iamb {bnlearn} | R Documentation |
Estimate the equivalence class of a directed acyclic graph (DAG) from data using the Incremental Association (IAMB) Constraint-based algorithm.
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
I. Tsamardinos, C. F. Aliferis, and A. Statnikov. Algorithms for large scale Markov blanket discovery. In Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, pages 376-381. AAAI Press, 2003.