bnlearn - Bayesian network structure learning

hybrid algorithms {bnlearn}

R Documentation

Hybrid structure learning algorithms

Description

Learn the structure of a Bayesian network with the Max-Min Hill Climbing (MMHC) and the more general 2-phase Restricted Maximization (RSMAX2) hybrid algorithms.

Usage

rsmax2(x, whitelist = NULL, blacklist = NULL, restrict,
  maximize = "hc", test = NULL, score = NULL, alpha = 0.05,
  B = NULL, ..., maximize.args = list(), optimized = TRUE,
  strict = FALSE, debug = FALSE)
mmhc(x, whitelist = NULL, blacklist = NULL, test = NULL,
  score = NULL, alpha = 0.05, B = NULL, ..., restart = 0,
  perturb = 1, max.iter = Inf, optimized = TRUE,
  strict = FALSE, debug = FALSE)

Arguments

`x`	a data frame containing the variables in the model.
`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.
`restrict`	a character string, the constraint-based algorithm to be used in the “restrict” phase. Possible values are `gs`, `iamb`, `fast.iamb`, `inter.iamb` and `mmpc`. See `bnlearn-package` and the documentation of each algorithm for details.
`maximize`	a character string, the score-based algorithm to be used in the “maximize” phase. The only possible value is `hc`. See `bnlearn-package` for details.
`test`	a character string, the label of the conditional independence test to be used by the constraint-based 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.
`score`	a character string, the label of the network score to be used in the score-based algorithm. If none is specified, the default score is the Bayesian Information Criterion for both discrete and continuous data sets. See `bnlearn-package` for details.
`alpha`	a numeric value, the target nominal type I error rate of the conditional independence test.
`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.
`...`	additional tuning parameters for the network score used by the score-based algorithm. See `score` for details.
`maximize.args`	a list of arguments to be passed to the score-based algorithm specified by `maximize`, such as `restart` for hill-climbing or `tabu` for tabu search.
`restart`	an integer, the number of random restarts for the score-based algorithm.
`perturb`	an integer, the number of attempts to randomly insert/remove/reverse an arc on every random restart.
`max.iter`	an integer, the maximum number of iterations for the score-based algorithm.
`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.

Note

mmhc is simply rshc with restrict set to mmpc and maximize set to hc.

Author(s)

Marco Scutari

References

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