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