In AnthonyEbert/StartNetwork: Calculates KL divergence between mechanistic and statistical network models

knitr::opts_chunk$set(echo = TRUE, cache = TRUE, cache.rebuild = FALSE, autodep = TRUE)

set.seed(1)

library(StartNetwork)
library(parallel)
library(ggplot2)

n = 12
replicates = 800
datapoints = 5

mech_net_gwdegree <- purrr::partial(sim_ergm_gwdegree, n = !!n)

gwdegrees 1

theta_s <- 0.5
theta_p <- rep(seq(0.3, 0.7, by = 0.1), datapoints)

cl <- parallel::makeCluster(parallel::detectCores())

g <- parallel::parLapply(cl, theta_p, StartNetwork::KL_ss, theta_s = theta_s, replicates = replicates, sorted = TRUE, mech_net = mech_net_gwdegree, lstat = function(x){as.numeric(ergm::summary_formula(x ~ gwdegree(10, fixed = TRUE)))}, mirror = TRUE, type = c("Bianconi", "Liebenau"), ergm = TRUE)

parallel::stopCluster(cl)

g_tidy <- StartNetwork::tidy_g(g)

ggplot(g_tidy) + aes(x = parameter, y = value, group = interaction(parameter, type), col = type) + geom_boxplot(outlier.shape = NULL, position = "identity") + facet_wrap( ~ key, scales = "free_y") + 
  ggplot2::geom_vline(mapping = aes(xintercept = theta_s))

gwdegrees 2

theta_s <- 0.4
theta_p <- rep(seq(0.2, 0.6, by = 0.1), datapoints)

cl <- parallel::makeCluster(parallel::detectCores())

g <- parallel::parLapply(cl, theta_p, StartNetwork::KL_ss, theta_s = theta_s, replicates = replicates, sorted = TRUE, mech_net = mech_net_gwdegree, lstat = function(x){as.numeric(ergm::summary_formula(x ~ gwdegree(10, fixed = TRUE)))}, mirror = TRUE, type = c("Bianconi", "Liebenau"), ergm = TRUE)

parallel::stopCluster(cl)

g_tidy <- StartNetwork::tidy_g(g)

ggplot(g_tidy) + aes(x = parameter, y = value, group = interaction(parameter, type), col = type) + geom_boxplot(outlier.shape = NULL, position = "identity") + facet_wrap( ~ key, scales = "free_y") + 
  ggplot2::geom_vline(mapping = aes(xintercept = theta_s))

gwdegrees 3

theta_s <- 0.3
theta_p <- rep(seq(0.1, 0.5, by = 0.1), datapoints)

cl <- parallel::makeCluster(parallel::detectCores())

g <- parallel::parLapply(cl, theta_p, StartNetwork::KL_ss, theta_s = theta_s, replicates = replicates, sorted = TRUE, mech_net = mech_net_gwdegree, lstat = function(x){as.numeric(ergm::summary_formula(x ~ gwdegree(10, fixed = TRUE)))}, mirror = TRUE, type = c("Bianconi", "Liebenau"), ergm = TRUE)

parallel::stopCluster(cl)

g_tidy <- StartNetwork::tidy_g(g)

ggplot(g_tidy) + aes(x = parameter, y = value, group = interaction(parameter, type), col = type) + geom_boxplot(outlier.shape = NULL, position = "identity") + facet_wrap( ~ key, scales = "free_y") + 
  ggplot2::geom_vline(mapping = aes(xintercept = theta_s))

AnthonyEbert/StartNetwork documentation built on April 24, 2020, 3:28 a.m.