Nothing
# File tests/testthat/test-miss.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
################################################################################
attach(MLE.tools)
library(statnet.common)
opttest({
library(ergm)
theta0err<- 1 # Perturbation in the initial values
tolerance<-4 # Result must be within 5*MCMCSE of truth.
bridge.target.se <- 0.005 # Log-likelihood MCMC standard error must be within this.
n<-20 # Number of nodes
b<-3 # Bipartite split
d<-.1 # Density
m<-.05 # Missingness rate
cat("n=",n,", density=",d,", missing=",m,"\n",sep="")
run.miss.test<-function(y){
theta <- edges.theta(y)
cat("Correct estimate =",theta,"with log-likelihood",edges.llk(y),".\n")
mplefit<-ergm(y~edges, eval.loglik=TRUE)
mple.theta.OK<-all.equal(theta,coef(mplefit),check.attributes=FALSE)
mple.llk.OK<-all.equal(edges.llk(y, coef(mplefit)),
as.vector(logLik(mplefit)),check.attributes=FALSE)
cat("MPLE estimate =", coef(mplefit),"with log-likelihood",logLik(mplefit), if(isTRUE(mple.theta.OK)&&isTRUE(mple.llk.OK)) "OK.","\n")
mcmcfit<-ergm(y~edges, control=snctrl(force.main=TRUE, init=theta+theta0err, bridge.target.se=bridge.target.se), verbose=TRUE, eval.loglik=TRUE)
mcmc.diagnostics(mcmcfit)
mcmc.theta.OK<-abs(theta-coef(mcmcfit))/sqrt(diag(vcov(mcmcfit, source="estimation")))
mcmc.llk.OK<-abs(edges.llk(y, coef(mcmcfit))-logLik(mcmcfit))/bridge.target.se
cat("MCMCMLE estimate =", coef(mcmcfit),"with log-likelihood",logLik(mcmcfit), if(mcmc.theta.OK<tolerance&&mcmc.llk.OK<tolerance) "OK.","\n")
expect_true(is.na(mplefit))
expect_true(is.na(mcmcfit))
expect_true(anyNA(mplefit))
expect_true(anyNA(mcmcfit))
expect_true(is.dyad.independent(mplefit))
expect_true(is.dyad.independent(mcmcfit))
expect_true(isTRUE(mple.theta.OK))
expect_lt(mcmc.theta.OK, tolerance)
expect_true(isTRUE(mple.llk.OK))
expect_lt(mcmc.llk.OK, tolerance)
}
# Directed
test_that("directed network", {
set.seed(123)
y<-mk.missnet(n, d, m, TRUE, FALSE)
run.miss.test(y)
})
# Undirected
test_that("undirected Network", {
set.seed(456)
y<-mk.missnet(n, d, m, FALSE, FALSE)
run.miss.test(y)
})
# Bipartite Undirected
test_that("Bipartite Undirected Network", {
set.seed(0)
y<-mk.missnet(n, d, m, FALSE, b)
run.miss.test(y)
})
# Add the curved+missing test here for now
test_that("curved+missing", {
set.seed(321)
n <- 100
y <- network.initialize(n, directed=FALSE) # Create an empty network
y <- simulate(y~edges, coef=logit(0.12), control=control.simulate(MCMC.burnin=2*n^2))
y.miss <- simulate(y~edges, coef=logit(0.01))
y[as.edgelist(y.miss)] <- NA
cat("Network statistics:\n")
print(summary(y~edges+gwesp()))
truth<-edges.theta(y)
cat("Correct estimate =",truth,"\n")
set.seed(654)
mcmcfit<-ergm(y~edges+gwesp(), control=control.ergm(MCMLE.maxit=5))
summary(mcmcfit)
expect_lt(abs(coef(mcmcfit)[1]-truth)/sqrt(mcmcfit$covar[1]), tolerance)
})
}, "missing data")
detach(MLE.tools)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.