Auxiliary function as user interface for fine-tuning 'ergm' fitting.
control.ergm(drop=TRUE, init=NULL,
init.method=NULL,
main.method=c("MCMLE","Robbins-Monro",
"Stochastic-Approximation","Stepping"),
force.main=FALSE,
main.hessian=TRUE,
MPLE.max.dyad.types=1e+6,
MPLE.samplesize=50000,
MPLE.type=c("glm", "penalized"),
MCMC.prop.weights="default", MCMC.prop.args=list(),
MCMC.interval=1024,
MCMC.burnin=MCMC.interval*16,
MCMC.samplesize=1024,
MCMC.effectiveSize=NULL,
MCMC.effectiveSize.damp=10,
MCMC.effectiveSize.maxruns=1000,
MCMC.effectiveSize.base=1/2,
MCMC.effectiveSize.points=5,
MCMC.effectiveSize.order=1,
MCMC.return.stats=TRUE,
MCMC.runtime.traceplot=FALSE,
MCMC.init.maxedges=20000,
MCMC.max.maxedges=Inf,
MCMC.addto.se=TRUE,
MCMC.compress=FALSE,
MCMC.packagenames=c(),
SAN.maxit=10,
SAN.burnin.times=10,
SAN.control=control.san(coef=init,
SAN.prop.weights=MCMC.prop.weights,
SAN.prop.args=MCMC.prop.args,
SAN.init.maxedges=MCMC.init.maxedges,
SAN.burnin=MCMC.burnin * SAN.burnin.times,
SAN.interval=MCMC.interval,
SAN.packagenames=MCMC.packagenames,
MPLE.max.dyad.types=MPLE.max.dyad.types,
parallel=parallel,
parallel.type=parallel.type,
parallel.version.check=parallel.version.check),
MCMLE.termination=c("Hummel", "Hotelling", "precision", "none"),
MCMLE.maxit=20,
MCMLE.conv.min.pval=0.5,
MCMLE.NR.maxit=100,
MCMLE.NR.reltol=sqrt(.Machine$double.eps),
obs.MCMC.samplesize=MCMC.samplesize,
obs.MCMC.interval=MCMC.interval,
obs.MCMC.burnin=MCMC.burnin,
obs.MCMC.burnin.min=obs.MCMC.burnin/10,
obs.MCMC.prop.weights=MCMC.prop.weights, obs.MCMC.prop.args=MCMC.prop.args,
MCMLE.check.degeneracy=FALSE,
MCMLE.MCMC.precision=0.005,
MCMLE.MCMC.max.ESS.frac=0.1,
MCMLE.metric=c("lognormal", "logtaylor",
"Median.Likelihood",
"EF.Likelihood", "naive"),
MCMLE.method=c("BFGS","Nelder-Mead"),
MCMLE.trustregion=20,
MCMLE.dampening=FALSE,
MCMLE.dampening.min.ess=20,
MCMLE.dampening.level=0.1,
MCMLE.steplength.margin=0.05,
MCMLE.steplength=if(is.null(MCMLE.steplength.margin)) 0.5 else 1,
MCMLE.adaptive.trustregion=3,
MCMLE.sequential=TRUE,
MCMLE.density.guard.min=10000,
MCMLE.density.guard=exp(3),
MCMLE.effectiveSize=NULL,
MCMLE.last.boost=4,
MCMLE.Hummel.esteq=TRUE,
MCMLE.Hummel.miss.sample=100,
MCMLE.Hummel.maxit=25,
MCMLE.steplength.min=0.0001,
SA.phase1_n=NULL,
SA.initial_gain=NULL,
SA.nsubphases=4,
SA.niterations=NULL,
SA.phase3_n=NULL,
SA.trustregion=0.5,
RM.phase1n_base=7,
RM.phase2n_base=100,
RM.phase2sub=7,
RM.init_gain=0.5,
RM.phase3n=500,
Step.MCMC.samplesize=100,
Step.maxit=50,
Step.gridsize=100,
CD.nsteps=8,
CD.multiplicity=1,
CD.nsteps.obs=128,
CD.multiplicity.obs=1,
CD.maxit=60,
CD.conv.min.pval=0.5,
CD.NR.maxit=100,
CD.NR.reltol=sqrt(.Machine$double.eps),
CD.metric=c("naive", "lognormal", "logtaylor",
"Median.Likelihood",
"EF.Likelihood"),
CD.method=c("BFGS","Nelder-Mead"),
CD.trustregion=20,
CD.dampening=FALSE,
CD.dampening.min.ess=20,
CD.dampening.level=0.1,
CD.steplength.margin=0.5,
CD.steplength=1,
CD.adaptive.trustregion=3,
CD.adaptive.epsilon=0.01,
CD.Hummel.esteq=TRUE,
CD.Hummel.miss.sample=100,
CD.Hummel.maxit=25,
CD.steplength.min=0.0001,
loglik.control=control.logLik.ergm(),
seed=NULL,
parallel=0,
parallel.type=NULL,
parallel.version.check=TRUE,
…)
Logical: If TRUE, terms whose observed statistic values are at the extremes of their possible ranges are dropped from the fit and their corresponding parameter estimates are set to plus or minus infinity, as appropriate. This is done because maximum likelihood estimates cannot exist when the vector of observed statistic lies on the boundary of the convex hull of possible statistic values.
numeric or NA vector equal in length to the number of
parameters in the model or NULL (the default); the initial
values for the estimation and coefficient offset terms. If NULL
is passed, all of the initial values are computed using the method specified by
control$init.method.
If a numeric vector is given, the elements of the vector are interpreted as follows:
Elements corresponding to
terms enclosed in offset() are used as the fixed offset
coefficients. Note that offset coefficients alone can be more conveniently specified
using ergm argument offset.coef. If both
offset.coef and init arguments are given,
values in offset.coef will take precedence.
Elements that do not correspond to offset terms and are not
NA are used as starting values in the estimation.
Initial values for the elements that are NA are fit
using the method specified by
control$init.method.
Passing control.ergm(init=coef(prev.fit)) can be used to ``resume''
an uncoverged ergm run, but see
enformulate.curved.
A chatacter vector or NULL. The default
method depends on the reference measure used. For the
binary ("Bernoulli") ERGMs, it's
maximum pseudo-likelihood estimation (MPLE). Other valid values include
"zeros" for a 0 vector of appropriate length and
"CD" for contrastive divergence.
Valid initial methods for a given reference can be queried
using ergm.init.methods.
One of "MCMLE" (default),"Robbins-Monro",
"Stochastic-Approximation", or "Stepping". Chooses
the estimation method used to find the MLE.
MCMLE attempts to maximize an approximation to the
log-likelihood function. Robbins-Monro and
Stochastic-Approximation are both
stochastic approximation algorithms that try to
solve the method of moments equation that yields the
MLE in the case of an exponential family model.
Another alternative is a partial stepping algorithm
(Stepping) as in Hummel et al. (2012).
The direct use of the likelihood function has many theoretical
advantages over stochastic approximation, but the choice will
depend on the model and data being fit. See Handcock (2000) and
Hunter and Handcock (2006) for details.
Note that in recent versions of ERGM, the enhancements of
Stepping have been folded into the default MCMLE,
which is able to handle more modeling scenarios.
Logical: If TRUE, then force MCMC-based estimation method, even if the exact MLE can be computed via maximum pseudolikelihood estimation.
Logical: If TRUE, then an approximate Hessian matrix is used in the MCMC-based estimation method.
Maximum number of unique values of
change statistic vectors, which are the predictors in a logistic
regression used to calculate the MPLE. This calculation uses
a compression algorithm that allocates space based on
MPLE.max.dyad.types.
Not currently documented; used in conditional-on-degree version of MPLE.
One of "glm" or "penalized". Chooses method of calculating MPLE. "glm" is the usual formal logistic regression, whereas "penalized" uses the bias-reduced method of Firth (1993) as originally implemented by Meinhard Ploner, Daniela Dunkler, Harry Southworth, and Georg Heinze in the "logistf" package.
Specifies the proposal distribution used in the MCMC
Metropolis-Hastings algorithm. Possible choices depending on
selected reference and constraints arguments of the ergm function,
but often include "TNT" and "random", and the "default" is to use the one with the
highest priority available.
The TNT (tie / no tie) option puts roughly equal weight on selecting a
dyad with or without a tie as a candidate for toggling, whereas the
random option puts equal weight on all possible dyads, though the
interpretation of random may change according to the constraints in
place. When no constraints are in place, the default is TNT, which
appears to improve Markov chain mixing particularly for networks with a
low edge density, as is typical of many realistic social networks.
obs.MCMC.prop.weights, if given separately, specifies the
weights to be used for the constrained MCMC when missing dyads are
present, defaulting to the same as MCMC.prop.weights.
An alternative, direct way
of specifying additional arguments to proposal. obs.MCMC.prop.args, if given separately, specifies the
weights to be used for the constrained MCMC when missing dyads are
present, defaulting to the same as MCMC.prop.args.
Number of proposals between sampled statistics. Increasing interval will reduces the autocorrelation in the sample, and may increase the precision in estimates by reducing MCMC error, at the expense of time. Set the interval higher for larger networks.
Number of proposals before any MCMC sampling is done. It typically is set to a fairly large number.
Number of network statistics, randomly drawn from a given distribution on the set of all networks, returned by the Metropolis-Hastings algorithm. Increasing sample size may increase the precision in the estimates by reducing MCMC error, at the expense of time. Set it higher for larger networks, or when using parallel functionality.
Set MCMLE.effectiveSize to non-NULL value to adaptively
determine the burn-in and the MCMC length needed
to get the specified effective size using the method of Sahlin (2011); 50 is a reasonable value. This
feature is in experimental status until we verify the
coverage of the standard errors.
Logical: If TRUE, return the matrix
of MCMC-sampled network statistics. This matrix should have
MCMC.samplesize rows.
This matrix can be
used directly by the coda package to assess MCMC
convergence.
Logical: If TRUE, plot traceplots of the MCMC sample after every MCMC MLE iteration.
Maximum number of edges
expected in network. Starting at MCMC.init.maxedges, it will
be incremented by a factor of 10 if exceeded during fitting, up to
MCMC.max.maxedges, at which point the process will stop with
an error.
Whether to add the standard errors induced by the MCMC algorithm to the estimates' standard errors.
Logical: If TRUE, the matrix of sample statistics returned is compressed to the set of unique statistics with a column of frequencies post-pended.
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.
Multiplier for SAN.burnin relative to MCMC.burnin. This lets one control the amount of SAN burn-in (arguably, the most important of SAN parameters) without overriding the other SAN.control defaults.
Control arguments to san.
See control.san for details.
The criterion used for terminating MCMLE estimation:
"Hummel"
Terminate when the Hummel step length
is 1 for two consecutive iterations. For the last iteration, the sample size is
boosted by a factor of MCMLE.last.boost. See Hummel et. al. (2012).
Note that this criterion is incompatible with
MCMLE.steplength \(\ne\) 1 or MCMLE.steplength.margin
\(=\) NULL.
"Hotelling"
After every MCMC sample, an autocorrelation-adjusted Hotelling's T^2
test for equality of MCMC-simulated network statistics to observed
is conducted, and if its P-value exceeds MCMLE.conv.min.pval, the
estimation is considered to have converged and finishes. This was the default option
in ergm version 3.1.
"precision"
Terminate when the estimated loss in estimating precision due to
using MCMC standard errors is below the precision bound specified by
MCMLE.MCMC.precision, and the Hummel step length is 1 for two consecutive
iterations. See MCMLE.MCMC.precision for details. This
feature is in experimental status until we verify the coverage of the
standard errors.
Note that this criterion is incompatible with \(\code{MCMLE.steplength}\ne 1\) or \(\code{MCMLE.steplength.margin}=\code{NULL}\).
"none"
Stop after MCMLE.maxit iterations.
Maximum number of times the parameter for the MCMC should be updated by maximizing the MCMC likelihood. At each step the parameter is changed to the values that maximizes the MCMC likelihood based on the current sample.
The P-value used in the Hotelling test for early termination.
The method, maximum number of iterations and relative tolerance to use within the optim rountine
in the MLE optimization. Note that by default, ergm uses trust, and falls back
to optim only when trust fails.
Sample size, burnin, and interval parameters for the MCMC sampling used when unobserved data are present in the estimation routine.
Logical: If TRUE, employ a check for model degeneracy.
MCMLE.MCMC.precision is a vector of upper bounds on the standard errors induced by the MCMC algorithm, expressed as a percentage of the total standard error. The MCMLE algorithm will terminate when the MCMC standard errors are below the
precision bound, and the Hummel step length is 1 for two consecutive iterations. This is an experimental feature.
If effective sample size is used (see MCMC.effectiveSize), then ergm may increase the target ESS
to reduce the MCMC standard error.
Method to calculate the loglikelihood approximation. See Hummel et al (2010) for an explanation of "lognormal" and "naive".
Deprecated. By default, ergm uses trust, and falls back
to optim with Nelder-Mead method when trust fails.
Maximum increase the algorithm will allow for the approximated likelihood at a given iteration. See Snijders (2002) for details.
Note that not all metrics abide by it.
(logical) Should likelihood dampening be used?
The effective sample size below which dampening is used.
The proportional distance from boundary of the convex hull move.
The extra margin required for a Hummel step to count as being inside
the convex hull of the sample.
Set this to 0 if the step length gets stuck at the same value over
several iteraions. Set it to NULL to use fixed step
length. Note that this parameter is required to be non-NULL
for MCMLE termination using Hummel or precision criteria.
Multiplier for step length, which may
(for values less than one)
make fitting more stable at the cost of computational efficiency.
Can be set to "adaptive"; see
MCMLE.adaptive.trustregion.
If MCMLE.steplength.margin is not NULL, the step
length will be set using the algorithm of Hummel et al. (2010). In
that case, it will serve as the maximum step length
considered. However, setting it to anything other than 1 will
preclude using Hummel or precision as termination criteria.
Maximum increase the algorithm
will allow for the approximated loglikelihood at a given
iteration when MCMLE.steplength="adaptive".
Logical: If TRUE,
the next iteration of the fit uses the last network
sampled as the starting network. If FALSE, always use the
initially passed network.
The results should be similar (stochastically), but the
TRUE option may help if the target.stats
in the ergm function are far from the
initial network.
A simple heuristic to stop optimization if it finds itself in an
overly dense region, which usually indicates ERGM degeneracy: if the
sampler encounters a network configuration that has more than
MCMLE.density.guard.min edges and whose number of edges is
exceeds the observed network by more than
MCMLE.density.guard, the optimization process will be stopped
with an error.
For the Hummel termination criterion, increase the MCMC sample size of the last iteration by this factor.
For curved ERGMs, should the estimating function values be used to compute the Hummel step length? This allows the Hummel stepping algorithm converge when some sufficient statistics are at 0.
Stops MCMLE estimation when the step length gets stuck below this minimum value.
In fitting the missing data MLE, the rules for step length become more complicated. In short, it is necessary for all points in the constrained sample to be in the convex hull of the unconstrained (though they may be on the border); and it is necessary for their centroid to be in its interior. This requires checking a large number of points against whether they are in the convex hull, so to speed up the procedure, a sample is taken of the points most likely to be outside it. This parameter specifies the sample size.
Maximum number of iterations in searching for the best step length.
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 to Phase 2 of the stochastic approximation algorithm. See Snijders (2002) for details.
Number of sub-phases
in Phase 2 of the stochastic approximation algorithm.
Defaults to MCMLE.maxit.
See Snijders (2002) for details.
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.
Sample size for the MCMC sample in Phase 3 of the stochastic approximation algorithm. See Snijders (2002) for details.
The trust region parameter for the likelihood functions, used in the stochastic approximation algorithm.
The Robbins-Monro control parameters are not yet documented.
MCMC sample size for the preliminary steps of the
"Stepping" method of optimization. This is usually chosen to be smaller
than the final MCMC sample size (which equals MCMC.samplesize).
See Hummel et al. (2012) for details.
Maximum number of iterations (steps) allowed by the "Stepping" method.
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. (2012) for details.
Main settings for contrastive divergence to obtain initial values for the
estimation: respectively, the number of Metropolis--Hastings steps to take before
reverting to the starting value and the number of tentative
proposals per step. Computational experiments indicate that
increasing CD.multiplicity improves the estimate faster than
increasing CD.nsteps --- up to a point --- but it also samples from the wrong
distribution, in the sense that while as
CD.nsteps\(\rightarrow\infty\), the CD estimate approaches
the MLE, this is not the case for CD.multiplicity.
In practice, MPLE, when available, usually outperforms CD for even a very high
CD.nsteps (which is, in turn, not very stable), so CD is useful primarily when
MPLE is not available. This feature is to be considered
experimental and in flux.
The default values have been set experimentally, providing a reasonably stable, if not great, starting values.
When there are missing dyads, CD.nsteps and
CD.multiplicity must be set to a relatively high value, as
the network passed is not necessarily a good start for
CD. Therefore, these settings are in effect if there are missing dyads in the observed
network, using a higher default number of steps.
Miscellaneous tuning parameters of the CD sampler and
optimizer. These have the same meaning as their MCMC.*
counterparts.
Note that only the Hotelling's stopping criterion is implemented for CD.
Seed value (integer) for the random number generator.
See set.seed
Number of threads in which to run the sampling. Defaults to 0 (no parallelism). See the entry on parallel processing for details and troubleshooting.
API to use for parallel
processing. Supported values are "MPI" and
"PSOCK". Defaults to using the parallel package with PSOCK clusters. See
ergm-parallel
Logical: If TRUE, check that the version of
ergm running on the slave nodes is the
same as that running on the master node.
Additional arguments, passed to other functions This argument is helpful because it collects any control parameters that have been deprecated; a warning message is printed in case of deprecated arguments.
A list with arguments as components.
This function is only used within a call to the ergm function.
See the usage section in ergm for details.
Snijders, T.A.B. (2002), Markov Chain Monte Carlo Estimation of Exponential Random Graph Models. Journal of Social Structure. Available from http://www.cmu.edu/joss/content/articles/volume3/Snijders.pdf.
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. (2012), Improving Simulation-Based Algorithms for Fitting ERGMs, Journal of Computational and Graphical Statistics, 21: 920-939.
Kristoffer Sahlin. Estimating convergence of Markov chain Monte Carlo simulations. Master's Thesis. Stockholm University, 2011. http://www2.math.su.se/matstat/reports/master/2011/rep2/report.pdf
ergm. The control.simulate
function performs a
similar function for
simulate.ergm;
control.gof performs a
similar function for gof.