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ergm (version 3.6.1)

control.logLik.ergm: Auxiliary for Controlling logLik.ergm

Description

Auxiliary function as user interface for fine-tuning logLik.ergm algorithm, which approximates log likelihood values.

Usage

control.logLik.ergm(nsteps=20, MCMC.burnin=NULL, MCMC.interval=NULL, MCMC.samplesize=NULL, obs.MCMC.samplesize=MCMC.samplesize, obs.MCMC.interval=MCMC.interval, obs.MCMC.burnin=MCMC.burnin,


                    MCMC.prop.weights=NULL, MCMC.prop.args=NULL, warn.dyads=TRUE,


                    MCMC.init.maxedges=NULL, MCMC.packagenames=NULL,


                    seed=NULL, parallel = NULL,  parallel.type = NULL, parallel.version.check = TRUE)

Arguments

nsteps
Number of geometric bridges to use.
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.samplesize
Number of network statistics, randomly drawn from a given distribution on the set of all networks, returned by the Metropolis-Hastings algorithm.
obs.MCMC.burnin, obs.MCMC.interval, obs.MCMC.samplesize
The obs versions of these arguments are for the unobserved data simulation algorithm.
MCMC.prop.weights
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.
MCMC.prop.args
An alternative, direct way of specifying additional arguments to proposal.
warn.dyads
Whether or not a warning should be issued when sample space constraints render the observed number of dyads ill-defined.
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.
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 logLik.ergm function.

See Also

logLik.ergm