ergmm.control {latentnet}R Documentation

Auxiliary for Controlling ERGMM Fitting

Description

Auxiliary function as user interface for ergmm fitting. Typically only used when calling ergmm. It is used to set parameters that affect the sampling but do not affect the posterior distribution.

Usage

ergmm.control(sample.size=4000,
              burnin=10000,
              interval=10,
              threads=1,
              mle.maxit=100,
              Z.delta=0.6,
              group.deltas=0.4,
              pilot.runs=4,
              pilot.factor=0.8,
              pilot.discard.first=0.5,
              target.acc.rate=0.234,
              backoff.threshold=0.05,
              backoff.factor=0.1,
              accept.all=FALSE,
              store.burnin=FALSE)

Arguments

sample.size The number of draws to be taken from the posterior distribution.
burnin The number of initial MCMC iterations to be discarded.
interval The number of iterations between consecutive draws.
threads The number of chains to run. If greater than 1, package snowFT is used to take advantage of any multiprocessing or distributed computing capabilities that may be available. Currently, only PVM (via rpvm) has been tested. Note, also, that PVM daemon needs to be started before the package is loaded.
mle.maxit Maximum number of iterations for computing the starting values, posterior modes, MLEs, MKL estimates, etc..
Z.delta Standard deviation of the proposal for the jump in the individual latent space position, or its starting value for the tuner.
group.deltas A scalar, a vector, or a matrix of an appropriate size, giving the initial proposal structure for the ``group proposal'' of a jump in covariate coefficients and scaling of latent space positions. If a matrix of an appropriate size is given, it is used as a matrix of coefficients for a correlated proposal. If a vector is given, an independent proposal is used with the corresponding elements being proposal standard deviations. If a scalar is given, it is used as a multiplier for an initial heuristic for the proposal structure. It is usually best to leave this argument alone and let the adaptive sampling be used.
pilot.runs Number of pilot runs into which to split the burn-in period. After each pilot run, the proposal standard deviations and coefficients Z.delta and group.deltas are reevaluated. If set to 0, disables adaptive sampling, and only makes a single burn-in run.
pilot.factor Initial value for the factor by which the coefficients gotten by a Choletsky decomposition of the pilot sample covariance matrix are multiplied.
pilot.discard.first Proportion of draws from the pilot run to discard for estimating acceptance rate and group proposal covariance.
target.acc.rate Taget acceptance rate for the proposals used. After a pilot run, the proposal variances are adjusted upward if the acceptance rate is above this, and downward if below.
backoff.threshold If a pilot run's acceptance rate is below this, redo it with drastically reduced proposal standard deviation. Set to 0 to disable this behavior.
backoff.factor Factor by which to multiply the relevant proposal standard deviation if its acceptance rate fell below the backoff threshold.
accept.all Forces all proposals to be accepted unconditionally. Use only in debugging proposal distributions!
store.burnin If TRUE, the samples from the burnin are also stored and returned, to be used in MCMC diagnostics.

Value

A list with the arguments as components.

See Also

ergmm

Examples

data(sampson)
## Shorter run than default.
samp.fit<-ergmm(samplike~latent(d=2,G=3),
control=ergmm.control(burnin=1000,sample.size= 2000,interval=5))

[Package latentnet version 2.1-1 Index]