model.selection.scores {BMRMM} | R Documentation |
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)