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 (D).

Details

log-likelihood = log P(D | θ),

Akaike's information criterion (AIC) = log P(D | θ) - |θ| / 2,

The Bayesian information criterion (BIC) score: = log P(D | θ) - N |θ| / 2,

where |θ| is the number of free parameters in object, D is the data set and N is the number of instances in 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 Nov. 16, 2022, 5:08 p.m.