ergm: control.logLik.ergm – R documentation

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ergm

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 = NULL,
  MCMC.init.maxedges = NULL,
  MCMC.packagenames = NULL,
  term.options = 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 depending on selected `reference` and `constraints` arguments of the `ergm()` function, but often include `"TNT"` and `"random"`, and the `"default"` is to use the one with the highest priority available. 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. Now defunct: use `options(ergm.logLik.warn_dyads=...)` instead.
`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.
`term.options`	A list of additional arguments to be passed to term initializers. It can also be set globally via `option(ergm.term=list(...))`.
`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 logLik.ergm function.

Value

A list with arguments as components.