bn.fit.methods: Utilities to manipulate fitted Bayesian networks

In bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Utilities to manipulate fitted Bayesian networks

Description

Assign, extract or compute various quantities of interest from objects of class bn.fit, bn.fit.dnode, bn.fit.gnode, bn.fit.cgnode or bn.fit.onode.

Usage

## methods available for "bn.fit"
## S3 method for class 'bn.fit'
fitted(object, ...)
## S3 method for class 'bn.fit'
coef(object, ...)
## S3 method for class 'bn.fit'
residuals(object, ...)
## S3 method for class 'bn.fit'
sigma(object, ...)
## S3 method for class 'bn.fit'
logLik(object, data, nodes, by.sample = FALSE, na.rm = FALSE, debug = FALSE, ...)
## S3 method for class 'bn.fit'
AIC(object, data, ..., k = 1)
## S3 method for class 'bn.fit'
BIC(object, data, ...)

## non-method functions for "bn.fit"
identifiable(x, by.node = FALSE)
singular(x, by.node = FALSE)

## methods available for "bn.fit.dnode"
## S3 method for class 'bn.fit.dnode'
coef(object, for.parents, ...)

## methods available for "bn.fit.onode"
## S3 method for class 'bn.fit.onode'
coef(object, for.parents, ...)

## methods available for "bn.fit.gnode"
## S3 method for class 'bn.fit.gnode'
fitted(object, ...)
## S3 method for class 'bn.fit.gnode'
coef(object, ...)
## S3 method for class 'bn.fit.gnode'
residuals(object, ...)
## S3 method for class 'bn.fit.gnode'
sigma(object, ...)

## methods available for "bn.fit.cgnode"
## S3 method for class 'bn.fit.cgnode'
fitted(object, ...)
## S3 method for class 'bn.fit.cgnode'
coef(object, for.parents, ...)
## S3 method for class 'bn.fit.cgnode'
residuals(object,  ...)
## S3 method for class 'bn.fit.cgnode'
sigma(object, for.parents, ...)

Arguments

object	an object of class bn.fit, bn.fit.dnode, bn.fit.gnode, bn.fit.cgnode or bn.fit.onode.
x	an object of class bn.fit.
nodes	a vector of character strings, the labels of the nodes whose log-likelihood components are to be computed.
data	a data frame containing the variables in the model.
...	additional arguments, currently ignored.
k	a numeric value, the penalty coefficient to be used; the default k = 1 gives the expression used to compute AIC.
by.sample	a boolean value. If TRUE, logLik() returns a vector containing the log-likelihood of each observation in the sample. If FALSE, logLik() returns a single value, the log-likelihood of the whole sample.
by.node	a boolean value. If TRUE, identifiable() and singular() return a vector containing one value for each local distribution. If FALSE, identifiable() and singular() return a single value for the whole model.
na.rm	a boolean value, whether missing values should be used in computing the log-likelihood. See below for details. The default value is FALSE, and it only has an effect if by.sample = FALSE.
debug	a boolean value. If TRUE, a lot of debugging output is printed. Otherwise the function is completely silent.
for.parents	a named list in which each element contains a set of values for the discrete parents of the nodes. coef() and sigma() will only return the parameters associated with those parent configurations. (Only relevant for conditional Gaussian nodes.)

Details

coef() (and its alias coefficients()) extracts model coefficients (that is, conditional probabilities for discrete nodes; linear regression coefficients for Gaussian and conditional Gaussian nodes; regression coefficients of the count and zero-inflation components for zero-inflated nodes).

residuals() (and its alias resid()) extracts model residuals, and fitted() (and its alias
fitted.values()) extracts fitted values from nodes other than discrete ones. If the bn.fit object does not include the residuals or the fitted values for the node of interest, both functions return NULL.

sigma() extracts the standard deviations of the residuals from Gaussian and conditional Gaussian networks and nodes.

logLik() returns the log-likelihood for the observations in data. If na.rm is set to TRUE, the log-likelihood will be NA if the data contain missing values. If na.rm is set to FALSE, missing values will be dropped, and the log-likelihood will be computed using only locally-complete observations (effectively returning the node-average log-likelihood times the sample size). Note that the log-likelihood may be NA even if na.rm = TRUE if the network contains NA parameters or is singular.

The for.parents argument in the methods for coef() and sigma() can be used to have both functions return the parameters associated with a specific configuration of the discrete parents of a node. If for.parents is not specified, all relevant parameters are returned.

Value

logLik() returns a numeric vector or a single numeric value, depending on the value of by.sample. AIC and BIC always return a single numeric value.

All the other functions return a list with an element for each node in the network (if object has class bn.fit) or a numeric vector or matrix (if object has class bn.fit.dnode, bn.fit.gnode, bn.fit.cgnode, bn.fit.onode, bn.fit.zihpnode, bn.fit.zinbnode.).

Author(s)

Marco Scutari

See Also

bn.fit, bn.fit-class.

Examples

data(gaussian.test)
dag = hc(gaussian.test)
fitted = bn.fit(dag, gaussian.test)
coefficients(fitted)
coefficients(fitted$C)
str(residuals(fitted))

data(learning.test)
dag2 = hc(learning.test)
fitted2 = bn.fit(dag2, learning.test)
coefficients(fitted2$E)
coefficients(fitted2$E, for.parents = list(F = "a", B = "b"))

bnlearn documentation built on July 17, 2026, 5:08 p.m.