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