causalEffectsSimulation: Simulate BAPs and compute causal effects of greedy search...

In cnowzohour/greedyBAPs: Greedy BAP Learning Using Penalised Maximum Likelihood Score

Description

Simulate BAPs and compute causal effects of greedy search results and ground truth

Usage

causalEffectsSimulation(N, p, n, mc.cores = 1, max.in.degree = Inf,
  n.restarts = 0, max.steps = Inf, max.iter.ricf = 10,
  equivalent.eps = 1e-10, verbose = FALSE)

Arguments

N	Number of simulation runs
p	Number of vertices
n	Number of samples to generate
mc.cores	Parallelism (number of cores to be used). If set to 1, no parallelism is used. Should be at most N.
max.in.degree	Maximal allowed in-degree
n.restarts	Number of restarts
max.steps	Max number of greedy steps per run
max.iter.ricf	Max number of iterations in RICF
equivalent.eps	Maximal allowed score difference for two graphs to be considered equivalent
verbose	Print extra log output?

Value

List containing: CE.gt - List of minimal absolute causal effect matrices E (one per simulation run) for the ground truths, where (E)_i,j is the causal effect on j on i CE.greedy - same for the greedy search results

cnowzohour/greedyBAPs documentation built on May 13, 2019, 1:36 a.m.