WAIC: WAIC and LOO

In FlexReg: Regression Models for Bounded Continuous and Discrete Responses

WAIC and LOO

Description

The function computes widely applicable information criterion (WAIC) and efficient approximate leave-one-out cross-validation (LOO) from fitted regression model objects of class `flexreg`.

Usage

WAIC(model, ...)

## S3 method for class 'WAIC.flexreg'
print(x, ...)

Arguments

model	an object (or a list of objects) of class `flexreg`, usually the result of flexreg or flexreg_binom functions.
...	additional arguments.
x	an object of class `WAIC.flexreg`, usually the result of WAIC.

Details

This function takes advantage of the loo package to compute the widely applicable information criterion (WAIC) and leave-one-out cross-validation (LOO) for objects of class `flexreg`. If a list of two or more objects of class `flexreg` is provided, the function returns the difference in their expected predictive accuracy (see loo_compare for further details).

Value

A named list with components from loo and waic.

References

Vehtari, A., Gelman, A., Gabry, J. (2017). 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

Examples

## Not run: 
data("Reading")
FB <- flexreg(accuracy.adj ~ iq, data = Reading, type="FB", n.iter=1000)
WAIC(FB)

## End(Not run)

FlexReg documentation built on Sept. 29, 2023, 9:06 a.m.