R: Watanabe-Akaike Information Criterion (WAIC)

WAIC {bamlss} | R Documentation

## 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)

[Package bamlss version 1.1-6 Index]