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, ... )
formula |
formula; an |
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. |
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
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. (2018), "Missing network data a comparison of different imputation methods," Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2018.
## Not run:
# Load the florentine marriage network
data(florentine)
# Create missing data
set.seed(22101992)
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.