Add leave-one-out (LOO; Vehtari et al., 2017) and/or widely applicable information criteria (WAIC; Watanabe, 2010) to fitted model objects.

## Usage

```
add_criterion(
x,
criterion = c("loo", "waic"),
overwrite = FALSE,
save = TRUE,
...,
r_eff = NA
)
```

## Arguments

- x
A measrfit object.

- criterion
A vector of criteria to calculate and add to the model object.

- overwrite
Logical. Indicates whether existing criteria should be overwritten. For example, if LOO has already been added to the model object

`add_criterion()`

is called again with`criterion = "loo"`

, should LOO be recalculated? Default is`FALSE`

.- save
Logical. Only relevant if a file was specified in the measrfit object passed to

`x`

. If`TRUE`

(the default), the model is re-saved to the specified file when new criteria are added to the R object. If`FALSE`

, the new criteria will be added to the R object, but the saved file will not be updated.- ...
Additional arguments passed to

`loo::loo.array()`

or`loo::waic.array()`

.- r_eff
Vector of relative effective sample size estimates for the likelihood (

`exp(log_lik)`

) of each observation. This is related to the relative efficiency of estimating the normalizing term in self-normalizing importance sampling when using posterior draws obtained with MCMC. If MCMC draws are used and`r_eff`

is not provided then the reported PSIS effective sample sizes and Monte Carlo error estimates will be over-optimistic. If the posterior draws are independent then`r_eff=1`

and can be omitted. The warning message thrown when`r_eff`

is not specified can be disabled by setting`r_eff`

to`NA`

. See the`relative_eff()`

helper functions for computing`r_eff`

.

## Value

A modified measrfit object with the `criteria`

slot populated with
the specified criteria.

## 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.
https://doi.org/10.1007/s11222-016-9696-4

Watanabe, S. (2010). Asymptotic equivalence of Bayes cross
validation and widely applicable information criterion in singular learning
theory. *Journal of Machine Learning Research, 11*(116), 3571-3594.
http://jmlr.org/papers/v11/watanabe10a.html