stability_validation: Stability validation of ego-Temporal Exponential Random Graph...

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

Description

This function examines the stability of ego-TERGM results to the pooling window. It takes some proportion of the window's network and compares the result of a model fit on these time periods to the original fit.

Usage

stability_validation(ego_tergm_fit = NULL, splitting_probability = 0.5,
  seed = 12345, R = 10, forking = FALSE, ncpus = 1, steps = 50,
  tol = 1e-06)

Arguments

ego_tergm_fit	The output of a previously fit ego-TERGM fit using the ego_tergm function. This is the model that stability validation will be performed on.
splitting_probability	A value from 0 to 1 that determines the probability that any given network is assigned to the comparison group.
seed	The seed set to replicate analysis for pseudorandom number generator.
R	The number of bootstrap replications that should be used for the estimation of a bootstrapped MPLE estimated TERGM for model initialization. Defaults to 10.
forking	If parallelization via forking should be used (TRUE) or if no parallel processing should be used (FALSE). Currently, sockets are not supported.
ncpus	The number of CPUs that should should be used for estimation, defaults to 1.
steps	The number of default EM steps that should be taken, defaults to 50.
tol	The difference in parameter estimates between EM iterations to determine if the algorithm has converged. Defaults to 1e-6.

Value

Returns comparison_table (a matrix of cross-tabulation results to compare common cluster assignments or if incompatible a table of relative proportions sorted by value to allow for comparisons under set incompatibility and label switching), networks_sampled (which networks were included in the new validation sample), comparison_lambda (the matrix of role assignments for validation networks), comparison_group.theta (centroids for validation networks), comparison_EE.BIC (Salter-Townshend and Murphy BIC that doesn't penalize for longitudinal networks for validation networks), comparison_TS.BIC (BIC that penalizes for longitudinal networks for validation networks), comparison_role_assignments (role assignments for validation networks), and comparison_ego_nets (validation ego-networks). Note that labels may switch.

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

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)

stability_check <- stability_validation(ego_tergm_fit = ego_tergm_fit, seed = 614)
print(stability_check$comparison_table)

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