Miscellaneous utilities

Description

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

Usage

## nodes
mb(x, node)
nbr(x, node, max.dist = 1)
parents(x, node)
parents(x, node, debug = FALSE) <- value
children(x, node)
children(x, node, debug = FALSE) <- value
spouses(x, node)
ancestors(x, node)
descendants(x, node)
in.degree(x, node)
out.degree(x, node)
root.nodes(x)
leaf.nodes(x)
isolated.nodes(x)
nnodes(x)

## arcs
arcs(x)
arcs(x, check.cycles = TRUE, check.illegal = TRUE, debug = FALSE) <- value
directed.arcs(x)
undirected.arcs(x)
incoming.arcs(x, node)
outgoing.arcs(x, node)
incident.arcs(x, node)
compelled.arcs(x)
reversible.arcs(x)
narcs(x)

## adjacency matrix
amat(x)
amat(x, check.cycles = TRUE, check.illegal = TRUE, debug = FALSE) <- value

## arc list
alst(x)
alst(x, check.cycles = TRUE, check.illegal = TRUE, debug = FALSE) <- value

## graphs
nparams(x, data, debug = FALSE)
ntests(x)

## shared with the graph package.
# these used to be a simple nodes(x) function.
## S4 method for signature 'bn'
nodes(object)
## S4 method for signature 'bn.fit'
nodes(object)
# these used to be a simple degree(x, node) function.
## S4 method for signature 'bn'
degree(object, Nodes)
## S4 method for signature 'bn.fit'
degree(object, Nodes)

Arguments

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). For conditional linear Gaussian networks, the number of parameters of discrete and Gaussian nodes is as above. The number of parameters of conditional Gaussian nodes is equal to 1 plus the number of continuous parents (who get one regression coefficient each, plus the intercept) times the number of configurations of the discrete parents (each configuration has an associated regression model).

implements arc lists (equivalently adjacency lists) as a list with one element per node. Elements are named after the nodes in x, and contain a character vector holding the labels of the children of that node. Undirected arcs are treated as if they were bi-directed, so each node is listed as the other's child.

Value

mb(), nbr(), nodes(), parents(), children(), spouses(), ancestors(), descendants(), root.nodes(), leaf.nodes() and isolated.nodes() return a vector of character strings.

arcs(), directed.arcs(), undirected.arcs(), incoming.arcs(), outgoing.arcs(), incident.arcs(),
compelled.arcs(), reversible.arcs(), return a matrix of two columns of character strings.

narcs() and nnodes() return the number of arcs and nodes in the graph, respectively.

amat() returns a matrix of 0/1 integer values.

alst() returns a list of character vectors.

degree(), in.degree(), out.degree(), nparams() and ntests() return an integer.

Author(s)

Marco Scutari

References

Chickering DM (1995). "A Transformational Characterization of Equivalent Bayesian Network Structures." Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, 87–98.

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

Examples

data(learning.test)
cpdag = pc.stable(learning.test)

##  the Markov blanket of A.
mb(cpdag, "A")
## the neighbourhood of F.
nbr(cpdag, "F")
## the arcs in the graph.
arcs(cpdag)
## the nodes of the graph.
nodes(cpdag)
## the adjacency matrix for the nodes of the graph.
amat(cpdag)
## the adjacency list of a graph.
alst(cpdag)
## the parents of D.
parents(cpdag, "D")
## the children of A.
children(cpdag, "A")
## the root nodes of the graph.
root.nodes(cpdag)
## the leaf nodes of the graph.
leaf.nodes(cpdag)
## number of parameters of the Bayesian network.
dag = set.arc(cpdag, "A", "B")
nparams(dag, learning.test)