View source: R/control.ergm.bridge.R
control.ergm.bridge  R Documentation 
ergm.bridge.llr()
and logLik.ergm()
Auxiliary functions as user interfaces for finetuning 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 = Inf,
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 
bridge.bidirectional 
Whether the bridge sampler first bridges from 
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 MetropolisHastings algorithm. 
obs.MCMC.burnin, obs.MCMC.burnin.between, obs.MCMC.interval, obs.MCMC.samplesize 
The 
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 onesided formula
with hints separated by A common and default "hint" is 
MCMC.prop.weights 
Specifies the proposal
distribution used in the MCMC MetropolisHastings algorithm. Possible
choices depending on selected 
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 
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 
seed 
Seed value (integer) for the random number generator. See

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 
parallel.version.check 
Logical: If TRUE, check that the version of

parallel.inherit.MT 
Logical: If TRUE, slave nodes and
processes inherit the 
... 
A dummy argument to catch deprecated or mistyped control parameters. 
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()
.
A list with arguments as components.
ergm.bridge.llr()
logLik.ergm
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