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cpdag {bnlearn} | R Documentation |
Equivalence classes, moral graphs and consistent extensions
Description
Find the equivalence class and the v-structures of a Bayesian network, construct its moral graph, or create a consistent extension of an equivalent class.
Usage
cpdag(x, wlbl = FALSE, debug = FALSE)
cextend(x, strict = TRUE, debug = FALSE)
moral(x, debug = FALSE)
cextend.all(x, debug = FALSE)
colliders(x, arcs = FALSE, debug = FALSE)
shielded.colliders(x, arcs = FALSE, debug = FALSE)
unshielded.colliders(x, arcs = FALSE, debug = FALSE)
vstructs(x, arcs = FALSE, debug = FALSE)
Arguments
x |
an object of class |
arcs |
a boolean value. If |
wlbl |
a boolean value. If |
strict |
a boolean value. If no consistent extension is possible and |
debug |
a boolean value. If |
Details
Note that arcs whose directions are dictated by the parametric assumptions of the network are preserved
as directed arcs in cpdag()
. This means that, in a conditional Gaussian network, arcs from
discrete nodes to continuous nodes will be treated as whitelisted in their only valid direction.
cextend.all()
returns all possible consistent extensions of a CPDAG, whereas
cextend()
returns only one.
Value
cpdag()
returns an object of class bn
, representing the equivalence class.
moral
on the other hand returns the moral graph. See bn-class
for details.
cextend()
returns an object of class bn
, representing a DAG that is the
consistent extension of x
.
cextend.all()
returns an object of class bn
or a list of objects of class
bn
.
vstructs()
, colliders()
, shielded.colliders()
and
unshielded.colliders()
return a matrix with either 2 or 3 columns, according to the value of
the arcs
argument.
Author(s)
Marco Scutari
References
Dor D (1992). A Simple Algorithm to Construct a Consistent Extension of a Partially Oriented Graph. UCLA, Cognitive Systems Laboratory.
Koller D, Friedman N (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
Pearl J (2009). Causality: Models, Reasoning and Inference. Cambridge University Press, 2nd edition.
Andersson SA, Madigan D, Perlman MD (1997). "A Characterization of Markov Equivalence Classes for Acyclic Digraphs." The Annals of Statistics, 25(2):505–541.
Wienobst M, Luttermann M, Bannach M, Liskiewicz M (2023). "Efficient Enumeration of Markov Equivalent DAGs." Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 12313–12320.
Examples
data(learning.test)
dag = hc(learning.test)
cpdag(dag)
vstructs(dag)
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