arges: Adaptively Restricted Greedy Equivalence Search Algorithm...

In rlebron-bioinfo/gnlearn: Genetic Network Learning

Description

This function allows you to learn a directed graph from a dataset using the Adaptively Restricted Greedy Equivalence Search (ARGES) algorithm.

Usage

arges(
  df,
  whitelist = NULL,
  blacklist = NULL,
  indep.test = pcalg::gaussCItest,
  alpha = 0.01,
  max.sx = Inf,
  adaptive = c("none", "vstructures", "triples"),
  maxDegree = integer(0),
  m = NULL,
  to = c("igraph", "adjacency", "edges", "graph", "bnlearn"),
  seed = sample(1:10^6, 1)
)

Arguments

df	Dataset.
whitelist	A data frame with two columns, containing a set of arcs to be included in the graph (optional).
blacklist	A data frame with two columns, containing a set of arcs not to be included in the graph (optional).
indep.test	Conditional independence test to be used (pcalg implementation). Default: pcalg::gaussCItest
alpha	Target nominal type I error rate. Default: 0.01
max.sx	Maximum allowed size of the conditioning sets.
adaptive	Whether constraints should be adapted to newly detected v-structures or unshielded triples: 'none', 'vstructures', or 'triples'. Default: 'none'
maxDegree	Parameter used to limit the vertex degree of the estimated graph. Default: integer(0)
m	Size of training set (optional). Default: nrow(df)/2
to	Output format ('adjacency', 'edges', 'graph', 'igraph', or 'bnlearn') (optional).
seed	Seed used for random selection. Default: NULL

Examples

g <- arges(df)

rlebron-bioinfo/gnlearn documentation built on July 25, 2020, 12:38 p.m.