Similar issue to the one here: Using model comparison (loo or waic) after imputation, but I can’t find any discussion that is more recent than 2021, so I’d like to revive the topic.

Using `brms`

. I have a dataset with about 9000 observations where the outcome variable is a latent variable, so I’m using a calibration dataset to fit a linear model for an indicator variable, then `posterior_predict`

to generate list of datasets with imputed values, and `brm_multiple`

to fit models on those imputed datasets. Similar to what is described here: Handle Missing Values with brms

I’m hoping to compare a number of models using k-fold cross validation, as this seems like the best method for a large dataset like this. When I do this (or other post-fitting diagnostics like `loo`

, `waic`

, or `bayes_R2`

, I get the message

```
Warning: Using only the first imputed data set. Please interpret the results with caution until a more principled approach has been implemented.
```

So my question - is there any more principled approach? How would you compare model structures in a dataset like this one?