# File tests/testthat/test-gflomiss.R in package ergm, part of the # Statnet suite of packages for network analysis, https://statnet.org . # # This software is distributed under the GPL-3 license. It is free, # open source, and has the attribution requirements (GPL Section 7) at # https://statnet.org/attribution . # # Copyright 2003-2023 Statnet Commons ################################################################################ library(statnet.common) opttest({ data(florentine) run.test <- function() { ## ## Create random 5% missing ## #mflomarriage <- rergm(network.size(flomarriage),prob=0.05, directed=FALSE) #flomarriage <- set.graph.attribute(flomarriage, "design", mflomarriage) #summary(flomarriage) # # Create random 2 nodes who are non-respondents # #respondent <- rmultinom(n=1, size=network.size(flomarriage)-2, # prob=rep(1,network.size(flomarriage))) #respondent respondent <- rep(FALSE,network.size(flomarriage)) respondent[sample(1:network.size(flomarriage), size=network.size(flomarriage)-2,replace=FALSE)] <- TRUE respondent # #one <- matrix(1,ncol=1,nrow=network.size(flomarriage)) #orespondent <- one %*% t(respondent) + respondent %*% t(one) - respondent %*% t(respondent) #orespondent <- 1-orespondent #diag(orespondent) <- 0 ## #mflomarriage <- graph(orespondent, directed=FALSE) #summary(mflomarriage) #sociomatrix(mflomarriage) #flomarriage <- set.graph.attribute(flomarriage, "design", mflomarriage) #efit <- ergm(flomarriage ~ edges + kstar(2), control=control.ergm(MCMC.samplesize=1000, MCMC.interval=1000)) efit <- ergm(flomarriage ~ edges + kstar(2), estimate="MPLE") summary(efit) flomarriage <- set.vertex.attribute(flomarriage, "respondent", respondent) rm(respondent) summary(flomarriage) efit <- ergm(flomarriage ~ edges + kstar(2), estimate="MPLE") summary(efit) efit <- ergm(flomarriage ~ edges + kstar(2), control=control.ergm(MCMC.samplesize=1000, MCMC.interval=1000)) summary(efit) efit <- ergm(flomarriage ~ edges + kstar(2), control=control.ergm(init=c(-1.6,0))) # # edges -1.6 -1.74242 0.8557 0.044 0.041373 # kstar2 0.0 0.02746 0.1774 0.877 0.009076 # summary(efit) } test_that("undirected network with missing data and dyadic dependence", { expect_error(run.test(), NA) }) },"undirected network with missing data and dyadic dependence")