dblpm_mcmc: dblpm_mcmc

Description Usage Arguments Value Examples

View source: R/dblpm_mcmc.R

Description

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

Usage

1
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

1
2
3
4
5
6
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.