mmpc: Local discovery structure learning algorithms

In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference

Description

Learn the skeleton of a directed acyclic graph (DAG) from data using the Max-Min Parents and Children (MMPC) and the Semi-Interleaved HITON-PC constraint-based algorithms. ARACNE and Chow-Liu learn an approximation of that structure using pairwise mutual information coefficients.

Usage

mmpc(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE)
si.hiton.pc(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE)

aracne(x, whitelist = NULL, blacklist = NULL, mi = NULL, debug = FALSE)
chow.liu(x, whitelist = NULL, blacklist = NULL, mi = NULL, debug = 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.
mi	a character string, the estimator used for the pairwise (i.e. unconditional) mutual information coefficients in the ARACNE and Chow-Liu algorithms. Possible values are mi (discrete mutual information) and mi-g (Gaussian mutual information).
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 categorical variables, the Jonckheere-Terpstra test for ordered factors and the linear correlation for continuous variables. 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.

Aliferis FC, Statnikov A, Tsamardinos I, Subramani M, Koutsoukos XD (2010). "Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation". Journal of Machine Learning Research, 11, 171-234.

Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006). "ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context". BMC Bioinformatics, 7(Suppl 1):S7.

See Also

constraint-based algorithms, score-based algorithms, hybrid algorithms.

vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.