Compute Widely Applicable Information Criterion (WAIC).
Usage
# S3 method for class 'ratiod_fit'
waic(x, ...)Details
Uses the same reconstructed pointwise log-likelihood as loo.ratiod_fit();
supported on the hmc, ess, sghmc, and vi backends and conditional on the
fixed-effect linear predictor.
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))
# waic() warns when an observation's p_waic exceeds 0.4; loo() is then
# the estimate to report
waic(fit)
#> Warning:
#> 7 (7.0%) p_waic estimates greater than 0.4. We recommend trying loo instead.
#>
#> Computed from 1000 by 100 log-likelihood matrix.
#>
#> Estimate SE
#> elpd_waic -439.0 14.6
#> p_waic 13.0 1.9
#> waic 877.9 29.2
#>
#> 7 (7.0%) p_waic estimates greater than 0.4. We recommend trying loo instead.
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.
# }