I use conditional_effects to plot my ordinal model, and would like to split the curves into different facets, but I can’t figure out how.

The plot I get is:

But here is a minimal reproducible example

```
library(tidyverse)
library(brms)
data <- data.frame(subject = rep(rep(c(1:10),5), 10),
judge = sort(rep(c(1:5), 100)),
distance = runif(500),
response = round(runif(500,1,6)))
fit <- brm(data = data,
response ~ distance + (1|subject) + (1|judge),
family = cumulative("probit"),
chains = 1,
save_all_pars = TRUE)
conditional_effects(fit, categorical = TRUE)
```

Adding facet_args does nothing, which makes sense because the docs say that this will work over different conditions, and what I would like to do is split the facets using different levels of the response, not of the predictors.

I also tried

```
plot(conditional_effects(fit, categorical = TRUE))[[1]] + ggplot2::facet_grid(~response)
```

But that didn’t change anything in the plot.

I found useful pointers to split the response here https://bookdown.org/content/3686/ordinal-predicted-variable.html# (there are no subsections to link to directly). After fitting a model with no random effects (“fit23.9”), there are some helpful lines of data wrangling code that generate `fitted`

lines for each level of the response and separates them in facets, but that does not look as good when I include my two random effects. I would be happy with the predictions as shown in `conditional_effects`

, (not through the `fitted`

lines) but split into facets.

I’m using brms 2.13.3 on MacOS Catalina

Thanks!