bnlearn - man/causal.discovery.html

causal discovery algorithms {bnlearn}

R Documentation

Causal discovery algorithms

Description

Learn the structure of a (causal) Bayesian network with DirectLiNGAM.

Usage

direct.lingam(x, cluster, whitelist, blacklist, mi, maximize = "alasso",
  maximize.args = list(), debug = FALSE)

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.

mi

a character string, the label of the mutual information measure used to identify the topological ordering of the nodes. It can take values "pwling" (the default) or "gkernel".

maximize

a character value, the label of the method used to identify the parents of each node given the causal ordering. The only possible value is "alasso" (adaptive LASSO).

maximize.args

a list of arguments to be passed to the method specified by maximize. See below for details.

debug

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

Details

The "alasso" method has the following optional arguments:

gamma: the coefficient of the weights used in the adaptive LASSO. The default value if 1.
lambda.min.ratio and pmax: identical to the arguments of the same names in glmnet(), with the same default values and interpretation.

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