Compute approximate leave-one-out cross-validation

Computes Pareto-smoothed importance-sampling leave-one-out cross-validation using loo::loo().

Usage

loo(x, ...)

# S3 method for class 'RprobitB_fit'
loo(x, ghk_draws = 500L, progress = interactive(), ...)

Arguments

x

[RprobitB_fit]
Fitted choice model.

...

Further arguments passed to loo::loo().

ghk_draws

[integer(1)]
Number of draws of the GHK simulator for multivariate normal probabilities of more than three dimensions, see oeli::pmvnorm().

progress

[logical(1)]
Show progress?

Value

A psis_loo object from loo. It contains estimates and standard errors as well as one Pareto-k diagnostic per independent likelihood unit.

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 .

Vehtari A, Simpson D, Gelman A, Yao Y, Gabry J (2024). “Pareto Smoothed Importance Sampling.” Journal of Machine Learning Research, 25(72), 1–58. https://www.jmlr.org/papers/v25/19-556.html.

Examples

set.seed(1)
model <- fit(
  choice ~ x | 0, dgp_parameters = list(beta = c(x = 1)), n_occasions = 5,
  chains = 1
)
loo(model)
#> 
#> Computed from 500 by 100 log-likelihood matrix.
#> 
#>          Estimate   SE
#> elpd_loo   -185.5 12.2
#> p_loo         0.8  0.2
#> looic       371.0 24.3
#> ------
#> MCSE of elpd_loo is 0.0.
#> MCSE and ESS estimates assume independent draws (r_eff=1).
#> 
#> All Pareto k estimates are good (k < 0.63).
#> See help('pareto-k-diagnostic') for details.