graph utilities {bnlearn} | R Documentation |
Check and manipulate graph-related properties of an object of class bn
.
# check whether the graph is acyclic/completely directed. acyclic(x, directed, debug = FALSE) directed(x) # check whether there is a path between two nodes. path(x, from, to, direct = TRUE, underlying.graph = FALSE, debug = FALSE) # build the skeleton or a complete orientation of the graph. dag2ug(x) pdag2dag(x, ordering)
x |
an object of class bn . |
from |
a character string, the label of a node. |
to |
a character string, the label of a node (different from from ). |
direct |
a boolean value. If FALSE ignore any arc between from
and to when looking for a path. |
directed |
a boolean value. If TRUE the graph is assumed to be
completely directed (no undirected arcs), and a faster cycle detection
algorithm is used. |
underlying.graph |
a boolean value. If TRUE the underlying undirected
graph is used instead of the (directed) one from the x parameter. |
ordering |
the labels of all the nodes in the graph; their order is the node ordering used to set the direction of undirected arcs. |
debug |
a boolean value. If TRUE a lot of debugging output is
printed; otherwise the function is completely silent. |
acyclic
, path
and directed
return a boolean value.
dag2ug
and pdag2dag
return an object of class bn
.
Marco Scutari
Bang-Jensen J, Gutin G (2009). Digraphs: Theory, Algorithms and Applications. Springer, 2nd edition.
data(learning.test) res = gs(learning.test) acyclic(res) # [1] TRUE directed(res) # [1] FALSE res = pdag2dag(res, ordering = LETTERS[1:6]) res # # Bayesian network learned via Constraint-based methods # # model: # [A][C][F][B|A][D|A:C][E|B:F] # nodes: 6 # arcs: 5 # undirected arcs: 0 # directed arcs: 5 # average markov blanket size: 2.33 # average neighbourhood size: 1.67 # average branching factor: 0.83 # # learning algorithm: grow-shrink # conditional independence test: mutual information (discrete) # alpha threshold: 0.05 # tests used in the learning procedure: 41 # optimized: TRUE # directed(res) # [1] TRUE dag2ug(res) # # Bayesian network learned via Constraint-based methods # # model: # [partially directed graph] # nodes: 6 # arcs: 5 # undirected arcs: 5 # directed arcs: 0 # average markov blanket size: 1.67 # average neighbourhood size: 1.67 # average branching factor: 0.00 # # learning algorithm: grow-shrink # conditional independence test: mutual information (discrete) # alpha threshold: 0.05 # tests used in the learning procedure: 41 # optimized: TRUE #