bnlearn (version 0.4)

network tools: Utilities to manipulate graphs

Description

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

Usage

mb(x, node, rebuild = FALSE)
  nbr(x, node, rebuild = FALSE)
  arcs(x)
  arcs(x, debug = FALSE) <- value
  nodes(x)
  amat(x)
  amat(x, debug = FALSE) <- value
  parents(x, node, rebuild = FALSE)
  parents(x, node, debug = FALSE) <- value
  children(x, node, rebuild = FALSE)
  children(x, node, debug = FALSE) <- value
  nparams(x, data, debug = FALSE)

Arguments

x

an object of class "bn".

node

a character string, the label of a node.

value

either an array 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.

rebuild

a boolean value. If TRUE the return value is rebuilt from scratch using the arc list; otherwise the cached value are returned.

debug

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

Value

• mb, nbr, nodes and parents return an array of character strings. arcs returns a matrix of two columns of character strings. amat returns a matrix of 0/1 numeric values. nparams returns an integer.

Details

The number of parameters of a discrete Bayesian network is computed as the sum of the number of logically independent parameters of each node given its parents (Chickering, 1995).

References

D. M. Chickering. A Transformational Characterization of Equivalent Bayesian Network Structures. In Proceedins of 11th Conference on Uncertainty in Artificial Intelligence, pages 87-98. Morgan Kaufmann Publishers Inc., 1995.

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"
children(res, "A")
# [1] "D"
# number of parameters of the baysesian network.
res = set.arc(res, "A", "B")
nparams(res, learning.test)
# [1] 41