bnlearn: hc – R documentation

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bnlearn

hc

Score-based structure learning algorithms

Description

Learn the structure of a Bayesian network using a hill-climbing (HC) or a Tabu search (TABU) greedy search.

Usage

hc(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
  debug = FALSE, restart = 0, perturb = 1, max.iter = Inf, maxp = Inf, optimized = TRUE)
tabu(x, start = NULL, whitelist = NULL, blacklist = NULL, score = NULL, ...,
  debug = FALSE, tabu = 10, max.tabu = tabu, max.iter = Inf, maxp = Inf, optimized = TRUE)

Arguments

`x`	a data frame containing the variables in the model.
`start`	an object of class `bn`, the preseeded directed acyclic graph used to initialize the algorithm. If none is specified, an empty one (i.e. without any arc) is used.
`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.
`score`	a character string, the label of the network score to be used in the algorithm. If none is specified, the default score is the Bayesian Information Criterion for both discrete and continuous data sets. See `network scores` for details.
`...`	additional tuning parameters for the network score. See `score` for details.
`debug`	a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.
`restart`	an integer, the number of random restarts.
`tabu`	a positive integer number, the length of the tabu list used in the `tabu` function.
`max.tabu`	a positive integer number, the iterations tabu search can perform without improving the best network score.
`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.
`maxp`	the maximum number of parents for a node. The default value is `Inf`.
`optimized`	a boolean value. If `TRUE` (the default), score caching is used to speed up structure learning.

Value

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

Note

See structure learning for a complete list of structure learning algorithms with the respective references.

Author(s)

Marco Scutari

hc