control.ergm.bridge: Auxiliaries for Controlling 'ergm.bridge.llr()' and...
In ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

Description Usage Arguments Details Value See Also

Auxiliary functions as user interfaces for fine-tuning the ergm.bridge.llr() algorithm, which approximates log likelihood ratios using bridge sampling.

By default, the bridge sampler inherits its control parameters from the ergm() fit; control.logLik.ergm() allows the user to selectively override them.

control.ergm.bridge(
  bridge.nsteps = 16,
  bridge.target.se = NULL,
  bridge.bidirectional = TRUE,
  MCMC.burnin = MCMC.interval * 128,
  MCMC.burnin.between = max(ceiling(MCMC.burnin/sqrt(bridge.nsteps)), MCMC.interval *
    16),
  MCMC.interval = 128,
  MCMC.samplesize = 16384,
  obs.MCMC.burnin = obs.MCMC.interval * 128,
  obs.MCMC.burnin.between = max(ceiling(obs.MCMC.burnin/sqrt(bridge.nsteps)),
    obs.MCMC.interval * 16),
  obs.MCMC.interval = MCMC.interval,
  obs.MCMC.samplesize = MCMC.samplesize,
  MCMC.prop = trim_env(~sparse),
  MCMC.prop.weights = "default",
  MCMC.prop.args = list(),
  obs.MCMC.prop = MCMC.prop,
  obs.MCMC.prop.weights = MCMC.prop.weights,
  obs.MCMC.prop.args = MCMC.prop.args,
  MCMC.maxedges = Inf,
  MCMC.packagenames = c(),
  term.options = list(),
  seed = NULL,
  parallel = 0,
  parallel.type = NULL,
  parallel.version.check = TRUE,
  parallel.inherit.MT = FALSE,
  ...
)

control.logLik.ergm(
  bridge.nsteps = 16,
  bridge.target.se = NULL,
  bridge.bidirectional = TRUE,
  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 = NULL,
  MCMC.prop.weights = NULL,
  MCMC.prop.args = NULL,
  obs.MCMC.prop = MCMC.prop,
  obs.MCMC.prop.weights = MCMC.prop.weights,
  obs.MCMC.prop.args = MCMC.prop.args,
  MCMC.maxedges = NULL,
  MCMC.packagenames = NULL,
  term.options = NULL,
  seed = NULL,
  parallel = NULL,
  parallel.type = NULL,
  parallel.version.check = TRUE,
  parallel.inherit.MT = FALSE,
  ...
)

`bridge.nsteps`	Number of geometric bridges to use.
`bridge.target.se`	If not `NULL`, if the estimated MCMC standard error of the likelihood estimate exceeds this, repeat the bridge sampling, accumulating samples.
`bridge.bidirectional`	Whether the bridge sampler first bridges from `from` to `to`, then from `to` to `from` (skipping the first burn-in), etc. if multiple attempts are required.
`MCMC.burnin`	Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number.
`MCMC.burnin.between`	Number of proposals between the bridges; typically, less and less is needed as the number of steps decreases.
`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.burnin.between, obs.MCMC.interval, obs.MCMC.samplesize`	The `obs` versions of these arguments are for the unobserved data simulation algorithm.
`MCMC.prop`	Specifies the proposal (directly) and/or a series of "hints" about the structure of the model being sampled. The specification is in the form of a one-sided formula with hints separated by `+` operations. If the LHS exists and is a string, the proposal to be used is selected directly. A common and default "hint" is `~sparse`, indicating that the network is sparse and that the sample should put roughly equal weight on selecting a dyad with or without a tie as a candidate for toggling.
`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.
`MCMC.prop.args`	An alternative, direct way of specifying additional arguments to proposal.
`obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args`	The `obs` versions of these arguments are for the unobserved data simulation algorithm.
`MCMC.maxedges`	The maximum number of edges that may occur during the MCMC sampling. If this number is exceeded at any time, sampling is stopped immediately.
`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. See `? term.options`.
`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.
`parallel.inherit.MT`	Logical: If TRUE, slave nodes and processes inherit the `set.MT_terms()` setting.
`...`	A dummy argument to catch deprecated or mistyped control parameters.