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bnlearn (version 1.1)

mmpc: Max-Min Parents and Children (MMPC) learning algorithm

Description

Estimate the underlying structure of a directed acyclic graph (DAG) from data using the Max-Min Parents and Children (MMPC) constraint-based algorithm.

Usage

mmpc(x, cluster = NULL, whitelist = NULL, blacklist = NULL,
    test = NULL, alpha = 0.05, debug = FALSE, optimized = TRUE,
    strict = 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. Possible values are mi (mutual information for discrete data), fmi (fast mutual information),
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 (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

Value

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

References

I. Tsamardinos, C. F. Aliferis, A. Statnikov. Time and sample efficient discovery of Markov blankets and direct causal relations. Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining KDD, pages 673-8, 2003.

I. Tsamardinos, L. E. Brown, C. Aliferis. The max-min hill-climbing Bayesian network learning algorithm. Machine Learning, 65(1), pages 31-78. Kluwer Academic Publishers, 2006.

See Also

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