model.selection.scores: Model Selection Scores for the Number of Components for...

In BMRMM: An Implementation of the Bayesian Markov (Renewal) Mixed Models

Model Selection Scores for the Number of Components for Duration Times

Description

Provides the LPML (Geisser and Eddy, 1979) and WAIC (Watanabe, 2010) scores of the Bayesian Markov renewal mixture models

Usage

model.selection.scores(object)

Arguments

object

An object of class BMRMM.

Details

The two scores can be used to compare different choices of isi_num_comp, i.e., the number of the mixture gamma components. Larger values of LPML and smaller values of WAIC indicate better model fits.

Value

a list consisting of LPML and WAIC scores for gamma mixture models.

References

Geisser, S. and Eddy, W. F. (1979). A predictive approach to model selection. Journal of the American Statistical Association, 74, 153–160.

Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research, 11, 3571–3594.

Examples

results <- BMRMM(foxp2sm, num.cov = 2, simsize = 50, 
                 duration.distr = list('mixgamma',shape=rep(1,3),rate=rep(1,3)))
model.selection.scores(results)

BMRMM documentation built on July 9, 2023, 7:37 p.m.