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

In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference

Description

Assign, extract or compute various quantities of interest from an object of class bn.fit, bn.fit.dnode, bn.fit.gnode 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'
predict(object, node, data, ..., debug = FALSE)
## S3 method for class 'bn.fit'
logLik(object, data, nodes, by.sample = FALSE, ...)
## S3 method for class 'bn.fit'
AIC(object, data, ..., k = 1)
## S3 method for class 'bn.fit'
BIC(object, data, ...)

## methods available for "bn.fit.dnode"
## S3 method for class 'bn.fit.dnode'
coef(object, ...)
## S3 method for class 'bn.fit.dnode'
predict(object, data, ..., debug = FALSE)

## methods available for "bn.fit.onode"
## S3 method for class 'bn.fit.onode'
coef(object, ...)
## S3 method for class 'bn.fit.onode'
predict(object, data, ..., debug = FALSE)

## 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'
predict(object, data, ..., debug = FALSE)

Arguments

object	an object of class bn.fit, bn.fit.dnode or bn.fit.gnode.
node	a character string, the label of a node.
nodes	a vector of character strings, the label of a nodes whose loglikelihood 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 per parameter 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 the log-likelihood of each observations in the sample. If FALSE, logLik returns a single value, the likelihood of the whole sample.
debug	a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Details

coef (and its alias coefficients) extracts model coefficients (which are conditional probabilities in discrete networks and linear regression coefficients in Gaussian networks).

residuals (and its alias resid) extracts model residuals and fitted (and its alias fitted.values) extracts fitted values from fitted Gaussian networks. If the bn.fit object does not include the residuals or the fitted values (for the nodes of interest, in the case of bn.fit.gnode objects), both functions return NULL.

predict returns the predicted values for node for the data specified by data.

Value

predict returns a numeric vector (for Gaussian networks) or a factor (for discrete networks).

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 (if object has class bn.fit.dnode or bn.fit.gnode).

Note

Ties in prediction are broken using Bayesian tie breaking, i.e. sampling at random from the tied values. Therefore, setting the random seed is required to get reproducible results.

Author(s)

Marco Scutari

See Also

bn.fit, bn.fit-class.

Examples

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

data(learning.test)
res2 = hc(learning.test)
fitted2 = bn.fit(res2, learning.test)
coefficients(fitted2$E)

vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.