boot.iamb: Incremental Association Algorithm (IAMB) With Bootstrapping

In rlebron-bioinfo/gnlearn: Genetic Network Learning

Description

This function allows you to learn a directed graph from a dataset using the Incremental Association algorithm.

Usage

boot.iamb(
  df,
  whitelist = NULL,
  blacklist = NULL,
  test = ci.tests,
  alpha = 0.01,
  B = NULL,
  max.sx = NULL,
  version = c("iamb", "fast.iamb", "inter.iamb", "iamb.fdr"),
  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.
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).
test	Conditional independence test to be used: 'cor', 'mc-cor', 'smc-cor', 'zf', 'mc-zf', 'smc-zf', 'mi-g', 'mc-mi-g', 'smc-mi-g', or 'mi-g-sh'. Default: 'cor'
alpha	Target nominal type I error rate. Default: 0.01
B	Number of permutations considered for each permutation test.
max.sx	Maximum allowed size of the conditioning sets.
version	Algorithm version: 'iamb', 'fast.iamb', 'inter.iamb', or 'iamb.fdr'. Default: 'iamb'
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.iamb(df)
avg.g <- obj$average
g.rep <- obj$replicates

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