constraint: Constraint-based structure learning algorithms

In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference

Description

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. Also use the same algorithms to learn the Markov blanket of a single variable.

Usage

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)

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. 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 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.

Value

An object of class bn. See bn-class for details.

Author(s)

Marco Scutari

References

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.

See Also

local discovery algorithms, score-based algorithms, hybrid algorithms.

vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.