inter.iamb {bnlearn} | R Documentation |
Estimate the equivalence class of a directed acyclic graph (DAG) from data using the Interleaved Incremental Association (Inter-IAMB) Constraint-based algorithm.
inter.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = "mi", alpha = 0.05, debug = FALSE, optimized = TRUE, strict = TRUE, direction = FALSE)
x |
a data frame, containing the variables in the model. |
cluster |
an optional cluster object from package snow.
See bnlearn-package for details. |
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), mh
(Cochran-Mantel-Haenszel), fmi (fast
mutual information), cor (linear correlation),
zf (Fisher's Z). See bnlearn-package
for details. |
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. |
optimized |
a boolean value. See bnlearn-package
for details. |
strict |
a boolean value. If TRUE conflicting results in
the learning process generate an error; otherwise they result in
a warning. |
direction |
a boolean value. If TRUE 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. |
An object of class bn
.
See bnlearn-package
for details.
Marco Scutari
S. Yaramakala, D. Margaritis. Speculative Markov Blanket Discovery for Optimal Feature Selection. In Proceedings of the Fifth IEEE International Conference on Data Mining, pages 809-812. IEEE Computer Society, 2005.