AIC-methods: AIC criterion.

In VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Description

This function gives the AIC criterion of an instance of VSLCMresults. AIC is computed according to the formula

AIC=log-likelihood - ν

where ν denotes the number of parameters in the fitted model.

Usage

## S4 method for signature 'VSLCMresults'
AIC(object)

Arguments

object

instance of VSLCMresults.

References

Akaike, H. (1974), "A new look at the statistical model identification", IEEE Transactions on Automatic Control, 19 (6): 716-723.

Examples

# Data loading:
data(heart)

# Cluster analysis without variable selection
res <- VarSelCluster(heart[,-13], 2, vbleSelec = FALSE)

# Get the AIC value
AIC(res)

Example output

Attaching package: 'VarSelLCM'

The following object is masked from 'package:stats':

    predict

      Sex 
-6439.329 

VarSelLCM documentation built on May 2, 2019, 4:59 p.m.