mfrow=c(1,3))
    par(oma=c(0.5,2,1,0.5))
    #
    plot(monks.gof)
    #
    # And now the odds
    #
    plot(monks.gof, plotlogodds=TRUE)

[Package latentnet version 2.10.5 Index]