single-node local discovery | R Documentation |
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, ..., max.sx = NULL, debug = FALSE)
learn.nbr(x, node, method, whitelist = NULL, blacklist = NULL,
test = NULL, alpha = 0.05, ..., max.sx = NULL, debug = FALSE)
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. |
method |
a character string, the label of a structure learning algorithm. Possible choices are listed in structure learning. |
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
|
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 |
alpha |
a numeric value, the target nominal type I error rate. |
... |
optional arguments to be passed to the test specified by
|
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 |
A vector of character strings, the labels of the nodes in the Markov blanket
(for learn.mb()
) or in the neighbourhood (for learn.nbr()
).
All algorithms used by learn.mb()
and learn.nbr()
accept
incomplete data, which they handle by computing individual conditional
independence tests on locally complete observations.
Marco Scutari
constraint-based algorithms.
learn.mb(learning.test, node = "D", method = "iamb")
learn.mb(learning.test, node = "D", method = "iamb", blacklist = c("A", "F"))
learn.nbr(gaussian.test, node = "F", method = "si.hiton.pc", whitelist = "D")
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