ergm (version 3.0-3)

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

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 d

MCMC.prop.args

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

MCMC.init.maxedges

Maximum number of edges expected in network.

MCMC.packagenames

Names of packages in which change statistic functions are found.

seed

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

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