View source: R/control.simulate.R
control.simulate.ergm  R Documentation 
Auxiliary function as user interface for finetuning 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.
control.simulate.formula.ergm( MCMC.burnin = MCMC.interval * 16, MCMC.interval = 1024, MCMC.prop = trim_env(~sparse), 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), 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), 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, ... )
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 onesided formula
with hints separated by A common and default "hint" is 
MCMC.prop.weights 
Specifies the proposal
distribution used in the MCMC MetropolisHastings algorithm. Possible
choices depending on selected 
MCMC.prop.args 
An alternative, direct way of specifying additional arguments to proposal. 
MCMC.batch 
if not 0 or 
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 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 burnin which would reduce
the difference between the initial values of statistics and their
equilibrium values by a factor of A Geweke diagnostic is then run, after thinning the sample to
If The effective
size of the postburnin sample is computed via Vats, Flegal, and
Jones (2015), 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.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 
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 
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 
parallel.version.check 
Logical: If TRUE, check that the version of

parallel.inherit.MT 
Logical: If TRUE, slave nodes and
processes inherit the 
... 
A dummy argument to catch deprecated or mistyped control parameters. 
MCMC.scale 
For 
This function is only used within a call to the simulate
function. See the usage
section in simulate.ergm
for
details.
A list with arguments as components.
simulate.ergm
, simulate.formula
.
control.ergm
performs a similar function for
ergm
; control.gof
performs a similar function
for gof
.
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