Prints HMC diagnostics and lists parameters with smallest effective sample
sizes and largest Rhat values. See hmc_diagnostics()
and
posterior::default_convergence_measures()
for details.
See also
Model diagnostics
hmc_diagnostics()
,
lfo()
,
loo.dynamitefit()
Examples
data.table::setDTthreads(1) # For CRAN
mcmc_diagnostics(gaussian_example_fit)
#> NUTS sampler diagnostics:
#>
#> No divergences, saturated max treedepths or low E-BFMIs.
#>
#> Smallest bulk-ESS values:
#>
#> nu_y_alpha_id16 106
#> alpha_y[28] 118
#> delta_y_y_lag1[14] 118
#>
#> Smallest tail-ESS values:
#>
#> alpha_y[7] 109
#> alpha_y[14] 111
#> delta_y_x[13] 114
#>
#> Largest Rhat values:
#>
#> delta_y_x[12] 1.04
#> delta_y_x[8] 1.03
#> nu_y_alpha_id39 1.03