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=TRUE,
MCMC.init.maxedges=NULL,
MCMC.packagenames=NULL,
seed=NULL)
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 are "TNT" or
"random"; the "default" is one of these two, depending on the
constraints in place (as d
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.
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.
seed
Seed value (integer) for the random number generator.
See set.seed