bnlearn (version 0.8)

iamb: Incremental Association (IAMB) learning algorithm

Description

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

Usage

iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL,
    test = NULL, 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 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. Possible values are mi (mutual information), mh (Cochran-Mantel-Haenszel), fmi (
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 bn-class for details.

References

I. Tsamardinos, C. F. Aliferis, and A. Statnikov. Algorithms for large scale Markov blanket discovery. In Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, pages 376-381. AAAI Press, 2003.

See Also

gs, fast.iamb, inter.iamb, hc.