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

control.gofR Documentation

Auxiliary for Controlling ERGM Goodness-of-Fit Evaluation


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().


  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

  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



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.


Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number.


Number of proposals between sampled statistics.


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.


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.


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.


An alternative, direct way of specifying additional arguments to proposal.


The maximum number of edges that may occur during the MCMC sampling. If this number is exceeded at any time, sampling is stopped immediately.


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.


Logical: If TRUE, plot traceplots of the MCMC sample.


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 value (integer) for the random number generator. See set.seed.


Number of threads in which to run the sampling. Defaults to 0 (no parallelism). See the entry on parallel processing for details and troubleshooting.


API to use for parallel processing. Supported values are "MPI" and "PSOCK". Defaults to using the parallel package with PSOCK clusters. See ergm-parallel


Logical: If TRUE, check that the version of ergm running on the slave nodes is the same as that running on the master node.


Logical: If TRUE, slave nodes and processes inherit the set.MT_terms() setting.


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


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. 2, 2022, 5:07 p.m.