blip.learn.tw {r.blip}

Learns a BN with a treewidth bound

Description

Fully learns a Bayesian networks with a treewidth bound.

Usage

blip.learn.tw(dat, scorer.method = "is", solver.method = "kmax",
  treewidth = 5, time = 3600, allocated = 80,
  scorefunction = "bic", alpha = 1, cores = 1, verbose = 0)

Arguments

dat

dataframe from which to learn the parent sets.(required)

scorer.method

Method to be used for scoring the parent sets. Possible values: "is" (independence selection), "sq" (sequential selection). (default: is)

solver.method

Method to be used for bounded-treewidth structure exploration. Possible values: "kmax", "kg", "ka". (default: kmax)

treewidth

Maximum treewidth (default: 4)

time

Execution time (default: 3600)

allocated

Percentage of the total execution time dedicated to parent set exploration (default: 80)

scorefunction

Chosen score function. Possible choices: BIC, BDeu (default: bic)

alpha

(if BDeu is chosen) equivalent sample size parameter (default: 1.0)

cores

Number of machine cores to use. If 0, all are used. (default: 1)

verbose

Verbose level (default: 0)

Details

The input data is required to be complete and discrete. Accordingly missing values in the input data.frame will be ignored, and all numeric values will be converted to integers.

Value

The learned Bayesian network in the bnlearn format.

Examples

bn <- blip.learn.tw(child, treewidth=4, time=3)