WAIC: Watanabe-Akaike Information Criterion (WAIC)

In bamlss: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

Watanabe-Akaike Information Criterion (WAIC)

Description

Function returning the Watanabe-Akaike Information Criterion (WAIC) of a fitted model object.

Usage

WAIC(object, ..., newdata = NULL)

Arguments

object	A fitted model object which contains MCMC samples.
...	Optionally more fitted model objects.
newdata	Optionally, use new data for computing the WAIC.

Value

A data frame containing the WAIC and estimated number of parameters.

References

Watanabe S. (2010). Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. The Journal of Machine Learning Research, 11, 3571–3594. https://jmlr.org/papers/v11/watanabe10a.html

Examples

## Not run: d <- GAMart()
b1 <- bamlss(num ~ s(x1), data = d)
b2 <- bamlss(num ~ s(x1) + s(x2), data = d)
WAIC(b1, b2)

## End(Not run)

bamlss documentation built on Nov. 2, 2023, 5:31 p.m.