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# File tests/testthat/test-mple-cov.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
################################################################################
set.seed(14392)
N <- 50
y <- matrix(rbinom(N^2, 1, 0.005), N, N)
diag(y) <- 0
y <- as.network.matrix(y, directed = FALSE)
init.sim <- simulate(y ~ edges + triangles, nsim = 1, coef = c(-log(N) + 4, -0.2))
test_that("Godambe covariance method for MPLE", {
set.seed(111)
m1 <- ergm(init.sim ~ edges + triangles, estimate = "MPLE",
control=control.ergm(MPLE.covariance.method = "Godambe"))
StdErr1 <- sqrt(diag(vcov(m1)))
expect_equal(StdErr1, c(0.255, 0.059), ignore_attr = TRUE, tolerance=.01)
})
test_that("Inverse Hessian from logistic regression model", {
set.seed(222) # However, this method is not stochastic
m2 <- ergm(init.sim ~ edges+triangles, estimate = "MPLE",
control=control.ergm(MPLE.covariance.method = "invHess"))
StdErr2 <- sqrt(diag(vcov(m2)))
expect_equal(StdErr2, c(0.155, 0.034), ignore_attr = TRUE, tolerance=.01)
})
test_that("Bootstrap covariance method for MPLE", {
set.seed(333)
m3 <- ergm(init.sim ~ edges + triangles, estimate = "MPLE",
control=control.ergm(MPLE.covariance.method = "bootstrap"))
StdErr3 <- sqrt(diag(vcov(m3)))
expect_equal(StdErr3, c(0.257, 0.060), ignore_attr = TRUE, tolerance=.01)
})
test_that("Bootstrap covariance method for MPLE with offsets", {
set.seed(445)
m4 <- ergm(init.sim ~ edges + triangles + offset(triangles), offset.coef=1,
estimate = "MPLE",
control=control.ergm(MPLE.covariance.method = "InvHess"))
StdErr4 <- sqrt(diag(vcov(m4)))
expect_equal(StdErr4, c(0.155, 0.034, 0), ignore_attr = TRUE, tolerance=.01)
})
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