Auxiliary function as user interface for finetuning 'stergm' fitting.
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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95  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("GradientDescent"),
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)

init.form, init.diss 
numeric or
Passing 
init.method 
Estimation method
used to acquire initial values for estimation. If 
force.main 
Logical: If TRUE, then force MCMCbased estimation method, even if the exact MLE can be computed via maximum pseudolikelihood estimation. 
MCMC.prop.weights.form, MCMC.prop.weights.diss 
Specifies the method to allocate probabilities of
being proposed to dyads in the formation/dissolution phase. Defaults to 
MCMC.prop.args.form, MCMC.prop.args.diss 
An alternative, direct way of specifying additional arguments to the proposal in the formation/dissolution phase. 
MCMC.init.maxedges 
Maximum number of edges for which to allocate space. 
MCMC.init.maxchanges 
Maximum number of changes in dynamic network simulation for which to allocate space. 
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. 
CMLE.MCMC.burnin 
Maximum number of MetropolisHastings steps per phase (formation and dissolution) per time step used in CMLE fitting. 
CMLE.MCMC.interval 
Number of MetropolisHastings steps between successive draws when running MCMC MLE. 
CMLE.control 
A convenience argument for specifying both

CMLE.control.form, CMLE.control.diss 
Control parameters used to
fit the CMLE for the formation/dissolution ERGM. See

CMLE.NA.impute 
In STERGM CMLE, missing dyads in transitionedto
networks are accommodated using methods of Handcock and Gile (2009),
but a similar approach to transitionedfrom networks requires much
more complex methods that are not, currently, implemented.
By default, no imputation is performed, and the fitting stops with an error if any transitionedfrom networks have missing dyads. 
CMLE.term.check.override 
The method

EGMME.main.method 
Estimation method used to find the Equilibrium Generalized Method of Moments estimator. Currently only "GradientDescent" is implemented. 
EGMME.MCMC.burnin.min, EGMME.MCMC.burnin.max,
EGMME.MCMC.burnin.pval, EGMME.MCMC.burnin.add 
Number of
MetropolisHastings steps per phase (formation and dissolution) per
time step used in EGMME fitting. By default, this is
determined adaptively by keeping track of increments in the Hamming
distance between the transitionedfrom network and the network being
sampled (formation network or dissolution network). Once
To use a fixed number of steps, set both 
SAN.maxit 
When 
SAN.control 
SAN control parameters. See

SA.restarts 
Maximum number of times to restart a failed optimization process. 
SA.burnin 
Number of time steps to advance the starting network before beginning the optimization. 
SA.plot.progress, SA.plot.stats 
Logical: Plot information about
the fit as it proceeds. If Do NOT use these with noninteractive plotting devices
like 
SA.max.plot.points 
If 
SA.init.gain 
Initial gain, the multiplier for the parameter update size. If the process initially goes crazy beyond recovery, lower this value. 
SA.gain.decay 
Gain decay factor. 
SA.runlength 
Number of parameter trials and updates per C run. 
SA.interval.mul 
The number of time steps between updates of the parameters is set to be this times the mean duration of extant ties. 
SA.init.interval 
Initial number of time steps between updates of the parameters. 
SA.min.interval, SA.max.interval 
Upper and lower bounds on the number of time steps between updates of the parameters. 
SA.phase1.tries 
Number of runs trying to find a reasonable parameter and network configuration. 
SA.phase1.jitter 
Initial jitter standard deviation of each parameter. 
SA.phase1.max.q 
Qvalue (false discovery rate) that a gradient estimate must obtain before it is accepted (since sign is what is important). 
SA.phase1.backoff.rat, SA.phase2.backoff.rat 
If the run produces this relative increase in the approximate objective function, it will be backed off. 
SA.phase1.minruns 
Number of runs during Phase 1 for estimating the gradient, before every gradient update. 
SA.phase2.levels.min, SA.phase2.levels.max 
Range of gain levels (subphases) to go through. 
SA.phase2.max.mc.se 
Approximate precision of the estimates that must be attained before stopping. 
SA.phase2.repeats, SA.stepdown.maxn,
SA.stepdown.p, SA.stepdown.ct 
A gain level may be repeated multiple times (up to

SA.stop.p 
At the end of each gain level after the minimum, if the precision is sufficiently high, the relationship between the parameters and the targets is tested for evidence of local nonlinearity. This is the pvalue used. If that test fails to reject, a Phase 3 run is made with the new parameter values, and the estimating equations are tested for difference from 0. If this test fails to reject, the optimization is finished. If either of these tests rejects, at 
SA.keep.oh, SA.keep.min, SA.keep.min.runs 
Parameters controlling how much of optimization history to keep for gradient and covariance estimation. A history record will be kept if it's at least one of the following:

SA.phase2.jitter.mul 
Jitter standard deviation of each parameter is this value times its standard deviation without jitter. 
SA.phase2.maxreljump 
To keep the optimization from "running away" due to, say, a poor gradient estimate building on itself, if a magnitude of change (Mahalanobis distance) in parameters over the course of a run divided by average magnitude of change for recent runs exceeds this, the change is truncated to this amount times the average for recent runs. 
SA.guard.mul 
The multiplier for the range of parameter and statistics values to compute the guard width. 
SA.par.eff.pow 
Because some parameters have much, much greater effects than others,
it improves numerical conditioning and makes estimation more stable
to rescale the kth estimating function by
s_k = (∑_{i=1}^{q} G_{i,k}^2/V_{i,i})^{p/2}, where
G_{i,k} is the estimated gradient of the ith target
statistics with respect to kth parameter. This parameter sets
the value of p: 
SA.robust 
Whether to use robust linear regression (for gradients) and covariance estimation. 
SA.oh.memory 
Absolute maximum number of data points per thread to store in the full optimization history. 
SA.refine 
Method, if any, used to refine the point estimate at the end: "linear" for linear interpolation, "mean" for average, and "none" to use the last value. 
SA.se 
Logical: If TRUE (the default), get an MCMC sample of statistics at
the final estimate and compute the
covariance matrix (and hence standard errors) of the
parameters. This sample is stored and can also be used by

SA.phase3.samplesize.runs 
This many optimization runs will be used to determine whether the optimization has converged and to estimate the standard errors. 
SA.restart.on.err 
Logical: if 
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

MCMC.burnin, MCMC.burnin.mul 
No longer used. See

This function is only used within a call to the stergm
function.
See the usage
section in stergm
for details.
A list with arguments as components.
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: 2738.
Hunter, D. R. and M. S. Handcock (2006), Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15: 565583.
Hummel, R. M., Hunter, D. R., and Handcock, M. S. (2010), A Steplength Algorithm for Fitting ERGMs, Penn State Department of Statistics Technical Report.
stergm
. The control.simulate.stergm
function performs a
similar function for
simulate.stergm
.
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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