Inspects the fitted model for common convergence problems and returns a
character vector of human-readable warnings. When no warnings apply, the
string "No warnings detected." is returned.
Examples
# \donttest{
library(hbsaems)
library(brms)
data("data_fhnorm")
model <- hbm(brms::bf(y ~ x1), data = data_fhnorm,
re = ~ (1 | regency), # area-level random effect
chains = 4, iter = 2000, warmup = 1000,
cores = 1, seed = 1, refresh = 0)
#> Compiling Stan program...
#> Start sampling
#> Warning: There were 32 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: There were 3 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.44, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
hbm_warnings(model)
#> [1] "R-hat > 1.1: model may not have converged."
#> [2] "neff_ratio < 0.1: low effective sample size."
#> [3] "Divergent NUTS transitions detected (32 divergences)."
# }