I’m getting a suggestion to “run more iterations to get at least about 2200 posterior draws to improve LOO-CV approximation accuracy” … but I don’t know how to do that. Sorry!
Is it asking for something simple that just takes more time, like passing some ndraws
parameter, or increasing the value of iter
when fitting the model? I’m using brms::brm(..., backend = "cmdstanr")
if that makes a difference.
I did find a relevant GitHub issue by @paul.buerkner and @avehtari but am still searching for practical hints/instructions.
Thanks in advance for your time and attention!
> loo(amount_wb_model)
Computed from 2000 by 3993 log-likelihood matrix.
Estimate SE
elpd_loo -8887.6 139.2
p_loo 1551.2 31.2
looic 17775.1 278.3
------
MCSE of elpd_loo is NA.
MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 2.2]).
Pareto k diagnostic values:
Count Pct. Min. ESS
(-Inf, 0.7] (good) 2830 70.9% 65
(0.7, 1] (bad) 910 22.8% <NA>
(1, Inf) (very bad) 253 6.3% <NA>
See help('pareto-k-diagnostic') for details.
Warning message:
Found 1163 observations with a pareto_k > 0.7 in model 'amount_wb_model'. We recommend to run more iterations to get at least about 2200 posterior draws to improve LOO-CV approximation accuracy.