control.ergm.bridge: Auxiliaries for Controlling 'ergm.bridge.llr()' and...

Description Usage Arguments Details Value See Also

View source: R/control.ergm.bridge.R

Description

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.

Usage

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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,
  ...
)

Arguments

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.

Details

control.ergm.bridge() is only used within a call to the ergm.bridge.llr(), ergm.bridge.dindstart.llk(), or ergm.bridge.0.llk() functions.

control.logLik.ergm() is only used within a call to the logLik.ergm().

Value

A list with arguments as components.

See Also

ergm.bridge.llr()

logLik.ergm


ergm documentation built on June 21, 2021, 9:07 a.m.