control.simulate.ergm: Auxiliary for Controlling ERGM Simulation

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

Auxiliary for Controlling ERGM Simulation

Description

Auxiliary function as user interface for fine-tuning ERGM simulation. control.simulate, control.simulate.formula, and control.simulate.formula.ergm are all aliases for the same function.

While the others supply a full set of simulation settings, control.simulate.ergm when passed as a control parameter to simulate.ergm() allows some settings to be inherited from the ERGM stimation while overriding others.

Usage

control.simulate.formula.ergm(
  MCMC.burnin = MCMC.interval * 16,
  MCMC.interval = 1024,
  MCMC.prop = trim_env(~sparse + .triadic),
  MCMC.prop.weights = "default",
  MCMC.prop.args = list(),
  MCMC.batch = NULL,
  MCMC.effectiveSize = NULL,
  MCMC.effectiveSize.damp = 10,
  MCMC.effectiveSize.maxruns = 1000,
  MCMC.effectiveSize.burnin.pval = 0.2,
  MCMC.effectiveSize.burnin.min = 0.05,
  MCMC.effectiveSize.burnin.max = 0.5,
  MCMC.effectiveSize.burnin.nmin = 16,
  MCMC.effectiveSize.burnin.nmax = 128,
  MCMC.effectiveSize.burnin.PC = FALSE,
  MCMC.effectiveSize.burnin.scl = 1024,
  MCMC.effectiveSize.order.max = NULL,
  MCMC.maxedges = Inf,
  MCMC.packagenames = c(),
  MCMC.runtime.traceplot = FALSE,
  network.output = "network",
  term.options = NULL,
  parallel = 0,
  parallel.type = NULL,
  parallel.version.check = TRUE,
  parallel.inherit.MT = FALSE,
  ...
)

control.simulate(
  MCMC.burnin = MCMC.interval * 16,
  MCMC.interval = 1024,
  MCMC.prop = trim_env(~sparse + .triadic),
  MCMC.prop.weights = "default",
  MCMC.prop.args = list(),
  MCMC.batch = NULL,
  MCMC.effectiveSize = NULL,
  MCMC.effectiveSize.damp = 10,
  MCMC.effectiveSize.maxruns = 1000,
  MCMC.effectiveSize.burnin.pval = 0.2,
  MCMC.effectiveSize.burnin.min = 0.05,
  MCMC.effectiveSize.burnin.max = 0.5,
  MCMC.effectiveSize.burnin.nmin = 16,
  MCMC.effectiveSize.burnin.nmax = 128,
  MCMC.effectiveSize.burnin.PC = FALSE,
  MCMC.effectiveSize.burnin.scl = 1024,
  MCMC.effectiveSize.order.max = NULL,
  MCMC.maxedges = Inf,
  MCMC.packagenames = c(),
  MCMC.runtime.traceplot = FALSE,
  network.output = "network",
  term.options = NULL,
  parallel = 0,
  parallel.type = NULL,
  parallel.version.check = TRUE,
  parallel.inherit.MT = FALSE,
  ...
)

control.simulate.formula(
  MCMC.burnin = MCMC.interval * 16,
  MCMC.interval = 1024,
  MCMC.prop = trim_env(~sparse + .triadic),
  MCMC.prop.weights = "default",
  MCMC.prop.args = list(),
  MCMC.batch = NULL,
  MCMC.effectiveSize = NULL,
  MCMC.effectiveSize.damp = 10,
  MCMC.effectiveSize.maxruns = 1000,
  MCMC.effectiveSize.burnin.pval = 0.2,
  MCMC.effectiveSize.burnin.min = 0.05,
  MCMC.effectiveSize.burnin.max = 0.5,
  MCMC.effectiveSize.burnin.nmin = 16,
  MCMC.effectiveSize.burnin.nmax = 128,
  MCMC.effectiveSize.burnin.PC = FALSE,
  MCMC.effectiveSize.burnin.scl = 1024,
  MCMC.effectiveSize.order.max = NULL,
  MCMC.maxedges = Inf,
  MCMC.packagenames = c(),
  MCMC.runtime.traceplot = FALSE,
  network.output = "network",
  term.options = NULL,
  parallel = 0,
  parallel.type = NULL,
  parallel.version.check = TRUE,
  parallel.inherit.MT = FALSE,
  ...
)

control.simulate.ergm(
  MCMC.burnin = NULL,
  MCMC.interval = NULL,
  MCMC.scale = 1,
  MCMC.prop = NULL,
  MCMC.prop.weights = NULL,
  MCMC.prop.args = NULL,
  MCMC.batch = NULL,
  MCMC.effectiveSize = NULL,
  MCMC.effectiveSize.damp = 10,
  MCMC.effectiveSize.maxruns = 1000,
  MCMC.effectiveSize.burnin.pval = 0.2,
  MCMC.effectiveSize.burnin.min = 0.05,
  MCMC.effectiveSize.burnin.max = 0.5,
  MCMC.effectiveSize.burnin.nmin = 16,
  MCMC.effectiveSize.burnin.nmax = 128,
  MCMC.effectiveSize.burnin.PC = FALSE,
  MCMC.effectiveSize.burnin.scl = 1024,
  MCMC.effectiveSize.order.max = NULL,
  MCMC.maxedges = Inf,
  MCMC.packagenames = NULL,
  MCMC.runtime.traceplot = FALSE,
  network.output = "network",
  term.options = NULL,
  parallel = 0,
  parallel.type = NULL,
  parallel.version.check = TRUE,
  parallel.inherit.MT = FALSE,
  ...
)

Arguments

MCMC.burnin	Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number.
MCMC.interval	Number of proposals between sampled statistics.
MCMC.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.
MCMC.prop.weights	Specifies the proposal distribution used in the MCMC 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.
MCMC.prop.args	An alternative, direct way of specifying additional arguments to proposal.
MCMC.batch	if not 0 or NULL, sample about this many networks per call to the lower-level code; this can be useful if ⁠output=⁠ is a function, where it can be used to limit the number of networks held in memory at any given time.
MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max	Set MCMC.effectiveSize to a non-NULL value to adaptively determine the burn-in and the MCMC length needed to get the specified effective size; 50 is a reasonable value. In the adaptive MCMC mode, MCMC is run forward repeatedly (MCMC.samplesize*MCMC.interval steps, up to MCMC.effectiveSize.maxruns times) until the target effective sample size is reached or exceeded. After each run, the returned statistics are mapped to the estimating function scale, then an exponential decay model is fit to the scaled statistics to find that burn-in which would reduce the difference between the initial values of statistics and their equilibrium values by a factor of MCMC.effectiveSize.burnin.scl of what it initially was, bounded by MCMC.effectiveSize.min and MCMC.effectiveSize.max as proportions of sample size. If the best-fitting decay exceeds MCMC.effectiveSize.max, the exponential model is considered to be unsuitable and MCMC.effectiveSize.min is used. A Geweke diagnostic is then run, after thinning the sample to MCMC.effectiveSize.burnin.nmax. If this Geweke diagnostic produces a p-value higher than MCMC.effectiveSize.burnin.pval, it is accepted. If MCMC.effectiveSize.burnin.PC>0, instead of using the full sample for burn-in estimation, at most this many principal components are used instead. The effective size of the post-burn-in sample is computed via \insertCiteVaFl15m;textualergm, and compared to the target effective size. If it is not matched, the MCMC run is resumed, with the additional draws needed linearly extrapolated but weighted in favor of the baseline MCMC.samplesize by the weighting factor MCMC.effectiveSize.damp (higher = less damping). Lastly, if after an MCMC run, the number of samples equals or exceeds 2*MCMC.samplesize, the chain will be thinned by 2 until it falls below that, while doubling MCMC.interval. MCMC.effectiveSize.order.max can be used to set the order of the AR model used to estimate the effective sample size and the variance for the Geweke diagnostic. Lastly, if MCMC.effectiveSize is a matrix, say, W, it will be treated as a target precision (inverse-variance) matrix. If V is the sample covariance matrix, the target effective size n_{\text{eff}} will be set such that V/n_{\text{eff}} is close to W in magnitude, specifically that \operatorname{tr}((V/n_{\text{eff}})W)/p\approx 1.
MCMC.maxedges	The maximum number of edges that may occur during the MCMC sampling. If this number is exceeded at any time, sampling is stopped immediately.
MCMC.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.
MCMC.runtime.traceplot	Logical: If TRUE, plot traceplots of the MCMC sample.
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)
term.options	A list of additional arguments to be passed to term initializers. See ? term.options.
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.
...	A dummy argument to catch deprecated or mistyped control parameters.
MCMC.scale	For control.simulate.ergm() inheriting MCMC.burnin and MCMC.interval from the ergm fit, the multiplier for the inherited values. This can be useful because MCMC parameters used in the fit are tuned to generate a specific effective sample size for the sufficient statistic in a large MCMC sample, so the inherited values might not generate independent realisations.

Details

This function is only used within a call to the ERGM simulate() function. See the Usage section in simulate.ergm() for details.

Value

A list with arguments as components.

See Also

simulate.ergm(), simulate.formula(). control.ergm() performs a similar function for ergm(); control.gof() performs a similar function for gof().

ergm documentation built on Oct. 7, 2024, 5:08 p.m.