cpdag {bnlearn}

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)

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 bn or bn.fit (with the exception of cextend, which only accepts objects of class bn).

arcs

a boolean value. If TRUE the arcs that are part of at least one v-structure are returned instead of the v-structures themselves.

wlbl

a boolean value. If TRUE arcs whose directions have been fixed by a whitelist or a by blacklist are preserved when constructing the CPDAG.

strict

a boolean value. If no consistent extension is possible and strict is TRUE, an error is generated; otherwise a partially extended graph is returned with a warning.

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

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.

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.

vstructs() returns 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.

Examples

data(learning.test)
dag = hc(learning.test)
cpdag(dag)
vstructs(dag)