bnclassify (version 0.4.8)

loglik: Compute (penalized) log-likelihood.

Description

Compute (penalized) log-likelihood and conditional log-likelihood score of a bnc_bn object on a data set. Requires a data frame argument in addition to object.

Usage

# S3 method for bnc_bn
AIC(object, ...)
# S3 method for bnc_bn
BIC(object, ...)
# S3 method for bnc_bn
logLik(object, ...)
cLogLik(object, ...)

Arguments

object

A bnc_bn object.

...

A data frame (\(\mathcal{D}\)).

Details

log-likelihood = \(log P(\mathcal{D} \mid \theta)\),

Akaike's information criterion (AIC) = \(log P(\mathcal{D} \mid \theta) - \frac{1}{2} |\theta|\),

The Bayesian information criterion (BIC) score: = \(log P(\mathcal{D} \mid \theta) - \frac{\log N}{2} |\theta|\),

where \(|\theta|\) is the number of free parameters in object, \(\mathcal{D}\) is the data set and N is the number of instances in \(\mathcal{D}\).

cLogLik computes the conditional log-likelihood of the model.

Examples

data(car)
nb <- bnc('nb', 'class', car, smooth = 1)
logLik(nb, car)   
AIC(nb, car)
BIC(nb, car)
cLogLik(nb, car)