collpcm.control {collpcm}

Specify parameters determining the collapsed LPCM model and MCMC fitting run

Description

Specify the number of samples to be collected, burn in to be used, sub-sampling interval, whether variable model jumps are allowed, and whether to run a pilot sample in the initial model.

Usage

 collpcm.control( x = list() , n, d )

Arguments

x

An optional list setting the set up parameters of the model. Any parameters not set in the list will default to the values described below.

n

The number of nodes in the network.

d

The dimension of the latent space for model fitting.

Value

collpcm.control returns a list giving the set up of the problem containing the following items:

G

Initial value of G for the chain.

Gmax

The maximum allowed value of G if doing model search.

Gprior

Log of the prior mass on the number of components G.

xi

Mean of the prior on the model intercept.

psi

Standard deviation of the prior on the model intercept.

gamma

Twice the rate of the Gamma prior on the cluster precision.

delta

Twice the shape of the Gamma prior on the cluster precision.

alpha

The parameter of the Dirichlet prior on group weights.

kappa

The scaling of the prior mean for the cluster centre (in units of cluster precision).

betainit

Initial value given to the intercept for the MCMC run.

Xinit

Initial configuration of latent positions for the MCMC run.

sample

Number of MCMC samples to be stored.

burn

Number of MCMC iterations to discard as burn-in.

interval

Number of iterations at which to sub-sample the chain and store i.e. total iterations post burn-in is sample*interval.

model.search

Logical; if TRUE (default) the model space for G is searched.

pilot

Number of iterations to run as a pilot to adapt the proposal standard deviations for the MCMC chains (in addition to adaptation during burn-in).

sd.beta.prop

Standard deviation of the random walk proposal updating the intercept.

sd.X.prop

Standard deviation of the (possibly multivariate) random walk proposal for an actor's latent position.

gamma.update

Logical; if TRUE (default) then the gamma hyperparameter is updated as part of the MCMC run.

store.sparse

Logical; do a sparse form of storage and don't return or store some of the MCMC run and only keep summary values.

adapt

Logical; if TRUE (default) use an adaptive phase during burn-in to tune the standard deviation of the proposals to get an "optimal" acceptance rate.

adapt.interval

The number of iterations between tweaks of the proposal standard deviations in the adaptation phase.

MKL

Logical; if TRUE (default) compute the maximum Kullback-Liebler configuration of the latent positons from Handcock, Raftery & Tantrum (2007)

verbose

Logical; if TRUE (default) print out progression messages througout the MCMC run and stages of fitting.

Author(s)

Jason Wyse

References

Ryan, C., Wyse, J. and Friel, N. (2017) Bayesian model selection for the latent position cluster model for Social Networks. Network Science, volume 5, 70-91.