FD.aicbic: Information Criterions of a Flexible Dirichlet Model

In FlexDir: Tools to Work with the Flexible Dirichlet Distribution

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)

FlexDir documentation built on May 2, 2019, 5:52 a.m.