control.ergmm {latentnet}

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

control.ergmm(
  sample.size = 4000,
  burnin = 10000,
  interval = 10,
  threads = 1,
  kl.threads = 1,
  mle.maxit = 100,
  Z.delta = 0.6,
  RE.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 = 0.2,
  accept.all = FALSE,
  store.burnin = FALSE,
  refine.user.start = TRUE
)

ergmm.control(
  sample.size = 4000,
  burnin = 10000,
  interval = 10,
  threads = 1,
  kl.threads = 1,
  mle.maxit = 100,
  Z.delta = 0.6,
  RE.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 = 0.2,
  accept.all = FALSE,
  store.burnin = FALSE,
  refine.user.start = TRUE
)

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.

kl.threads

If greather than 1, uses an experimental parallelized label-switching algorithm. This is not guaranteed to work.

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.

RE.delta

Standard deviation of the proposal for the jump in the individual random effects values, 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, scaling of latent space positions, and a shift in random ffects. 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, RE.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.

refine.user.start

If TRUE, the values passed to ergmm in the user.start argument can be updated by the mode-finding algorithm.

Value

A list with the arguments as components.

See Also

ergmm

Examples

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