control.stergm: Auxiliary for Controlling Separable Temporal ERGM Fitting

Description

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

Usage

control.stergm(init.form=NULL,
               init.diss=NULL,
               init.method=NULL,
               force.main = FALSE,               
               MCMC.prop.weights.form="default",MCMC.prop.args.form=NULL,
               MCMC.prop.weights.diss="default",MCMC.prop.args.diss=NULL,
               MCMC.init.maxedges=20000,
               MCMC.init.maxchanges=20000,
               MCMC.packagenames=c(),
               CMLE.MCMC.burnin = 1024*16,
               CMLE.MCMC.interval = 1024,
               CMLE.control=NULL,
               CMLE.control.form=control.ergm(init=init.form,
                 MCMC.burnin=CMLE.MCMC.burnin,
                 MCMC.interval=CMLE.MCMC.interval,
                 MCMC.prop.weights=MCMC.prop.weights.form,
                 MCMC.prop.args=MCMC.prop.args.form,
                 MCMC.init.maxedges=MCMC.init.maxedges,
                 MCMC.packagenames=MCMC.packagenames,
                 parallel=parallel,
                 parallel.type=parallel.type,
                 parallel.version.check=parallel.version.check,
                 force.main=force.main),
               CMLE.control.diss=control.ergm(init=init.diss,
                 MCMC.burnin=CMLE.MCMC.burnin,
                 MCMC.interval=CMLE.MCMC.interval,
                 MCMC.prop.weights=MCMC.prop.weights.diss,
                 MCMC.prop.args=MCMC.prop.args.diss,
                 MCMC.init.maxedges=MCMC.init.maxedges,
                 MCMC.packagenames=MCMC.packagenames,
                 parallel=parallel,
                 parallel.type=parallel.type,
                 parallel.version.check=parallel.version.check,
                 force.main=force.main),
               CMLE.NA.impute=c(),
               CMLE.term.check.override=FALSE,
               EGMME.main.method=c("Gradient-Descent"),
               EGMME.MCMC.burnin.min=1000,
               EGMME.MCMC.burnin.max=100000,
               EGMME.MCMC.burnin.pval=0.5,
               EGMME.MCMC.burnin.add=1,
               MCMC.burnin=NULL, MCMC.burnin.mul=NULL,
               SAN.maxit=10,
               SAN.control=control.san(coef=init.form,
                 SAN.prop.weights=MCMC.prop.weights.form,
                 SAN.prop.args=MCMC.prop.args.form,
                 SAN.init.maxedges=MCMC.init.maxedges,
                 SAN.burnin=round(sqrt(EGMME.MCMC.burnin.min * EGMME.MCMC.burnin.max)),
                 SAN.packagenames=MCMC.packagenames,
                 parallel=parallel,
                 parallel.type=parallel.type,
                 parallel.version.check=parallel.version.check),
               SA.restarts=10,
               SA.burnin=1000,
               SA.plot.progress=FALSE,
               SA.max.plot.points=400,
               SA.plot.stats=FALSE,
               SA.init.gain=0.1,
               SA.gain.decay=0.5, 
               SA.runlength=25, 
               SA.interval.mul=2, 
               SA.init.interval=500, 
               SA.min.interval=20, 
               SA.max.interval=500, 
               SA.phase1.minruns=4, 
               SA.phase1.tries=20, 
               SA.phase1.jitter=0.1, 
               SA.phase1.max.q=0.1, 
               SA.phase1.backoff.rat=1.05, 
               SA.phase2.levels.max=40, 
               SA.phase2.levels.min=4, 
               SA.phase2.max.mc.se=0.001, 
               SA.phase2.repeats=400, 
               SA.stepdown.maxn=200, 
               SA.stepdown.p=0.05, 
               SA.stop.p=0.1, 
               SA.stepdown.ct=5, 
               SA.phase2.backoff.rat=1.1, 
               SA.keep.oh=0.5, 
               SA.keep.min.runs=8, 
               SA.keep.min=0, 
               SA.phase2.jitter.mul=0.2, 
               SA.phase2.maxreljump=4, 
               SA.guard.mul = 4,
               SA.par.eff.pow = 1,
               SA.robust = FALSE,
               SA.oh.memory = 100000,
               SA.refine=c("mean","linear","none"), 
               SA.se=TRUE, 
               SA.phase3.samplesize.runs=10, 
               SA.restart.on.err=TRUE, 
               seed=NULL,
               parallel=0,
               parallel.type=NULL,
               parallel.version.check=TRUE)

Arguments

init.form, init.diss

numeric or NA vector equal in length to the number of parameters in the formation/dissolution model or NULL (the default); the initial values for the estimation and coefficient offset terms. If NULL is pa

Value

A list with arguments as components.

item

init.method
force.main
MCMC.prop.weights.form, MCMC.prop.weights.diss
MCMC.prop.args.form, MCMC.prop.args.diss
MCMC.init.maxedges
MCMC.init.maxchanges
MCMC.packagenames
CMLE.MCMC.burnin
CMLE.MCMC.interval
CMLE.control
CMLE.control.form, CMLE.control.diss
CMLE.NA.impute
CMLE.term.check.override
EGMME.main.method
EGMME.MCMC.burnin.min, EGMME.MCMC.burnin.max, EGMME.MCMC.burnin.pval, EGMME.MCMC.burnin.add
SAN.maxit
SAN.control
SA.restarts
SA.burnin
SA.plot.progress, SA.plot.stats
SA.max.plot.points
SA.init.gain
SA.gain.decay
SA.runlength
SA.interval.mul
SA.init.interval
SA.min.interval, SA.max.interval
SA.phase1.tries
SA.phase1.jitter
SA.phase1.max.q
SA.phase1.backoff.rat, SA.phase2.backoff.rat
SA.phase1.minruns
SA.phase2.levels.min, SA.phase2.levels.max
SA.phase2.max.mc.se
SA.phase2.repeats, SA.stepdown.maxn, SA.stepdown.p, SA.stepdown.ct
SA.stop.p
SA.keep.oh, SA.keep.min, SA.keep.min.runs
Among the last SA.keep.min (a count) records.
From the last SA.keep.min.runs (a count) optimization runs.
SA.phase2.jitter.mul
SA.phase2.maxreljump
SA.guard.mul
SA.par.eff.pow
SA.robust
SA.oh.memory
SA.refine
SA.se
SA.phase3.samplesize.runs
SA.restart.on.err
seed
parallel
parallel.type
parallel.version.check
MCMC.burnin, MCMC.burnin.mul

code

CMLE.MCMC.interval

itemize

Among the lastSA.keep.oh(a fraction) of all runs.

eqn

$p$

link

parallel processing

Details

This function is only used within a call to the stergm function. See the usage section in stergm for details.

References

Boer, P., Huisman, M., Snijders, T.A.B., and Zeggelink, E.P.H. (2003), StOCNET User\'s Manual. Version 1.4.
Firth (1993), Bias Reduction in Maximum Likelihood Estimates. Biometrika, 80: 27-38.
Hunter, D. R. and M. S. Handcock (2006), Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15: 565-583.
Hummel, R. M., Hunter, D. R., and Handcock, M. S. (2010), A Steplength Algorithm for Fitting ERGMs, Penn State Department of Statistics Technical Report.