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.batch = 0,
      MCMC.prop = trim_env(~sparse),
      MCMC.prop.weights = "default",
      MCMC.prop.args = list(),
      MCMC.maxedges = Inf,
      MCMC.packagenames = c(),
      MCMC.runtime.traceplot = FALSE,
      network.output = "network",
      seed = NULL,
      parallel = 0,
      parallel.type = NULL,
      parallel.version.check = TRUE,
      parallel.inherit.MT = FALSE
    )

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

### 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.batch` | if not 0 or `NULL`, sample about this many networks per call to the lower-level code; this can be useful if `output=` is a function, where it can be used to limit the number of networks held in memory at any given time.
`MCMC.prop` | Specifies the proposal (directly) and/or a series of "hints" about the structure of the model being sampled. The specification is in the form of a one-sided formula with hints separated by `+` operations. If the LHS exists and is a string, the proposal to be used is selected directly. A common and default "hint" is `~sparse`, indicating that the network is sparse and that the sample should put roughly equal weight on selecting a dyad with or without a tie as a candidate for toggling.
`MCMC.prop.weights` | Specifies the proposal distribution used in the MCMC Metropolis-Hastings algorithm. Possible choices depending on selected `reference` and `constraints` arguments of the `ergm()` function, but often include `"TNT"` and `"random"`, and the `"default"` is to use the one with the highest priority available.
`MCMC.prop.args` | An alternative, direct way of specifying additional arguments to proposal.
`MCMC.maxedges` | The maximum number of edges that may occur during the MCMC sampling. If this number is exceeded at any time, sampling is stopped immediately.
`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.
`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.
`parallel.inherit.MT` | Logical: If TRUE, slave nodes and processes inherit the `set.MT_terms()` setting.

### 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 4.1.2 Index]