dblpm_mcmc: dblpm_mcmc

In IrishDirectorates: A Dynamic Bipartite Latent Space Model to Analyse Irish Companies' Boards from 2003 to 2013

Description

Runs the Metropolis-within-Gibbs sampler on the given Dynamic Bipartite Latent Position Model (dblpm network).

Usage

dblpm_mcmc(network, niter, burnin, thin, x_var, w_var, gamma_var, beta_var, verbose = T)

Arguments

network	A list identifying a dblpm network.
niter	Number of iterations after thinning and burnin.
burnin	Number of iterations to be discaded before starting the count for niter. The burnin iterations are not thinned.
thin	After burnin, keep one sampled observation every thin and discard the rest.
x_var	Proposal variance for the positions of sender nodes.
w_var	Proposal variance for the positions of receiver nodes.
gamma_var	Proposal variance for the intercept gamma.
beta_var	Proposal variance for the intercept beta.
verbose	true or false indicating whether a lengthy output should be printed out.

Value

computing_time	Number of seconds required for the sampling process.
samples	Sampled values for each of the model parameters.
tail	dblpm network sampled in the last iteration.

Examples

data(IrishDirectoratesFit)
IrishDirectoratesFit <- dblpm_mcmc(network = IrishDirectoratesFit$tail, 
                  niter = 3, burnin = 6, thin = 3, 
                  x_var = 4.75, w_var = 0.25, gamma_var = 1.825, beta_var = 0.2175, 
                  verbose = TRUE)
# to replicate the results of the paper: niter = 10000, burnin = 500000, thin = 50

IrishDirectorates documentation built on Aug. 29, 2019, 9:03 a.m.