Hi all,

I’ve been playing around with fitting models in `rstanarm::stan_gamm4()`

.The models fit fine but when I use `loo`

I receive warning messages suggesting I use kfold with K=10 (see MWE below). However, when I use kfold it produces an error that indicates it cannot find the random effect variable (in this case fac). Am I missing something obvious here?

Package versions:

rstanarm (2.19.3)

rstantools (2.0.0)

loo (2.2.0)

MWE:

```
library(rstanarm)
library(mgcv)
#Simulate a model
set.seed(200)
dat <- gamSim(6, n=200, scale=2)
# Fit the stan model
#options(mc.cores=1) # Note this doesn't work in Rstudio 1.2.5042 with R 4.0.0
fit <- rstanarm::stan_gamm4(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, random = ~(1|fac))
# Loo
loo_fit <- loo(fit)
#Warning message:
#Found 200 observations with a pareto_k > 0.7. With this many problematic observations we #recommend calling 'kfold' with argument 'K=10' to perform 10-fold cross-validation rather than LOO.
#Kfold
kfold(fit, K=10)
#Fitting model 1 out of 10
#Error in eval(predvars, data, env) : object 'fac' not found
```

Note that the above also happens if I supply a folds to the kfold function using one of the convenience functions like `loo::kfold_split_grouped()`