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.
```