bnlearn - Bayesian network structure learning

local discovery algorithms {bnlearn}

R Documentation

Local discovery structure learning algorithms

Description

Learn 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 for continuous ones. See `bnlearn-package` for details.
`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.

Author(s)

Marco Scutari

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.