gs {bnlearn} | R Documentation |
Estimate the equivalence class of a directed acyclic graph (DAG) from data using the Grow-Shrink (GS) constraint-based algorithm.
gs(x, cluster = NULL, whitelist = NULL, blacklist = NULL, test = NULL, alpha = 0.05, debug = FALSE, optimized = TRUE, strict = FALSE, undirected = FALSE, direction = 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. |
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. |
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. This step is not included in the original
algorithm, and is intended as an experimental debugging tool. |
An object of class bn
.
See bn-class
for details.
Marco Scutari
D. Margaritis. Learning Bayesian Network Model Structure from Data. PhD thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, May 2003. Available as Technical Report CMU-CS-03-153.
iamb
, fast.iamb
, inter.iamb
,
mmpc
, hc
.