BIC-methods: BIC criterion.

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

Description

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

BIC=log-likelihood - 0.5*ν*log(n)

where ν denotes the number of parameters in the fitted model and n represents the sample size.

Usage

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

Arguments

object

instance of VSLCMresults.

References

Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464.

Examples

# Data loading:
data(heart)

# Cluster analysis without variable selection (number of clusters between 1 and 3)
res<- VarSelCluster(heart[,-13], 2, vbleSelec = FALSE)

# Get the BIC value
BIC(res)

Example output

Attaching package: 'VarSelLCM'

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

    predict

[1] -6520.294

VarSelLCM documentation built on Oct. 23, 2020, 8:20 p.m.