R: Auxiliary for Controlling SAN

control.san {ergm}

R Documentation

Auxiliary for Controlling SAN

Description

Auxiliary function as user interface for fine-tuning simulated annealing algorithm.

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.

Details

This function is only used within a call to the san function. See the usage section in san for details.

Value