misc utilities {bnlearn} | R Documentation |
Assign or extract various quantities of interest from an object of class bn
.
## nodes nodes(x) mb(x, node, rebuild = FALSE) nbr(x, node, rebuild = FALSE) parents(x, node, rebuild = FALSE) parents(x, node, debug = FALSE) <- value children(x, node, rebuild = FALSE) 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) ## adjacency matrix amat(x) amat(x, ignore.cycles = FALSE, debug = FALSE) <- value ## graphs nparams(x, data, debug = FALSE)
x |
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. |
rebuild |
a boolean value. If TRUE the return value is rebuilt
from scratch using the arc set; otherwise the cached value are returned. |
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. |
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).
mb
, nbr
, nodes
, parents
, rootnodes
and leafnodes
return a vector 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.
Marco Scutari
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.
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