R: Auxiliary for Controlling ergm.bridge

control.ergm.bridge {ergm}

R Documentation

Auxiliary for Controlling ergm.bridge

Description

Auxiliary function as user interface for fine-tuning ergm.bridge algorithm, which approximates log likelihood ratios using bridge sampling.

Usage

control.ergm.bridge(nsteps=20,
                    MCMC.burnin=10000,
                    MCMC.interval=100,
                    MCMC.samplesize=10000, 
                    obs.MCMC.samplesize=MCMC.samplesize,
                    obs.MCMC.interval=MCMC.interval,
                    obs.MCMC.burnin=MCMC.burnin,

                    MCMC.prop.weights="default",
                    MCMC.prop.args=list(),

                    MCMC.init.maxedges=20000,
                    MCMC.packagenames=c(),

                    seed=NULL,
                    parallel=0,
                    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.
`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.

Details

This function is only used within a call to the ergm.bridge.llr or ergm.bridge.dindstart.llk functions.

Value