Nothing
## ---- eval = FALSE-------------------------------------------------------
# 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"))
#
## ---- eval = FALSE-------------------------------------------------------
# library(egoTERGM)
# 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)
#
## ---- eval = FALSE-------------------------------------------------------
# interpret_ego_tergm(ego_tergm_fit = ego_tergm_fit)
#
# plots <- plot_ego_tergm(ego_tergm_fit = ego_tergm_fit)
# plots[[1]]
#
## ---- eval = FALSE-------------------------------------------------------
# net_list <- prepare_for_tergm(ego_tergm_fit = ego_tergm_fit)
#
# # Indexing of the output for prepare_for_tergm refers to the
# # role numbering from initial ego_tergm_fit
# role1_btergm <- btergm(net_list[[1]] ~ edges + mutual + triangle + nodeicov('idegsqrt') +
# nodeocov('odegsqrt') + nodematch('sex'),
# R = 500)
#
# # You could then continue this for all remaining network sets
# # in net_list
#
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