Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

graph

Utilities to manipulate graphs

Description

Check and manipulate graph-related properties of an object of class bn.

Usage

# check whether the graph is acyclic/completely directed.
acyclic(x, directed = FALSE, debug = FALSE)
directed(x)
# check whether there is a path between two nodes.
path.exists(x, from, to, direct = TRUE, underlying.graph = FALSE, debug = FALSE)
# deprecated alias of path.exists().
## S4 method for signature 'bn'
path(object, from, to, direct = TRUE, underlying.graph = FALSE,
    debug = FALSE)
## S4 method for signature 'bn.fit'
path(object, from, to, direct = TRUE,
    underlying.graph = FALSE, debug = FALSE)
# build the skeleton or a complete orientation of the graph.
skeleton(x)
pdag2dag(x, ordering)
# build a subgraph spanning a subset of nodes.
subgraph(x, nodes)

Arguments

x, object

an object of class bn. skeleton(), acyclic(), directed(), path.exixsts() and path() also accept objects of class bn.fit.
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.
underlying.graph

a boolean value. If TRUE the underlying undirected graph is used instead of the (directed) one from the x argument.
ordering

the labels of all the nodes in the graph; their order is the node ordering used to set the direction of undirected arcs.
nodes

the labels of the nodes that induce the subgraph.
directed

a boolean value. If TRUE only completely directed cycles are considered; otherwise undirected arcs will also be considered and treated as arcs present in both directions.
debug

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

Value

acyclic(), path() and directed() return a boolean value.
skeleton(), pdag2dag() and subgraph() return an object of class bn.

Author(s)

Marco Scutari

References

Bang-Jensen J, Gutin G (2009). Digraphs: Theory, Algorithms and Applications. Springer, 2nd edition.

Examples

data(learning.test)
res = gs(learning.test)

acyclic(res)
directed(res)
res = pdag2dag(res, ordering = LETTERS[1:6])
res
directed(res)
skeleton(res)
bnlearn

Bayesian Network Structure Learning, Parameter Learning and Inference

v4.6.1
GPL (>= 2)
Authors
Marco Scutari [aut, cre], Robert Ness [ctb]
Initial release
2020-09-16

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.