blip.scorer: Parent set exploration

In r.blip: Bayesian Network Learning Improved Project

Description

Generates the cache of parent sets from a given data source

Usage

blip.scorer(dat, method = "is", indeg = 6, time = 3600,
  scorefunction = "bic", alpha = 1, cores = 1, verbose = 0)

Arguments

dat	dataframe from which to learn the parent sets.(required)
method	Method to be used. Possible values: "is" (independence selection), "sq" (sequential selection). (default: is)
indeg	Maximum number of parents (default: 6)
time	Maximum Execution time (default: 3600)
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

Usually the first step in the learning of a Bayesian network.

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

Cache of parent sets

Examples

jkl <- blip.scorer(child, time=3)

r.blip documentation built on May 2, 2019, 3:01 a.m.