bn class: The bn class structure
Description
The structure of an object of the bn S3 class.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.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).
arcs: the arcs of the Bayesian network (a two-column
matrix, whose columns are labeledfromandto).whitelist: a sanitized copy of thewhitelistparameter (a two-column matrix, whose columns are labeledfromandto).blacklist: a sanitized copy of theblacklistparameter (a two-column matrix, whose columns are labeledfromandto).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 score-based algorithms,
and "none" for randomly generated graphs.ntests: the number of conditional independence tests
used in the learning (an integer value).algo: the label of the algorithm used in the learning
process (a character string), or "random/generated" for randomly
generated or empty networks.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
numerical value) of the conditional independence tests.iss: a positive numerical value, the imaginary
sample size used by thebgeandbdescores.phi: a character string, eitherheckermanorbottcher; used by thebgescore.k: a positive numerical value, the penalty per
parameter used by theaicandbicscores.prob: the probability of each arc to be present in
a graph generated by theorderedgraph generation algorithm.burn.in: the number of iterations for theic-daggraph generation algorithm to converge to a stationary (and uniform)
probability distribution.max.degree: the maximum degree for any node in a graph
generated by theic-daggraph generation algorithm.max.in.degree: the maximum in-degree for any node in
a graph generated by theic-daggraph generation algorithm.max.out.degree: the maximum out-degree for any node in
a graph generated by theic-daggraph generation algorithm.
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 two-column
matrix, whose columns are labeledfromandto).