node ordering utilities {bnlearn} | R Documentation |
Find the partial node ordering implied by a network or generate the blacklist implied by a complete node ordering.
node.ordering(x, debug = FALSE) ordering2blacklist(nodes) tiers2blacklist(tiers)
x |
an object of class |
nodes |
a vector of character strings, the node ordering. |
tiers |
a vector of character strings or a list, see below. |
debug |
a boolean value. If |
ordering2blacklist()
takes a vector of character strings (the labels
of the nodes), which specifies a complete node ordering. An object of class
bn
or bn.fit
; in that case, the node ordering is derived by
the graph. In both cases, the blacklist returned by ordering2blacklist()
contains all the possible arcs that violate the specified node ordering.
tiers2blacklist()
takes (again) a vector of character strings (the
labels of the nodes), which specifies a complete node ordering, or a list of
character vectors, which specifies a partial node ordering. In the latter
case, all arcs going from a node in a particular element of the list
(sometimes known as tier) to a node in one of the previous elements
are blacklisted. Arcs between nodes in the same element are not blacklisted.
node.ordering()
returns a vector of character strings, an ordered set of
node labels.
ordering2blacklist()
and tiers2blacklist()
return a sanitized
blacklist
(a two-column matrix, whose columns are labeled from
and to
).
node.ordering()
and ordering2blacklist()
support only completely
directed Bayesian networks.
Marco Scutari
data(learning.test) res = gs(learning.test) ntests(res) res = set.arc(res, "A", "B") ord = node.ordering(res) ord ## partial node ordering saves us two tests in the v-structure ## detection step of the algorithm. ntests(gs(learning.test, blacklist = ordering2blacklist(ord))) tiers2blacklist(list(LETTERS[1:3], LETTERS[4:6]))