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.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)
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.
Specifies the proposal distribution used in the
MCMC Metropolis-Hastings algorithm. Possible choices are "TNT" or
"random"; the "default" is one of these two, depending on the
constraints in place (as defined by the constraints argument of the
ergm function), though not all weights may be used with all
constraints. 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.
An alternative, direct way of specifying additional arguments to proposal.
Maximum number of edges expected in network.
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 after every MCMC MLE iteration.
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.
A list with arguments as components.
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.