Auxiliary function as user interface for fine-tuning ERGM simulation.
control.simulate(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",
parallel=0,
parallel.type=NULL,
parallel.version.check=TRUE,
…)control.simulate.formula(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",
parallel=0,
parallel.type=NULL,
parallel.version.check=TRUE,
…)
control.simulate.formula.ergm(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",
parallel=0,
parallel.type=NULL,
parallel.version.check=TRUE,
…)
control.simulate.ergm(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",
parallel=0,
parallel.type=NULL,
parallel.version.check=TRUE,
…)
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.
Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number.
Number of proposals between sampled statistics.
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)
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.
Additional arguments, passed to other functions This argument is helpful because it collects any control parameters that have been deprecated; a warning message is printed in case of deprecated arguments.
A list with arguments as components.
This function is only used within a call to the simulate function.
See the usage section in simulate.ergm for details.
simulate.ergm, simulate.formula.
control.ergm performs a
similar function for
ergm;
control.gof performs a
similar function for gof.