control.ergm: Auxiliary for Controlling ERGM Fitting

Description

Auxiliary function as user interface for fine-tuning 'ergm' fitting.

Usage

control.ergm(prop.weights = "default", prop.args = NULL,
             nr.maxit = 100, calc.mcmc.se = TRUE, hessian = FALSE, compress = FALSE,
             maxNumDyadTypes = 10000, maxedges = 20000, maxchanges = 1e+06,
             maxMPLEsamplesize = 100000, MPLEtype=c("glm", "penalized"), trace = 0,
             steplength = 0.5, drop = TRUE, force.mcmc = FALSE, check.degeneracy=TRUE, mcmc.precision =
             0.05, metric = c("Likelihood", "raw"), method = c("BFGS", "Nelder-Mead"),
             trustregion = 20, initial.loglik = NULL, initial.network = NULL,
             style = c("Newton-Raphson", "Robbins-Monro",
             "Stochastic-Approximation"), phase1_n = NULL, initial_gain = NULL,
             nsubphases = "maxit", niterations = NULL, phase3_n = NULL,
             RobMon.phase1n_base = 7, RobMon.phase2n_base = 7, RobMon.phase2sub
             = 4, RobMon.init_gain = 0.4, RobMon.phase3n = 500, dyninterval =
             1000, packagenames="ergm", parallel = 0, returnMCMCstats = TRUE)

Arguments

prop.weights

Specifies the method to allocate probabilities of being proposed to dyads. Defaults to "default", which picks a reasonable default for the specified constraint. Possible values are "TNT", "random", and

prop.args

An alternative, direct way of specifying additional arguments to proposal.

nr.maxit

count; The maximum number of iterations in the Newton-Raphson optimization. Defaults to 100. 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

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 determines the sample size to use for endogenous sampling in the pseudolikelihood computation. The sample size is the number of dyads in a network with maxMPLEsamplesize nodes. Default is 100000.

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

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.

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 "Likelihood".

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 Boer, Huisman, Snijders, and Zeggelink (2003) for details.

initial.loglik

Initial value of loglikelihood, if known.

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"). The direct u

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 Boer, Huisman, Snijders, and Zeggelink (2003) for details.

initial_gain

numeric; The initial gain to Phase 2 of the stochastic approximation algorithm. Defaults to 0.1. See Boer, Huisman, Snijders, and Zeggelink (2003) for details.

nsubphases

count; The number of sub-phases in Phase 2 of the stochastic approximation algorithm. Defaults to maxit. See Boer, Huisman, Snijders, and Zeggelink (2003) 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 Boer, Huisman, Snijders, and Zeggelink (2003) for details.

phase3_n

count; The sample size for the MCMC sample in Phase 3 of the stochastic approximation algorithm. Defaults to 1000. See Boer, Huisman, Snijders, and Zeggelink (2003) for details.

RobMon.phase1n_base