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misc utilities {bnlearn}  R Documentation 
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)
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
## 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
x , object 
an object of class 
node , Nodes 
a character string, the label of a node. 
value 
either a vector of character strings (for 
data 
a data frame containing the data the Bayesian network was learned from. It's only needed if

check.cycles 
a boolean value. If 
check.illegal 
a boolean value. If 
debug 
a boolean value. If 
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).
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.
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 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)
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