fast.iamb {bnlearn}R Documentation

Fast Incremental Association (Fast-IAMB) learning algorithm

Description

Estimate the equivalence class of a directed acyclic graph (DAG) from data using the Fast Incremental Association (FAST-IAMB) Constraint-based algorithm.

Usage


  fast.iamb(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

S. Yaramakala, D. Margaritis. Speculative Markov Blanket Discovery for Optimal Feature Selection. In Proceedings of the Fifth IEEE International Conference on Data Mining, pages 809-812. IEEE Computer Society, 2005.

See Also

gs, fast.iamb, iamb.


[Package bnlearn version 0.4 Index]