Bergm (version 3.0.1)

bbeta: Bayesian beta model

Description

Function to fit the Bayesian beta model using an MCMC algorithm.

Usage

bbeta(formula,  burn.in = 100,  main.iters = 1000,  m.prior = NULL,  sigma.prior = NULL,  sigma.epsilon = NULL, ...)

Arguments

formula

formula; an R formula object, of the form ~ where is a network object and are ergm-terms.

burn.in

count; number of burn-in iterations at the beginning of an MCMC run.

main.iters

count; number of iterations for the MCMC chain excluding burn-in.

m.prior

vector; mean of the multivariate Normal prior. By default set to a vector of 0's.

sigma.prior

variance/covariance matrix for the multivariate Normal prior. By default set to a diagonal matrix with every diagonal entry equal to 100.

sigma.epsilon

variance/covariance matrix for the multivariate Normal proposal. By default set to a diagonal matrix with every diagonal entry equal to 0.0025.

...

additional arguments, to be passed to lower-level functions.

Examples

# Make sure the observed network does not have isolated nodes
set.seed(27)

y <- network(7, directed = FALSE)

post.est <- bbeta(y ~ sociality(base = 0), 
                  main.iters = 3000,
                  sigma.epsilon = diag(1.3, dim(y[,])[1]))

bbeta.output(post.est, plot = TRUE)