I’ve adapted the vignette on distributional models (https://cran.r-project.org/web/packages/brms/vignettes/brms_distreg.html) for my data:

- Participants split into two groups (~40 per group)
- Each participant provides measurements over ~ 50 days

```
fit= brm(bf(symptom ~ day*group+(day|ID), sigma~group+(1|ID)),data=df)```
```

I find in my results that sigma_group is different between the groups. I was wondering what is the best to illustrate this? I think the brms call conditional_effects does not show the right thing as it just shows effects on the mean (day, group, day*group), but not on the sigma.

I guess I could manually do a bar plot where first I compute for each person the standard deviation of their measurements and then compute the mean (and standard error) for each group to make the two bars?

Many thanks

Jacquie