Description Usage Arguments Details Value See Also
Auxiliary function as user interface for finetuning ERGM GoodnessofFit Evaluation.
The control.gof.ergm
version is intended to be used
with gof.ergm()
specifically and will "inherit" as many control
parameters from ergm
fit as possible().
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  control.gof.formula(
nsim = 100,
MCMC.burnin = 10000,
MCMC.interval = 1000,
MCMC.batch = 0,
MCMC.prop = trim_env(~sparse),
MCMC.prop.weights = "default",
MCMC.prop.args = list(),
MCMC.maxedges = Inf,
MCMC.packagenames = c(),
MCMC.runtime.traceplot = FALSE,
network.output = "network",
seed = NULL,
parallel = 0,
parallel.type = NULL,
parallel.version.check = TRUE,
parallel.inherit.MT = FALSE
)
control.gof.ergm(
nsim = 100,
MCMC.burnin = NULL,
MCMC.interval = NULL,
MCMC.batch = NULL,
MCMC.prop = NULL,
MCMC.prop.weights = NULL,
MCMC.prop.args = NULL,
MCMC.maxedges = NULL,
MCMC.packagenames = NULL,
MCMC.runtime.traceplot = FALSE,
network.output = "network",
seed = NULL,
parallel = 0,
parallel.type = NULL,
parallel.version.check = TRUE,
parallel.inherit.MT = FALSE
)

nsim 
Number of networks to be randomly drawn using Markov chain Monte Carlo. This sample of networks provides the basis for comparing the model to the observed network. 
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.batch 
if not 0 or 
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.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) 
seed 
Seed value (integer) for the random number generator. 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 
This function is only used within a call to the gof
function.
See the usage
section in gof
for details.
A list with arguments as components.
gof
. The control.simulate
function
performs a similar function for simulate.ergm
;
control.ergm
performs a similar function for
ergm
.
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