R: Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation

control.gof {ergm} | R Documentation

## Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation

### Description

Auxiliary function as user interface for fine-tuning ERGM Goodness-of-Fit Evaluation.

The `control.gof.ergm` version is intended to be used with `gof.ergm()` specifically and will "inherit" as many control parameters from `ergm` fit as possible().

### Usage

    control.gof.formula(nsim = 100, MCMC.burnin = 10000,
      MCMC.interval = 1000, MCMC.prop.weights = "default",
      MCMC.prop.args = list(), MCMC.init.maxedges = 20000,
      MCMC.packagenames = c(), MCMC.runtime.traceplot = FALSE,
      network.output = "network", seed = NULL, parallel = 0,
      parallel.type = NULL, parallel.version.check = TRUE)

    control.gof.ergm(nsim = 100, MCMC.burnin = NULL,
      MCMC.interval = NULL, MCMC.prop.weights = NULL,
      MCMC.prop.args = NULL, MCMC.init.maxedges = NULL,
      MCMC.packagenames = NULL, MCMC.runtime.traceplot = FALSE,
      network.output = "network", seed = NULL, parallel = 0,
      parallel.type = NULL, parallel.version.check = TRUE)

### Arguments

`nsim` | Number of networks to be randomly drawn using Markov chain Monte Carlo. This sample of networks provides the basis for comparing the model to the observed network.
`MCMC.burnin` | Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number.
`MCMC.interval` | Number of proposals between sampled statistics.
`MCMC.prop.weights` | Specifies the proposal distribution used in the MCMC Metropolis-Hastings algorithm. Possible choices are `"TNT"` or `"random"`; the `"default"` is one of these two, depending on the constraints in place (as defined by the `constraints` argument of the `ergm` function), though not all weights may be used with all constraints. The `TNT` (tie / no tie) option puts roughly equal weight on selecting a dyad with or without a tie as a candidate for toggling, whereas the `random` option puts equal weight on all possible dyads, though the interpretation of `random` may change according to the constraints in place. When no constraints are in place, the default is TNT, which appears to improve Markov chain mixing particularly for networks with a low edge density, as is typical of many realistic social networks.
`MCMC.prop.args` | An alternative, direct way of specifying additional arguments to proposal.
`MCMC.init.maxedges` | Maximum number of edges expected in network.
`MCMC.packagenames` | Names of packages in which to look for change statistic functions in addition to those autodetected. This argument should not be needed outside of very strange setups.
`MCMC.runtime.traceplot` | Logical: If TRUE, plot traceplots of the MCMC sample after every MCMC MLE iteration.
`network.output` | R class with which to output networks. The options are "network" (default) and "edgelist.compressed" (which saves space but only supports networks without vertex attributes)
`seed` | Seed value (integer) for the random number generator. See `set.seed`.
`parallel` | Number of threads in which to run the sampling. Defaults to 0 (no parallelism). See the entry on parallel processing for details and troubleshooting.
`parallel.type` | API to use for parallel processing. Supported values are `"MPI"` and `"PSOCK"`. Defaults to using the `parallel` package with PSOCK clusters. See `ergm-parallel`
`parallel.version.check` | Logical: If TRUE, check that the version of `ergm` running on the slave nodes is the same as that running on the master node.

### Details

This function is only used within a call to the `gof` function. See the `usage` section in `gof` for details.

### Value

A list with arguments as components.

### See Also

`gof`. The `control.simulate` function performs a similar function for `simulate.ergm`; `control.ergm` performs a similar function for `ergm`.

[Package ergm version 3.10.4 Index]