loglik: Compute (penalized) log-likelihood.

In bnclassify: Learning Discrete Bayesian Network Classifiers from Data

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 class 'bnc_bn'
AIC(object, ...)

## S3 method for class 'bnc_bn'
BIC(object, ...)

## S3 method for class '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)   

bnclassify documentation built on June 22, 2024, 11:08 a.m.