This is used to evaluate the fit of the model using the Watanabe-Akaike Information criteria

get_waic(bpc_object)

## Arguments

## Value

a loo object

## References

Gelman, Andrew, Jessica Hwang, and Aki Vehtari. Understanding predictive information criteria for Bayesian models. Statistics and computing 24.6 (2014): 997-1016.

## Examples

# \donttest{
m<-bpc(data = tennis_agresti,
player0 = 'player0',
player1 = 'player1',
result_column = 'y',
model_type = 'bt',
solve_ties = 'none')
#> Running MCMC with 4 parallel chains...
#>
#> Chain 3 finished in 3.2 seconds.
#> Chain 2 finished in 3.3 seconds.
#> Chain 1 finished in 3.4 seconds.
#> Chain 4 finished in 3.4 seconds.
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 3.3 seconds.
#> Total execution time: 3.5 seconds.
waic<-get_waic(m)
print(waic)
#>
#> Computed from 8000 by 46 log-likelihood matrix
#>
#> Estimate SE
#> elpd_waic -30.5 3.6
#> p_waic 4.3 0.6
#> waic 60.9 7.3
# }