control.gof: 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 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.

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.

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.

Value

A list with arguments as components.

Details

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

Description

Usage

Arguments

Value

Details

See Also