Auxiliary function as user interface for fine-tuning simulated annealing algorithm.
control.san(coef = NULL, SAN.tau = 1, SAN.invcov = NULL,
SAN.burnin = 1e+05, SAN.interval = 10000,
SAN.init.maxedges = 20000, SAN.prop.weights = "default",
SAN.prop.args = list(), SAN.packagenames = c(),
MPLE.max.dyad.types = 1e+06, MPLE.samplesize = 50000,
network.output = "network", term.options = list(), seed = NULL,
parallel = 0, parallel.type = NULL, parallel.version.check = TRUE)Vector of model coefficients used for MCMC simulations, one for each model term.
Currently unused.
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.
Number of MCMC proposals before any sampling is done.
Number of proposals between sampled statistics.
Maximum number of edges expected in network.
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.
An alternative, direct way of specifying additional arguments to proposal.
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.
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
Not currently documented; used in conditional-on-degree version of MPLE.
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)
A list of additional arguments to be passed to term initializers. It can also be set globally via option(ergm.term=list(...)).
Seed value (integer) for the random number generator. See
set.seed.
Number of threads in which to run the sampling. Defaults to 0 (no parallelism). See the entry on parallel processing for details and troubleshooting.
API to use for parallel processing. Supported values
are "MPI" and "PSOCK". Defaults to using the parallel
package with PSOCK clusters. See ergm-parallel
Logical: If TRUE, check that the version of
ergm running on the slave nodes is the same as
that running on the master node.
A list with arguments as components.
This function is only used within a call to the san function.
See the usage section in san for details.