bnlearn (version 2.6)

misc utilities: Miscellaneous utilities

Description

Assign or extract various quantities of interest from an object of class bn of bn.fit.

Usage

## nodes
nodes(x)
mb(x, node)
nbr(x, node)
parents(x, node)
parents(x, node, debug = FALSE) <- value
children(x, node)
children(x, node, debug = FALSE) <- value
in.degree(x, node)
out.degree(x, node)
degree(x, node)
root.nodes(x)
leaf.nodes(x)
## arcs
arcs(x)
arcs(x, ignore.cycles = FALSE, debug = FALSE) <- value
directed.arcs(x)
undirected.arcs(x)
narcs(x)
## adjacency matrix
amat(x)
amat(x, ignore.cycles = FALSE, debug = FALSE) <- value
## graphs
nparams(x, data, debug = FALSE)
ntests(x)
whitelist(x)
blacklist(x)

Value

• mb, nbr, nodes, parents, root.nodes and leaf.nodes return a vector of character strings. arcs, whitelist and blacklist return a matrix of two columns of character strings. narcs returns the number of arcs in the graph. amat returns a matrix of 0/1 integer values. nparams and ntests returns an integer.

Details

The number of parameters of a discrete Bayesian network is defined as the sum of the number of logically independent parameters of each node given its parents (Chickering, 1995). For Gaussian Bayesian networks the distribution of each node can be viewed as a linear regression, so it has a number of parameters equal to the number of the parents of the node plus one (the intercept) as per Neapolitan (2003).

References

Chickering DM (1995). "A Transformational Characterization of Equivalent Bayesian Network Structures". In "UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence", pp. 87-98. Morgan Kaufmann.

Neapolitan RE (2003). Learning Bayesian Networks. Prentice Hall.

Examples

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

##  the Markov blanket of A.
mb(res, "A")
# [1] "B" "D" "C"
## the neighbourhood of F.
nbr(res, "F")
# [1] "E"
## the arcs in the graph.
arcs(res)
#      from to
# [1,] "A"  "B"
# [2,] "A"  "D"
# [3,] "B"  "A"
# [4,] "B"  "E"
# [5,] "C"  "D"
# [6,] "F"  "E"
## the nodes of the graph.
nodes(res)
# [1] "A" "B" "C" "D" "E" "F"
## the adjacency matrix for the nodes of the graph.
amat(res)
#   A B C D E F
# A 0 1 0 1 0 0
# B 1 0 0 0 1 0
# C 0 0 0 1 0 0
# D 0 0 0 0 0 0
# E 0 0 0 0 0 0
# F 0 0 0 0 1 0
## the parents of D.
parents(res, "D")
# [1] "A" "C"
## the children of A.
children(res, "A")
# [1] "D"
## the root nodes of the graph.
root.nodes(res)
# [1] "C" "F"
## the leaf nodes of the graph.
leaf.nodes(res)
# [1] "D" "E"
## number of parameters of the Bayesian network.
res = set.arc(res, "A", "B")
nparams(res, learning.test)
# [1] 41