Hello,

I have a beta regression where I model both the response and the dispersion, like this:

```
mean.spec <- as.formula(y ~ ...)
disp.spec <- as.formula(~ ...)
fit <- brm(bf(mean.spec, phi=disp.spec), family=Beta(), ...)
```

The model fits fine, but when I try to see what dispersion is taken at each observation:

```
predict(fit, resp="phi")
```

I get back predictions of the response, i.e. the `resp`

argument is ignored.

This is likely something quite trivial, but I am stumped. Thank you very much in advance.

Versions:

- Operating System: Windows 10
- brms: 2.9.0

Hi!

The correct way is to use the ‘fitted’ function, using the argument ‘dpar = phi’.

Have a good day!

Lucas

Do not forget that phi is a precision parameter, non linearly and negatively related to dispersion :)

Thank you for your reply, Lucas. Wouldn’t `fitted`

ignore the random effects and the residual variability though? I care about both in my application.

The argument `re_formula`

controls this and by default will include all group level effects. See `?brms:::fitted.brmsfit`

for details.

Thanks, Lucas and Gavin. `fitted`

indeed works and includes the group effects, which is sufficient in my case, actually. But what should I do if I also wanted to include the residual variability? The documentation directs me to `predict`

.

Residual variability applies, by definition, only to the response variable itself. Distributional parameters have no residual variability.

You’re right. Thanks everyone.