Compute leave-one-out cross-validation using Pareto-smoothed importance sampling (PSIS-LOO).
Usage
# S3 method for class 'ratiod_fit'
loo(x, ...)Details
The pointwise log-likelihood is reconstructed from the posterior draws and
the model (see ratiod_compare()); it works on any fit produced by the
hmc, ess, sghmc, or vi backend, for every family including the zero-inflated,
one-inflated and hurdle ones. Each observation's likelihood is evaluated at
the full linear predictor: fixed, random and structured effects.
Examples
# \donttest{
sim <- sim_ratiod(n = 100, family = ratiod_poisson_gamma(), seed = 1)
fit <- tratio(y_num | y_denom ~ x1 + (1 | group), data = sim$data,
family = ratiod_poisson_gamma(),
control = list(iter = 1000, chains = 2, seed = 1))
loo(fit)
#>
#> Computed from 1000 by 100 log-likelihood matrix.
#>
#> Estimate SE
#> elpd_loo -439.2 14.6
#> p_loo 13.2 2.0
#> looic 878.5 29.2
#> ------
#> MCSE of elpd_loo is 0.2.
#> MCSE and ESS estimates assume independent draws (r_eff=1).
#>
#> All Pareto k estimates are good (k < 0.67).
#> See help('pareto-k-diagnostic') for details.
# }