constraint-based algorithms {bnlearn} | R Documentation |
Learn the equivalence class of a directed acyclic graph (DAG) from data using the Grow-Shrink (GS), the Incremental Association (IAMB), the Fast Incremental Association (Fast IAMB) or the Interleaved Incremental Association (Inter IAMB) constraint-based algorithms.
gs(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL, alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE, undirected = FALSE) iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL, alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE, undirected = FALSE) fast.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL, alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE, undirected = FALSE) inter.iamb(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL, alpha = 0.05, B = NULL, debug = FALSE, optimized = TRUE, strict = FALSE, undirected = FALSE)
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. If none is
specified, the default test statistic is the mutual information
for discrete data sets and the linear correlation for
continuous ones. See bnlearn-package for details. |
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. |
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. |
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. |
An object of class bn
.
See bn-class
for details.
Marco Scutari
for Grow-Shrink (GS):
Margaritis D (2003). Learning Bayesian Network Model Structure from Data. Ph.D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. Available as Technical Report CMU-CS-03-153.
for Incremental Association (IAMB):
Tsamardinos I, Aliferis CF, Statnikov A (2003). "Algorithms for Large Scale Markov Blanket Discovery". In "Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference", pp. 376-381. AAAI Press.
for Fast IAMB and Inter IAMB:
Yaramakala S, Margaritis D (2005). "Speculative Markov Blanket Discovery for Optimal Feature Selection". In "ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining", pp. 809-812. IEEE Computer Society.
local discovery algorithms
,
score-based algorithms
, hybrid algorithms
.