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

In ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

Auxiliaries for Controlling ergm.bridge.llr() and logLik.ergm()

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

control.ergm.bridge(
  bridge.nsteps = 16,
  bridge.target.se = NULL,
  bridge.bidirectional = TRUE,
  drop = 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 + .triadic),
  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,
  drop = NULL,
  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 = Inf,
  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.
drop	See control.ergm().
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 ergm-parallel for details and troubleshooting.
parallel.type	API to use for parallel processing. 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 April 3, 2025, 7:53 p.m.