loo_compare_bgam: Calls the 'loo' package to compare models fit by...

In bayesGAM: Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo

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 signature '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), 1413–1432. 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). “loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models.” 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

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)

bayesGAM documentation built on March 18, 2022, 6:29 p.m.