This is used to evaluate the fit of the model using entropy criteria

get_loo(bpc_object)

## Arguments

## Value

a loo object

## References

Vehtari A, Gelman A, Gabry J (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing_, 27, 1413-1432

## 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 3.3 seconds.
#> Chain 1 finished in 3.5 seconds.
#> Chain 2 finished in 3.6 seconds.
#> Chain 4 finished in 3.6 seconds.
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 3.5 seconds.
#> Total execution time: 3.6 seconds.
l<-get_loo(m)
print(l)
#>
#> Computed from 8000 by 46 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -30.6 3.7
#> p_loo 4.4 0.6
#> looic 61.1 7.3
#> ------
#> Monte Carlo SE of elpd_loo is 0.0.
#>
#> All Pareto k estimates are good (k < 0.5).
#> See help('pareto-k-diagnostic') for details.
# }