Hi all,

I am wondering if there is a way to ask brms, in a multivariate model context, to calculate the correlation between the random intercept in model 1 and residuals in model 2?

For instance we have two univariate models such as:

```
bf_Trait1 <- bf(Trait1 ~ covariate + (1 | Individual)) + gaussian()
bf_Trait2 <- bf(Trait2 ~ covariate) + gaussian()
```

`bf_Trait1`

has repeated measured per Individual and `bf_Trait2`

has only one.

I want to estimate the correlation between `sd(Intercept)`

of the group level `Individual`

in `bf_Trait1 `

and the `Sigma`

in `bf_Trait2 `

.

An alternative way to do this is to force the residual variance in `bf_Trait2`

to 0 and add a random intercept to `bf_Trait2`

such as:

```
bf_Trait1 <- bf(Trait1 ~ covariate + (1 |p| Individual)) + gaussian()
bf_Trait2 <- bf(Trait2 ~ covariate + (1 |p| Individual)) + gaussian()
```

and then just get the correlation between random intercepts of both models. Unfortunately, I did find how to force brms to set residual variance to 0. Apparently, it is planning to add a new function constant() to fix a prior to a constant value, but it doesn’t seem to be added to brms yet.

Thanks!

- Operating System: Windows 10
- brms Version: 2.10.0