inst/doc/KnechtVignette.R

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

## ---- 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
#