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().
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
)
A list with arguments as components.
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.
gof(). The control.simulate() function
performs a similar function for simulate.ergm();
control.ergm() performs a similar function for
ergm().