Usage
control.san(coef=NULL,
SAN.tau=1, SAN.invcov=NULL, SAN.burnin=100000, SAN.interval=10000, SAN.init.maxedges=20000, SAN.prop.weights="default", SAN.prop.args=list(), SAN.packagenames=c(), MPLE.max.dyad.types=1e6, MPLE.samplesize = 50000, network.output="network",
seed=NULL, parallel=0, parallel.type=NULL, parallel.version.check=TRUE)
Arguments
coef
Vector of model coefficients used for MCMC simulations,
one for each model term.
SAN.tau
Currently unused.
SAN.invcov
Initial inverse covariance matrix used to calculate
Mahalanobis distance in determining how far a proposed MCMC
move is from the target.stats vector.
If NULL, taken to be the covariance
matrix returned when fitting the MPLE if coef==NULL,
or the identity matrix otherwise.
SAN.burnin
Number of MCMC proposals before any sampling
is done.
SAN.interval
Number of proposals between sampled statistics.
SAN.init.maxedges
Maximum number of edges expected in network.
SAN.prop.weights
Specifies the method to allocate probabilities of
being proposed to dyads. Defaults to "default", which picks a
reasonable default for the specified constraint.
Other possible values are
"TNT", "random", and "nonobserved", though not
all values may be used
with all possible constraints.
SAN.prop.args
An alternative, direct way of specifying additional
arguments to proposal.
SAN.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.
MPLE.max.dyad.types
Maximum number of unique values of
change statistic vectors, which are the predictors in a logistic
regression used to calculate the MPLE. This calculation uses
a compression algorithm that allocates space based on
MPLE.max.dyad.types
MPLE.samplesize
Not currently documented; used in
conditional-on-degree version of MPLE.
network.output
R class with which to output
networks. The options are "network" (default) and "edgelist.compressed"
(which saves space but only supports networks without vertex attributes)
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.