Usage
inter.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL,
test = NULL, alpha = 0.05, debug = FALSE, optimized = TRUE,
strict = FALSE, undirected = FALSE, direction = FALSE)Arguments
x
a data frame, containing the variables in the model.
cluster
an optional cluster object from package snow.
See snow integration for details and a simple
example. 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. Possible
values are mi (mutual information for discrete
data), fmi (fast mutual information),
alpha
a numerical value, the target nominal type I error rate.
debug
a boolean value. If TRUE a lot of debugging output
is printed; otherwise the function is completely silent.
strict
a boolean value. If TRUE conflicting results in
the learning process generate an error; otherwise they result in
a warning.
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.
direction
a boolean value. If TRUE (and undirected is
set to FALSE) each possible direction of each undirected arc is
tested, and the one with the lowest p-value is accepted as the true
direction for that arc