Usage
control.ergm(prop.weights = "default", prop.args = NULL,
prop.weights.diss = "default", prop.args.diss = NULL,
nr.maxit = 100,
calc.mcmc.se = TRUE, hessian = TRUE, compress = TRUE,
SAN.burnin=NULL,
maxNumDyadTypes = 1e+06, maxedges = 20000, maxchanges = 1e+06,
maxMPLEsamplesize = 1e+05, MPLEtype=c("glm", "penalized"),
nr.reltol=sqrt(.Machine$double.eps), trace = 0,
steplength = 0.5, sequential=TRUE,
drop = TRUE, force.mcmc = FALSE, check.degeneracy=FALSE,
mcmc.precision = 0.05,
metric = c("lognormal", "Median.Likelihood",
"EF.Likelihood", "naive"),
method = c("BFGS", "Nelder-Mead"),
trustregion = 20, initial.loglik = NULL, loglik.nsteps =
20,
initial.network = NULL,
style = c("Newton-Raphson", "Robbins-Monro",
"Stochastic-Approximation", "Stepping","PILA"),
style.dyn = c("Robbins-Monro", "SPSA", "SPSA2"),
phase1_n = NULL, initial_gain = NULL,
nsubphases = "maxit", niterations = NULL, phase3_n = NULL,
RobMon.phase1n_base = 7, RobMon.phase2n_base = 100, RobMon.phase2sub
= 7, RobMon.init_gain = 0.5, RobMon.phase3n = 500,
stepMCMCsize=100, gridsize=100, dyninterval=1000,
packagenames="ergm", parallel = 0, returnMCMCstats = TRUE)Arguments
prop.weights
Specifies the proposal distribution used in the MCMC
Metropolis-Hastings algorithm. Possible choices are "TNT" or
"random"; the "default" is one of these two, depending on the
constraints in place (as defined
prop.args
An alternative, direct way of specifying additional arguments to proposal.
prop.weights.diss
As prop.weights, for dissolution model.
prop.args.diss
As prop.args, for dissolution model.
nr.maxit
count; The maximum number of iterations in the
Newton-Raphson optimization. Defaults to 1000.
maxit gives the total number of likelihood
function evaluations.
calc.mcmc.se
logical; should the contribution to the
standard errors of the estimator incurred by the MCMC sampling
be computed. Default is TRUE.
hessian
logical; Should the Hessian matrix
of the likelihood function be computed.
Default is TRUE.
compress
logical; Should the matrix of sample statistics
returned be compressed to the set of unique statistics with a
column of frequencies post-pended. This also uses a compression
algorithm in the computation of the maximum psuedo-likelihood
e
SAN.burnin
Burnin used for calling SAN routine. If NULL,
burnin is used.
maxNumDyadTypes
count; The maximum number of unique
pseudolikelihood change statistics to be allowed if compress=TRUE.
It is only relevant in that case.
Default is 10000.
maxedges
Maximum number of edges for which to allocate space.
maxchanges
Maximum number of changes in dynamic network
simulation for which to allocate space.
maxMPLEsamplesize
count; the sample size to use for endogenous
sampling in the pseudolikelihood computation.
Default is 10^11.
MPLEtype
one of "glm" or "penalized"; method to use
for psuedolikelihood. "glm" is the usual
formal logistic regression. "penalized" uses the bias-reduced method
of Firth (1993) as originally implemented by
Meinhard Ploner, Daniela Dunkler, Harry
nr.reltol
Relative convergence tolerance, passed to optimization
routines like optim. See the reltol control argument for the
optim function.
trace
non-negative integer; If positive,
tracing information on the
progress of the optimization is produced. Higher values may
produce more tracing information: for method "L-BFGS-B"
there are six levels of tracing. (To understand
steplength
Multiplier for step length, to make fitting more
stable at the cost of efficiency.
sequential
Should the next iteration of the fit use the last network
sampled as the starting network or always use the initially passed network?
The results should be similar (stochastically), but the
sequential=TRUE option if meansta
drop
logical; Should the degenerate terms in the model be
dropped from the fit?
If statistics occur on the extreme of their range they
correspond to infinite parameter estimates.
Default is TRUE.
force.mcmc
logical; should MCMC maximum likelihood be used? Only
relevant for dyadic independent networks, in which the MLE could be found
using MPLE instead.
check.degeneracy
Logical: Should the diagnostics include a
check for model degeneracy?
mcmc.precision
vector; upper bounds on the precision of the
standard errors induced by the MCMC algorithm.
Defaults to 0.05.
metric
character; The name of the optimization metric
to use. Defaults to "Median.Likelihood". See Hummel et al (2010)
for an explanation of "lognormal" and "naive".
method
character; The name of the optimization method
to use. See optim for the options. The default method
"BFGS" is a quasi-Newton method (also known as a variable
metric algorithm). trustregion
numeric; The maximum amount the algorithm will
allow the approximated likelihood to be increased at a given iteration.
Defaults to 20.
See Snijders (2002) for details.
initial.loglik
Initial value of loglikelihood, if known.
loglik.nsteps
Number of bridges to use to evaluate
dyad-dependent log-likelihood if eval.loglik=TRUE.
initial.network
Initial network for MCMC, if different from observed
network.
style
character; The style of maximum
likelihood estimation to use. The default is optimization of an
MCMC estimate of the log-likelihood. An alternative is to use
a form of stochastic approximation ("Robbins-Monro").
Another alte
style.dyn
character; The style of method of moments estimation
to use. The default is a form of stochastic approximation
("Robbins-Monro"), but it should only be used if it is known
a priori that the derivative of each element of the equili
phase1_n
count; The number of MCMC samples to draw
in Phase 1 of the stochastic approximation algorithm.
Defaults to 7 plus 3 times the number of terms in the model.
See Snijders (2002) for details.
initial_gain
numeric; The initial gain to
Phase 2 of the stochastic approximation algorithm.
Defaults to 0.1.
See Snijders (2002) for details.
nsubphases
count; The number of sub-phases
in Phase 2 of the stochastic approximation algorithm.
Defaults to maxit.
See Snijders (2002) for details.
niterations
count; The number of MCMC samples to draw
in Phase 2 of the stochastic approximation algorithm.
Defaults to 7 plus the number of terms in the model.
See Snijders (2002) for details.
phase3_n
count; The sample size for the MCMC sample
in Phase 3 of the stochastic approximation algorithm.
Defaults to 1000.
See Snijders (2002) for details.
RobMon.phase1n_base
Robbins-Monro control parameter
RobMon.phase2n_base
Robbins-Monro control parameter
RobMon.phase2sub
Robbins-Monro control parameter
RobMon.init_gain
Robbins-Monro control parameter
RobMon.phase3n
Robbins-Monro control parameter
returnMCMCstats
logical; If this is TRUE (the
default) the matrix of change
statistics from the MCMC run is returned as component sample.
This matrix is actually an object of class mcmc and can be
used directly in
stepMCMCsize
MCMC sample size for the preliminary steps of the
"Stepping" style of optimization. This is usually chosen to be smaller
than the final MCMC sample size (which equals MCMCsamplesize).
gridsize
Integer $N$ such that the "Stepping" style of
optimization chooses a step length equal to the largest possible multiple
of $1/N$; see Hummel et al. (2010) for details.
dyninterval
Number of Metropolis-Hastings proposal for each
phase in the dynamic network simulation.
packagenames
Names of packages in which changestatistics are found.
parallel
Number of threads in which to run the sampling.