control.san: Auxiliary for Controlling SAN

In ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks

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.prop = trim_env(~sparse),
  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,
  parallel.inherit.MT = FALSE
)

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. In either case, during subsequent runs, it is 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.prop	Specifies the proposal (directly) and/or a series of "hints" about the structure of the model being sampled. The specification is in the form of a one-sided formula with hints separated by + operations. If the LHS exists and is a string, the proposal to be used is selected directly. A common and default "hint" is ~sparse, indicating that the network is sparse and that the sample should put roughly equal weight on selecting a dyad with or without a tie as a candidate for toggling.
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.
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. See ? term.options.
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.
parallel.inherit.MT	Logical: If TRUE, slave nodes and processes inherit the set.MT_terms() setting.

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.

See Also

san

ergm documentation built on May 31, 2023, 8:04 p.m.