bayesGAM (version 0.0.2)

loo_compare_bgam: Calls the loo package to compare models fit by bayesGAMfit

Description

Compares fitted models based on ELPD, the expected log pointwise predictive density for a new dataset.

Usage

loo_compare_bgam(object, ...)
# S4 method for bayesGAMfit
loo_compare_bgam(object, ...)

Arguments

object

Object of type bayesGAMfit generated from bayesGAM.

...

Additional objects of type bayesGAMfit

Value

a matrix with class compare.loo that has its own print method from the loo package

References

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

Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413<U+2013>1432. doi:10.1007/s11222-016-9696-4 (journal version, preprint arXiv:1507.04544).

Vehtari, A., Gelman, A., and Gabry, J. (2017b). Pareto smoothed importance sampling. preprint arXiv:1507.02646

Vehtari A, Gabry J, Magnusson M, Yao Y, Gelman A (2019). <U+201C>loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models.<U+201D> R package version 2.2.0, <URL: https://mc-stan.org/loo>.

Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), Visualization in Bayesian workflow. J. R. Stat. Soc. A, 182: 389-402. doi:10.1111/rssa.12378

Examples

# NOT RUN {
f1 <- bayesGAM(weight ~ height, data = women,
              family = gaussian, iter=500, chains = 1)
f2 <- bayesGAM(weight ~ np(height), data=women, 
              family = gaussian, iter=500, chains = 1)
loo_compare_bgam(f1, f2)
# }