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 the whitelist argument (a two-column matrix, whose columns are labeled from and to) as transformed by sanitization functions.

    • blacklist: a copy of the blacklist argument (a two-column matrix, whose columns are labeled from and to) 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 score-based 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 as maxscore (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 the bge and bde scores.

      • phi: a character string, either heckerman or bottcher; used by the bge score.

      • k: a positive numeric value, the penalty coefficient used by the aic, aic-g, bic and bic-g scores.

      • prob: the probability of each arc to be present in a graph generated by the ordered graph generation algorithm.

      • burn.in: the number of iterations for the ic-dag 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 the ic-dag graph generation algorithm.

      • max.in.degree: the maximum in-degree for any node in a graph generated by the ic-dag graph generation algorithm.

      • max.out.degree: the maximum out-degree for any node in a graph generated by the ic-dag 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 two-column matrix, whose columns are labeled from and to). 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 two-column matrix, whose columns are labeled from and to).

  • restrict: the label of the constraint-based 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 score-based 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 constraint-based algorithms.

Author(s)

Marco Scutari