bnlearn - Bayesian network structure learning

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
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
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)

Arguments

`x`	an object of class `bn` or `bn.fit`. The replacement form of `parents`, `children`, `arcs` and `amat` require an object of class `bn`.
`node`	a character string, the label of a node.
`value`	either a vector of character strings (for `parents` and `children`), an adjacency matrix (for `amat`) or a data frame with two columns (optionally labeled "from" and "to", for `arcs`).
`data`	a data frame containing the data the Bayesian network was learned from. It's only needed if `x` is an object of class `bn`.
`ignore.cycles`	a boolean value. If `TRUE` the returned network will not be checked for cycles.
`debug`	a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.

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).

Value

mb, nbr, nodes, parents, root.nodes and leaf.nodes return a vector of character strings.
arcs returns 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 returns an integer.

Author(s)

Marco Scutari

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

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