Usage
learn.mb(x, node, method, whitelist = NULL, blacklist = NULL, start = NULL,
test = NULL, alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE)
learn.mb(x, node, method, whitelist = NULL, blacklist = NULL, start = NULL,
test = NULL, alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE)
Arguments
x
a data frame containing the variables in the model.
node
a character string, the label of the node whose local
structure is being learned.
whitelist
a vector of character strings, the labels of the
whitelisted nodes.
blacklist
a vector of character strings, the labels of the
blacklisted nodes.
start
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 l
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 discrete data sets and the linear correlation
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.
debug
a boolean value. If TRUE a lot of debugging output
is printed; otherwise the function is completely silent.