FlexDir (version 1.0)

FD.aicbic: Information Criterions of a Flexible Dirichlet Model

Description

Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) of a fitted Flexible Dirichlet model. An Information Criterion for one fitted model object for which a log-likelihood value can be obtained is defined as \( -2*log-likelihood + k*npar\), where \( npar\) represents the number of parameters in the fitted model, and \( k = 2\) for AIC, or \( k = log(n)\) for BIC (\( n\) being the number of observations).

Usage

FD.aicbic(x)

Arguments

x

an object of class FDfitted, usually the result of FD.estimation.

See Also

FD.estimation, FD.stddev, FD.barycenters

Examples

data <- FD.generate(n=20,a=c(12,7,15),p=c(0.3,0.4,0.3),t=8)
data
results <- FD.estimation(data, normalize=TRUE,iter.initial.SEM = 5,iter.final.EM = 10)
results
FD.aicbic(results)