ergm: control.san – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

ergm

control.san

Auxiliary for Controlling SAN

Description

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

Usage

control.san(
  SAN.maxit = 4,
  SAN.tau = 1,
  SAN.invcov = NULL,
  SAN.invcov.diag = FALSE,
  SAN.nsteps.alloc = function(nsim) 2^seq_len(nsim),
  SAN.nsteps = 2^19,
  SAN.samplesize = 2^12,
  SAN.init.maxedges = 20000,
  SAN.max.maxedges = 2^26,
  SAN.prop.weights = "default",
  SAN.prop.args = list(),
  SAN.packagenames = c(),
  SAN.ignore.finite.offsets = TRUE,
  term.options = list(),
  seed = NULL,
  parallel = 0,
  parallel.type = NULL,
  parallel.version.check = TRUE
)

Arguments

`SAN.maxit`	Number of temperature levels to use.
`SAN.tau`	Tuning parameter, specifying the temperature of the process during the penultimate iteration. (During the last iteration, the temperature is set to 0, resulting in a greedy search, and during the previous iterations, the temperature is set to `SAN.tau*(iterations left after this one)`.
`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`, initially set to the identity matrix, then during subsequent runs estimated empirically.
`SAN.invcov.diag`	Whether to only use the diagonal of the covariance matrix. It seems to work better in practice.
`SAN.nsteps.alloc`	Either a numeric vector or a function of the number of runs giving a sequence of relative lengths of simulated annealing runs.
`SAN.nsteps`	Number of MCMC proposals for all the annealing runs combined.
`SAN.samplesize`	Number of realisations' statistics to obtain for tuning purposes.
`SAN.init.maxedges`	Maximum number of edges expected in network.
`SAN.max.maxedges`	Hard upper bound on the number of edges in the network.
`SAN.prop.weights`	Specifies the proposal distribution used in the SAN Metropolis-Hastings algorithm. Possible choices depending on selected `reference` and `constraints` arguments of the `ergm()` function, but often include `"TNT"` and `"random"`, and the `"default"` is to use the one with the highest priority available. The `TNT` (tie / no tie) option puts roughly equal weight on selecting a dyad with or without a tie as a candidate for toggling, whereas the `random` option puts equal weight on all possible dyads, though the interpretation of `random` may change according to the constraints in place. When no constraints are in place, the default is TNT, which appears to improve Markov chain mixing particularly for networks with a low edge density, as is typical of many realistic social networks.
`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.
`SAN.ignore.finite.offsets`	Whether SAN should ignore (treat as 0) finite offsets.
`term.options`	A list of additional arguments to be passed to term initializers. It can also be set globally via `option(ergm.term=list(...))`.
`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

A list with arguments as components.