@avehtari Looking at the vignette,

```
Computed from 4000 by 262 log-likelihood matrix
Estimate SE
elpd_loo -6236.9 725.4
p_loo 284.9 69.1
looic 12473.8 1450.7
------
Monte Carlo SE of elpd_loo is NA.
Pareto k diagnostic values:
Count Pct. Min. n_eff
(-Inf, 0.5] (good) 240 91.6% 206
(0.5, 0.7] (ok) 7 2.7% 48
(0.7, 1] (bad) 8 3.1% 7
(1, Inf) (very bad) 7 2.7% 1
See help('pareto-k-diagnostic') for details.
```

I didn’t notice the `Min. n_eff`

column until now. The latest guideline is that `n_eff`

should be at least 100 times the number of chains. Does this apply to loo also? What about \widehat R? Also, does loo check these statistics itself or is it recommended to use the usual procedure (rstan’s summary function) to evaluate the `log_lik`

vector?

I understand the logic of looking at observations associated with large k values (outliers or unexpected given the posterior). Is there a useful interpretation of `elpd_loo`

or the SE of this quantity? Or is `elpd_loo`

only useful for model comparison?

There was a recent article about Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods. Do I understand correctly that loo should not be used to compare latent variable models without integrating out the latent variables? Apparently, the `blavaan`

package has some code to integrate out latent variables. Any idea if this code is specific to `blavaan`

models or if it is generic? If it is generic, maybe it can be moved out of `blavaan`

some more generic package like `latentStan`

(I made that up)? What about Pareto k values? Do k values still retain their useful interpretation in the context of latent variable models?