bn.fit.bn.fit is a list whose elements correspond to
the nodes of the Bayesian network. If the latter is discrete (i.e. the
nodes are multinomial random variables), the object also has class
bn.fit.dnet; each node has class bn.fit.dnode and
contains the following elements:
node: a character string, the label of the node.parents: a vector of character strings, the labels of
the parents of the node.children: a vector of character strings, the labels of
the children of the node.prob: a (multi)dimensional numeric table, the
conditional probability table of the node given its parents. Nodes encoding ordinal variables (i.e. ordered factors) have class
bn.fit.onode and contain the same elements as bn.fit.dnode
nodes. Networks containing only ordinal nodes also have class
bn.fit.onet, while those contatining both ordinal and multinomial
nodes also have class bn.fit.donet.
If on the other hand the network is continuous (i.e. the nodes are
Gaussian random variables), the object also has class bn.fit.gnet;
each node has class bn.fit.gnode and contains the following elements:
node: a character string, the label of the node.parents: a vector of character strings, the labels of
the parents of the node.children: a vector of character strings, the labels of
the children of the node.coefficients: a numeric vector, the linear regression
coefficients of the parents against the node.residuals: a numeric vector, the residuals of the
linear regression.fitted.values: a numeric vector, the fitted mean values
of the linear regression.sd: a numeric value, the standard deviation of the
residuals (i.e. the standard error). Hybrid (i.e. conditional linear Gaussian) networks also have class
bn.fit.gnet. Gaussian nodes have class bn.fit.gnode,
discrete nodes have class bn.fit.dnode and conditional Gaussian
nodes have class bn.fit.cgnode. Each node contains the following
elements:
node: a character string, the label of the node.parents: a vector of character strings, the labels of
the parents of the node.children: a vector of character strings, the labels of
the children of the node.dparents: an integer vector, the indexes of the discrete
parents inparents.gparents: an integer vector, the indexes of the continuous
parents inparents.dlevels: a list containing the levels of the discrete
parents inparents.coefficients: a numeric matrix, the linear regression
coefficients of the continuous parents. Each column corresponds to
a configuration of the discrete parents.residuals: a numeric vector, the residuals of the linear
regression.fitted.values: a numeric vector, the fitted mean values
of the linear regression.configs: an integer vector, the indexes of the
configurations of the discrete parents.sd: a numeric vector, the standard deviation of the
residuals (i.e. the standard error) for each configuration of the
discrete parents. Furthermore, Bayesian network classifiers store the label of the training
node in an additional attribute named training.