bayesGAM (version 0.0.2)

waic_bgam: Calls the loo package to calculate the widely applicable information criterion (WAIC)

Description

Computes WAIC by calling the appropriate function from the loo package

Usage

waic_bgam(object, ...)
# S4 method for bayesGAMfit
waic_bgam(object, ...)
# S4 method for array
waic_bgam(object, ...)

Arguments

object

Object of type bayesGAMfit generated from bayesGAM.

...

Additional parameters to pass to pass to loo::waic

Value

a named list of class c("waic", "loo")

estimates

A matrix with two columns ("Estimate", "SE") and three rows ("elpd_waic", "p_waic", "waic"). This contains point estimates and standard errors of the expected log pointwise predictive density (elpd_waic), the effective number of parameters (p_waic) and the information criterion waic (which is just -2 * elpd_waic, i.e., converted to deviance scale).

pointwise

A matrix with three columns (and number of rows equal to the number of observations) containing the pointwise contributions of each of the above measures (elpd_waic, p_waic, waic).

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>.

Examples

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