bergmM function

Parameter estimation for Bayesian ERGMs under missing data

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

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

Arguments

• formula: formula; an ergm formula object, of the form ~ where is a network object and are ergm-terms.
• 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.
• 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.
• 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.
• seed: count; random number seed for the Bergm estimation.
• startVals: vector; optional starting values for the parameter estimation.
• offset.coef: vector; A vector of coefficients for the offset terms.
• nImp: count; number of imputed networks to be returned. If null, no imputed network will be returned.
• missingUpdate: count; number of tie updates in each imputation step. By default equal to number of missing ties. Smaller numbers increase speed. Larger numbers lead to better sampling.
• ...: additional arguments, to be passed to lower-level functions.

Examples

## Not run:

# Load the florentine marriage network
data(florentine)

# Create missing data
set.seed(14021994)
n <- dim(flomarriage[, ])[1]
missNode <- sample(1:n, 1)
flomarriage[missNode, ] <- NA
flomarriage[, missNode] <- NA

# Posterior parameter estimation:
m.flo <- bergmM(flomarriage ~ edges + kstar(2),
                burn.in    = 50,
                aux.iters  = 500,
                main.iters = 1000,
                gamma      = 1.2,
                nImp       = 5)

# Posterior summaries:
summary(m.flo)
## End(Not run)

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/v61/i02

Koskinen, J.H., Robins, G.L., Pattison, P.E. (2010), "Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation," Statistical Methodology 7(3), 366-384.

Krause, R.W., Huisman, M., Steglich, C., Snijders, T.A. (2020), "Missing data in cross-sectional networks-An extensive comparison of missing data treatment methods", Social Networks 62: 99-112.