collpcm (version 1.3)

collpcm.fit: Fit a latent position cluster network model with model search

Description

collpcm.fit is used to fit the latent position cluster model with uncertainty in the number of clusters incorporated. A posterior distribution for the number of clusters is estimated.

Usage

collpcm.fit( Y , d = 2, G = NULL, Gmax = NULL, control = list(), Xref = NA )

Value

collpcm.fit returns an object of class collpcm that is a list. The list will have the following slots.

call

The values of each of the arguments used in the model fitting MCMC run.

sample

A list containing the samples from the MCMC run.

Gpost

Estimated posterior distribution of the number of groups/clusters.

Xpostmean

Estimated posterior mean from sampled latent positions.

XpostMKL

MKL posterior latent positions as described in Handcock, Raftery & Tantrum (2007).

Gslot

An indexing vector for the lists of posterior mean and MKL positions.

acceptance.rates

Acceptance rates for different moves of MCMC algorithm.

adapted.sd.prop

The standard deviations of the proposal distributions after the adaptation phase.

timings

A list of timings for each part of the algorithm.

Arguments

Y

A network object containing the network in question.

d

The dimension of the latent position to represent each node in the network (defaults to 2).

G

Give the initial number of groups for the algorithm.

Gmax

Give the maximum allowed number of groups if doing model search.

control

List giving the set up of the algorithm (see collpcm.control)

Xref

Optional latent positions to be used as a reference configuration for the Procrustes rotations.

Author

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.

Handcock, M. S., Raftery, A. E. and Tantrum, J. (2007). Model-Based Clustering for Social Networks. Journal of the Royal Statistical Society, Series A, Vol. 170, 301-354 <doi: 10.1111/j.1467-985X.2007.00471.x>

See Also

collpcm.control

Examples

# load the Monks data
data(Monks)

# run the model printing run updates to screen
# this is an illustrative example (it should be run for much longer)
z <- collpcm.fit( Monks, G=3, d=2, 
control=list( verbose=TRUE, sample=2500, interval=1, burn=500 ) )

# plot of the collpcm object
plot( z )