Learn R Programming
bnlearn (version 1.6)

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, B = NULL, debug = FALSE,
    optimized = TRUE, strict = 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
alpha
a numeric value, the target nominal type I error rate.
B
a positive integer, the number of permutations considered for each permutation test. It will be ignored with a warning if the conditional independence test specified by the test argument is not a permutation test.
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.

Value

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

References

Tsamardinos I, Aliferis CF, Statnikov A (2003). "Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations". In "KDD '03: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining", pp. 673-678. ACM.

Tsamardinos I, Brown LE, Aliferis CF (2006). "The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm". Machine Learning, 65(1), 31-78.

See Also

constraint-based algorithms, hc, hybrid algorithms.