KL_net: Generates list of degree sequences and summary statistics for...

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

Description

Generates list of degree sequences and summary statistics for a mechanistic model

Usage

net_ss(
  theta_m,
  replicates = 1000,
  sorted = TRUE,
  mech_net,
  lstat,
  lapply_opt = TRUE,
  ergm = FALSE
)

process_ss(
  g,
  theta_s,
  mirror,
  type = "Bianconi",
  entropy_ss = FALSE,
  degree_triangle = FALSE
)

KL_ss(
  theta_m,
  theta_s,
  mirror = TRUE,
  type = "Bianconi",
  entropy_ss = FALSE,
  degree_triangle = FALSE,
  ...
)

KL_net(theta_m, ...)

Arguments

theta_m	numeric mechanistic model parameters
replicates	numeric number of replicates
sorted	logical whether to consider the sorted or unsorted degree sequence as the integral statistic
mech_net	function the mechanistic network simulator
lstat	function computes the likelihood statistic
lapply_opt	boolean internal
g	output of 'net_ss'
theta_s	statistical parameter
mirror	Boolean. whether base measure should be mirrored
type	String: either "Liebenau" or "Bianconi"

Examples

library(StartNetwork)
n = 15
replicates = 400

mech_net_triangles <- purrr::partial(mech_net_triangles_n, n = !!n)
lstat = function(x){length(igraph::triangles(x))/3}

x = net_ss(
 theta_m = 0.3,
 replicates = 50,
 mech_net = mech_net_triangles,
 lstat = lstat
)

y = process_ss(x, 0.5, mirror = TRUE)

reprocess_ss(y, 0.6)

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