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bnlearn (version 4.9.4)

constraint-based algorithms: Constraint-based structure learning algorithms

Description

Learn the equivalence class of a directed acyclic graph (DAG) from data using the PC, Grow-Shrink (GS), Incremental Association (IAMB), Fast Incremental Association (Fast-IAMB), Interleaved Incremental Association (Inter-IAMB), Incremental Association with FDR (IAMB-FDR), Max-Min Parents and Children (MMPC), Semi-Interleaved HITON-PC or Hybrid Parents and Children (HPC) constraint-based algorithms.

Usage

pc.stable(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
gs(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
fast.iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
inter.iamb(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
iamb.fdr(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = FALSE)
mmpc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE)
si.hiton.pc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE)
hpc(x, cluster, whitelist = NULL, blacklist = NULL, test = NULL,
  alpha = 0.05, B = NULL, max.sx = NULL, debug = FALSE, undirected = TRUE)

Value

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

Arguments

x

a data frame containing the variables in the model.

cluster

an optional cluster object from package parallel.

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 categorical variables, the Jonckheere-Terpstra test for ordered factors and the linear correlation for continuous variables. See independence tests 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.

max.sx

a positive integer, the maximum allowed size of the conditioning sets used in conditional independence tests. The default is that there is no limit on size.

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

undirected

a boolean value. If TRUE no attempt will be made to determine the orientation of the arcs; the returned (undirected) graph will represent the underlying structure of the Bayesian network.

Author

Marco Scutari

See Also

independence tests, local discovery algorithms, score-based algorithms, hybrid algorithms, cextend.