Posterior Predictive Checks for Ratio Models

Visual and numerical checks comparing observed data to posterior predictive distributions. Essential for assessing model fit.

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))
if (requireNamespace("ggplot2", quietly = TRUE)) {
  pp_check(fit, type = "dens_overlay")
}

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.
# }