Bergm (version 5.0.7)

bergm: Parameter estimation for Bayesian ERGMs

Description

Function to fit Bayesian exponential random graphs models using the approximate exchange algorithm.

Usage

bergm(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  burn.in = 100,
  main.iters = 1000,
  aux.iters = 1000,
  nchains = NULL,
  gamma = 0.5,
  V.proposal = 0.0025,
  startVals = NULL,
  offset.coef = NULL,
  ...
)

Arguments

formula

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

prior.mean

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

prior.sigma

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

burn.in

count; number of burn-in iterations for every chain of the population.

main.iters

count; number of iterations for every chain of the population.

aux.iters

count; number of auxiliary iterations used for network simulation.

nchains

count; number of chains of the population MCMC. By default set to twice the model dimension (number of model terms).

gamma

scalar; parallel adaptive direction sampling move factor.

V.proposal

count; diagonal entry for the multivariate Normal proposal. By default set to 0.0025.

startVals

vector; optional starting values for the parameter estimation.

offset.coef

vector; A vector of coefficients for the offset terms.

...

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

References

Caimo, A. and Friel, N. (2011), "Bayesian Inference for Exponential Random Graph Models," Social Networks, 33(1), 41-55. https://arxiv.org/abs/1007.5192

Caimo, A. and Friel, N. (2014), "Bergm: Bayesian Exponential Random Graphs in R," Journal of Statistical Software, 61(2), 1-25. https://www.jstatsoft.org/article/view/v061i02

Examples

if (FALSE) {
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 3000,
               gamma      = 1.2)

# Posterior summaries:
summary(p.flo)
}