prepare_for_tergm: Prepares ego-TERGM output for xergm's btergm function.

In egoTERGM: Estimation of Ego-Temporal Exponential Random Graph Models via Expectation Maximization (EM)

Description

This takes the output of an ego-TERGM call and prepares it for use by the xergm btergm function. Note: This routine assumes temporal independence within ego-networks and independence across ego-networks.

Usage

prepare_for_tergm(ego_tergm_fit = NULL)

Arguments

ego_tergm_fit

The output of an ego-TERGM call.

Value

A list of length G containing pooled cluster assignments. First-level elements of this list may be fed to a btergm call.

References

Campbell, Benjamin W. (2018): Inferring Latent Roles in Longitudinal Networks. Political Analysis 26(3): 292-311. https://doi.org/10.1017/pan.2018.20

Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2017): Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software 83(6): 1-36. http://dx.doi.org/10.18637/jss.v083.i06

Examples

# Code from xergm.common and their preparation of the Knecht network
library(xergm.common)
set.seed(1)

data("knecht")

for (i in 1:length(friendship)) {
 rownames(friendship[[i]]) <- paste("Student.", 1:nrow(friendship[[i]]), sep="")
 colnames(friendship[[i]]) <- paste("Student.", 1:nrow(friendship[[i]]), sep="")
}
rownames(primary) <- rownames(friendship[[1]])
colnames(primary) <- colnames(friendship[[1]])
sex <- demographics$sex
names(sex) <- rownames(friendship[[1]])
# step 2: imputation of NAs and removal of absent nodes:
friendship <- xergm.common::handleMissings(friendship, na = 10, method = "remove")
friendship <- xergm.common::handleMissings(friendship, na = NA, method = "fillmode")
# step 3: add nodal covariates to the networks
for (i in 1:length(friendship)) {
  s <- xergm.common::adjust(sex, friendship[[i]])
  friendship[[i]] <- network::network(friendship[[i]])
  friendship[[i]] <- network::set.vertex.attribute(friendship[[i]], "sex", s)
  idegsqrt <- sqrt(sna::degree(friendship[[i]], cmode = "indegree"))
  friendship[[i]] <- network::set.vertex.attribute(friendship[[i]],
                                                   "idegsqrt", idegsqrt)
  odegsqrt <- sqrt(sna::degree(friendship[[i]], cmode = "outdegree"))
  friendship[[i]] <- network::set.vertex.attribute(friendship[[i]],
                                                   "odegsqrt", odegsqrt)
}
sapply(friendship, network::network.size)
net <- friendship
rm(list=setdiff(ls(), "net"))

ego_tergm_fit <- ego_tergm(net = net,
                          form = c("edges", "mutual", "triangle",
                                   "nodeicov('idegsqrt')", "nodeocov('odegsqrt')",
                                   "nodematch('sex')"),
                          core_size = 1,
                          min_size = 5,
                          roles = 3,
                          add_drop = TRUE,
                          directed = TRUE,
                          edge_covariates = FALSE,
                          seed = 12345,
                          R = 10,
                          forking = FALSE,
                          ncpus = 1,
                          steps = 50,
                          tol = 1e-06)

net_list <- prepare_for_tergm(ego_tergm_fit)

role1_btergm <- btergm(net_list[[1]] ~ edges + mutual + triangle + nodeicov('idegsqrt') +
                                       nodeocov('odegsqrt') + nodematch('sex'),
                       R = 500)

egoTERGM documentation built on May 17, 2019, 5:03 p.m.