Index  Topics 
bn class {bnlearn}  R Documentation 
The bn class structure
Description
The structure of an object of S3 class bn
.
Details
An object of class bn
is a list containing at least the following components:

learning
: a list containing some information about the results of the learning algorithm. It's never changed afterward.
whitelist
: a copy of thewhitelist
argument (a twocolumn matrix, whose columns are labeledfrom
andto
) as transformed by sanitization functions. 
blacklist
: a copy of theblacklist
argument (a twocolumn matrix, whose columns are labeledfrom
andto
) as transformed by sanitization functions. 
test
: the label of the conditional independence test used by the learning algorithm (a character string); the label of the network score is used for scorebased algorithms; the label of the newtork score used in the “Maximize” phase of hybrid algorithms; "none" for randomly generated graphs. For hybrid algorithms,test
always has the same value asmaxscore
(see below). 
ntests
: the number of conditional independence tests or score comparisons used in the learning (an integer value). 
algo
: the label of the learning algorithm or the random generation algorithm used to generate the network (a character string). 
args
: a list. The values of the parameters of either the conditional tests or the scores used in the learning process. Only the relevant ones are stored, so this may be an empty list.
alpha
: the target nominal type I error rate (a numeric value) of the conditional independence tests. 
iss
: a positive numeric value, the imaginary sample size used by thebge
andbde
scores. 
phi
: a character string, eitherheckerman
orbottcher
; used by thebge
score. 
k
: a positive numeric value, the penalty coefficient used by theaic
,aicg
,bic
andbicg
scores. 
prob
: the probability of each arc to be present in a graph generated by theordered
graph generation algorithm. 
burn.in
: the number of iterations for theicdag
graph generation algorithm to converge to a stationary (and uniform) probability distribution. 
max.degree
: the maximum degree for any node in a graph generated by theicdag
graph generation algorithm. 
max.in.degree
: the maximum indegree for any node in a graph generated by theicdag
graph generation algorithm. 
max.out.degree
: the maximum outdegree for any node in a graph generated by theicdag
graph generation algorithm. 
training
: a character string, the label of the training node in a Bayesian network classifier. 
threshold
: the threshold used to determine which arcs are significant when averaging network structures.



nodes
: a list. Each element is named after a node and contains the following elements:
mb
: the Markov blanket of the node (a vector of character strings). 
nbr
: the neighbourhood of the node (a vector of character strings). 
parents
: the parents of the node (a vector of character strings). 
children
: the children of the node (a vector of character strings).


arcs
: the arcs of the Bayesian network (a twocolumn matrix, whose columns are labeledfrom
andto
). Undirected arcs are stored as two directed arcs with opposite directions between the corresponding incident nodes.
Additional (optional) components under learning
:

optimized
: whether additional optimizations have been used in the learning algorithm (a boolean value). 
illegal
: arcs that are illegal according to the parametric assumptions used to learn the network structure (a twocolumn matrix, whose columns are labeledfrom
andto
). 
restrict
: the label of the constraintbased algorithm used in the “Restrict” phase of a hybrid learning algorithm (a character string). 
rtest
: the label of the conditional independence test used in the “Restrict” phase of a hybrid learning algorithm (a character string). 
maximize
: the label of the scorebased algorithm used in the “Maximize” phase of a hybrid learning algorithm (a character string). 
maxscore
: the label of the network score used in the “Maximize” phase of a hybrid learning algorithm (a character string). 
max.sx
: the maximum allowed size of the conditioning sets in the conditional independence tests used in constraintbased algorithms.
Author(s)
Marco Scutari
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