gs {bnlearn}R Documentation

Grow-Shrink (GS) learning algorithm

Description

Estimate the equivalence class of a directed acyclic graph (DAG) from data using the Grow-Shrink (GS) constraint-based algorithm.

Usage

  gs(x, cluster = NULL, whitelist = NULL, blacklist = NULL,
    test = NULL, alpha = 0.05, debug = FALSE, optimized = TRUE,
    strict = FALSE, undirected = FALSE, direction = FALSE)

Arguments

x a data frame, containing the variables in the model.
cluster an optional cluster object from package snow. See snow integration for details and a simple example.
whitelist a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph.
blacklist a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph.
test a character string, the label of the conditional independence test to be used in the algorithm. If none is specified, the default test statistic is the mutual information for discrete data sets and the linear correlation for continuous ones. See bnlearn-package for details.
alpha a numeric value, the target nominal type I error rate.
debug a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.
optimized a boolean value. See bnlearn-package for details.
strict a boolean value. If TRUE conflicting results in the learning process generate an error; otherwise they result in a warning.
undirected a boolean value. If TRUE no attempt will be made to determine the orientation of the arcs; the returned (undirected) graph will represent the underlying structure of the Bayesian network.
direction a boolean value. If TRUE (and undirected is set to FALSE) each possible direction of each undirected arc is tested, and the one with the lowest p-value is accepted as the true direction for that arc. This step is not included in the original algorithm, and is intended as an experimental debugging tool.

Value

An object of class bn. See bn-class for details.

Author(s)

Marco Scutari

References

D. Margaritis. Learning Bayesian Network Model Structure from Data. PhD thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, May 2003. Available as Technical Report CMU-CS-03-153.

See Also

iamb, fast.iamb, inter.iamb, mmpc, hc.


[Package bnlearn version 1.3 Index]