collpcm.control function

Specify parameters determining the collapsed LPCM model and MCMC fitting run

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.

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.

Returns

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.