Nothing
test_that("calculating statistics based on a categorical node level covariate works", {
skip_on_cran()
skip("For time")
set.seed(12345)
net <- matrix(runif(100,0,1),10,10)
colnames(net) <- rownames(net) <- letters[1:10]
node_level_covariates <- data.frame(Age = c(25,30,34,27,36,39,27,28,35,40),
Height = c(70,70,67,58,65,67,64,74,76,80),
Type = c("A","B","B","A","A","C","B","B","C","C"))
rownames(node_level_covariates) <- letters[1:10]
network_covariate <- net + matrix(rnorm(100,0,.5),10,10)
formula <- net ~ edges + mutual + ctriads + out2stars(covariate = "Type") + in2stars(covariate = "Type", base = "C") + sender("Age") +
netcov("network_covariate") + nodematch("Type",base = "A")
test <- gergm(formula,
covariate_data = node_level_covariates,
network_is_directed = TRUE,
estimation_method = "Metropolis",
number_of_networks_to_simulate = 100000,
thin = 1/100,
proposal_variance = 0.1,
MCMC_burnin = 50000,
seed = 456,
convergence_tolerance = 0.5)
check_against <- c(0.711, 0.360, -3.588, 0.089, -6.208, 3.554, -3.676,
-0.013, 0.084, 2.495, 0.180, -2.024)
check <- c(round(as.numeric(test@theta.coef[1,]),3),
round(as.numeric(test@lambda.coef[1,]),3))
expect_equal(check, check_against)
})
test_that("stochastic aproximation works", {
skip_on_cran()
skip("Still in development")
# have not messed with this in a while (3-27-17) may not work.
# try some simulations
system.time({
set.seed(12345)
net <- matrix(runif(40000),200,200)
diag(net) <- 0
colnames(net) <- rownames(net) <- 1:200
formula <- net ~ edges + ttriads + in2stars
test <- simulate_networks(formula,
thetas = c(-.4,.3),
network_is_directed = TRUE,
simulation_method = "Metropolis",
number_of_networks_to_simulate = 100,
thin = 1/10,
proposal_variance = 0.0001,
downweight_statistics_together = TRUE,
MCMC_burnin = 50,
omit_intercept_term = TRUE,
seed = 456)
})
ncol(combn(1:500,3))
system.time({
set.seed(12345)
net <- matrix(runif(250000),500,500)
diag(net) <- 0
colnames(net) <- rownames(net) <- 1:500
formula <- net ~ edges + ttriads + in2stars
test2 <- simulate_networks(formula,
thetas = c(-.4,.3),
network_is_directed = TRUE,
simulation_method = "Metropolis",
number_of_networks_to_simulate = 100,
thin = 1/10,
proposal_variance = 0.00005,
downweight_statistics_together = TRUE,
MCMC_burnin = 50,
omit_intercept_term = TRUE,
seed = 456,
use_stochastic_MH = TRUE,
stochastic_MH_proportion = 0.00004)
})
set.seed(12345)
net <- matrix(runif(2500,0,1),50,50)
colnames(net) <- rownames(net) <- 1:50
formula <- net ~ edges + mutual + ttriads
test <- gergm(formula,
estimation_method = "Metropolis",
number_of_networks_to_simulate = 1000,
thin = 1/10,
proposal_variance = 0.04,
downweight_statistics_together = TRUE,
MCMC_burnin = 500,
seed = 456,
convergence_tolerance = 0.5,
use_stochastic_MH = TRUE,
stochastic_MH_proportion = 0.4,
)
check_against <- c(0.657, 0.212, -2.134, 0.234, -3.997, 2.960, -2.605,
-0.013, 0.057, 2.586, 0.170, -1.944)
check <- c(round(as.numeric(test@theta.coef[1,]),3),round(as.numeric(test@lambda.coef[1,]),3))
expect_equal(check, check_against)
})
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