boot.hc: Hill-Climbing Algorithm (HC) With Bootstrapping

In rlebron-bioinfo/gnlearn: Genetic Network Learning

Description

This function allows you to learn a directed graph from a dataset using the Hill-Climbing algorithm.

Usage

boot.hc(
  df,
  start = NULL,
  whitelist = NULL,
  blacklist = NULL,
  score = scores,
  restart = 0,
  perturb = 1,
  max.iter = Inf,
  maxp = Inf,
  R = 200,
  m = NULL,
  threshold = 0.5,
  to = c("igraph", "adjacency", "edges", "graph", "bnlearn"),
  cluster = parallel::detectCores(),
  seed = sample(1:10^6, 1)
)

Arguments

df	Dataset.
start	Preseeded directed acyclic graph used to initialize the algorithm (optional).
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).
score	Score to be used: 'pred-loglik-g', 'loglik-g', 'aic-g', 'bic-g', or 'bge'. Default: 'pred-loglik-g'
restart	Number of random restarts. Default: 0
perturb	Number of attempts to randomly insert/remove/reverse an arc on every random restart. Default: 1
max.iter	Maximum number of iterations. Default: Inf
maxp	Maximum number of parents for a node. Default: Inf
R	Number of bootstrap replicates (optional). Default: 200
m	Size of training set (optional). Default: nrow(df)/2
threshold	Minimum strength required for a coefficient to be included in the average adjacency matrix (optional). Default: 0.5
to	Output format ('adjacency', 'edges', 'graph', 'igraph', or 'bnlearn') (optional).
cluster	A cluster object from package parallel or the number of cores to be used (optional). Default: parallel::detectCores()
seed	Seed used for random selection. Default: NULL

Examples

obj <- boot.hc(df)
avg.g <- obj$average
g.rep <- obj$replicates

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