Widely Application Information Criteria (WAIC) for model comparison

`waic(fit, pointwise = FALSE, digits = 2)`

## Source

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.

## Arguments

- fit
An `geostan_fit`

object or any Stan model with a parameter named "log_lik", the pointwise log likelihood of the observations.

- pointwise
Logical (defaults to `FALSE`

), should a vector of values for each observation be returned?

- digits
Round results to this many digits.

## Value

A vector of length 3 with `WAIC`

, a rough measure of the effective number of parameters estimated by the model `Eff_pars`

, and log predictive density `Lpd`

. If `pointwise = TRUE`

, results are returned in a `data.frame`

.

## Examples

```
data(georgia)
fit <- stan_glm(log(rate.male) ~ 1, data = georgia,
chains = 2, iter = 800) # for speed only
waic(fit)
```