control.ergm {ergm} | R Documentation |
Auxiliary function as user interface for fine-tuning 'ergm' fitting.
control.ergm(prop.weights = "default", prop.args = NULL, nr.maxit = 100, calc.mcmc.se = TRUE, hessian = FALSE, compress = TRUE, SAN.burnin=NULL, maxNumDyadTypes = 1e+06, maxedges = 20000, maxchanges = 1e+06, maxMPLEsamplesize = 1e+05, MPLEtype=c("glm", "penalized"), trace = 0, steplength = 0.5, drop = TRUE, force.mcmc = FALSE, check.degeneracy=FALSE, 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 = 0.1, RobMon.phase3n = 500, dyninterval = 1000, packagenames="ergm", parallel = 0, returnMCMCstats = TRUE)
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 "nonobserved" , though not
all values may be used
with all possible constraints (in the ergm function).
|
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
estimate that will dramatically speed it for large networks.
Default is FALSE . |
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 Southworth, and Georg Heinze in the "logistf" package. Default is "glm". |
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 exactly what
these do see the source code for optim : higher levels
give more detail.) |
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). It is attributed to
Broyden, Fletcher, Goldfarb and Shanno. This uses function values
and gradients to build up a picture of the surface to be optimized. |
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 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. |
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 |
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 the CODA package to assess MCMC
convergence. |
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. |
This function is only used within a call to the ergm
function.
See the usage
section in ergm
for details.
A list with arguments as components.
ergm
. The control.simulate
function performs a
similar function for
simulate.ergm
;
control.gof
performs a
similar function for gof
.