control.gof: Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation

In ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

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 + .triadic),
  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().

ergm documentation built on Oct. 7, 2024, 5:08 p.m.