cpdag(x, moral = TRUE, debug = FALSE)
cextend(x, strict = TRUE, debug = FALSE)
vstructs(x, arcs = FALSE, moral = TRUE, debug = FALSE)
moral(x, debug = FALSE)bn.TRUE the arcs that are part
of at least one v-structure are returned instead of the v-structures
themselves.TRUE we define a v-structure as
in Pearl (2000); if FALSE, as in Koller and Friedman (2009).
See below.strict is TRUE, an error is generated; otherwise
a partially extended graph is returned with a warning.TRUE a lot of debugging output
is printed; otherwise the function is completely silent.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 parameter.
Setting moral to FALSE in cpdag and vstructs
makes those functions follow the definition from Koller and Friedman
(2009); the default value of TRUE, on the other hand, makes
those functions follow the definition from Pearl (2000). The former call
v-structures both shielded and unshielded colliders (respectively
moral v-structures and immoral v-structures); the latter
requires v-structures to be unshielded colliders.
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.
data(learning.test)
res = gs(learning.test)
cpdag(res)
vstructs(res)Run the code above in your browser using DataLab