If I understand correctly, one should be able to use the kfold-helpers in the Loo 2 package to do a grouped k-fold cv. However, I do not understand exactly how to do it.

As a toy example, say that I have an experiment with some participants and I am interested in whether them being correct influences their response time.

I define the two models:

```
# Define null model
rtm0 <- stan_glmer(rt~+(1|id),
data=data, family=gaussian)
# Define model with one predictor
rtm1 <- update(rtm0,.~.+acc)
```

The classic way of comparing them would then be

```
### Compare with classic loo
loo_rtm0 <- loo(rtm0)
loo_rtm1 <- loo(rtm1)
compare(loo_rtm0,loo_rtm1)
```

But if I understand correctly, this answers how the participants should respond if they are getting one additional trial. The more interesting question is how a new participant will do.

To test this, I hope to use the kfold_split_stratified function something like this:

```
### Compare with grouped k-fold cv
kfold_split_stratified(K = nrparticipants, x = data$id)
```

But I am not sure what to do afterwards? Having gone through the vignettes and some googling has thus far not helped.

Any help would be appreciated. The specific code can be found here and the dataset, E1_gbr.csv can be found here.