Learn the Markov blanket or the neighbourhood centered on a node.
learn.mb(x, node, method, whitelist = NULL, blacklist = NULL, start = NULL,
test = NULL, alpha = 0.05, B = NULL, debug = FALSE)
learn.nbr(x, node, method, whitelist = NULL, blacklist = NULL,
test = NULL, alpha = 0.05, B = NULL, debug = FALSE)a data frame containing the variables in the model.
a character string, the label of the node whose local structure is being learned.
a character string, the label of a structure learning algorithm.
Possible choices are constraint-based algorithms for learn.mb
and local discovery algorithms for learn.nbr.
a vector of character strings, the labels of the whitelisted nodes.
a vector of character strings, the labels of the blacklisted nodes.
a vector of character strings, the labels of the nodes to be
included in the Markov blanket before the learning process (in
learn.mb). Note that the nodes in start can be removed from
the Markov blanket by the learning algorithm, unlike the nodes included due
to whitelisting.
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.
a numeric value, the target nominal type I error rate.
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.
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent.
A vector of character strings, the labels of the nodes in the Markov blanket
(for learn.mb) or in the neighbourhood (for learn.nbr).