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 = "mi", alpha = 0.05, debug = FALSE, optimized = TRUE,
    strict = TRUE, direction = FALSE)

Arguments

x a data frame, containing the variables in the model.
cluster an optional cluster object from package snow. See bnlearn-package for details.
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. Possible values are mi (mutual information), mh (Cochran-Mantel-Haenszel), fmi (fast mutual information), cor (linear correlation), zf (Fisher's Z). See bnlearn-package for details.
alpha a numerical 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.
direction a boolean value. If TRUE 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.

Value

An object of class bn. See bnlearn-package 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.


[Package bnlearn version 0.4 Index]