network tools {bnlearn} | R Documentation |
Assign or extract various quantities of interest from an object of class bn
.
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)
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. |
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).
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.
nparams
supports only completely directed discrete Bayesian networks.
Marco Scutari
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.
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