Hi everyone,

and thanks in advance for your help!

I would like to test the correlation between random slopes by running a model comparison.

**Model**

model<-brm(Y ~ Phase + (1+Phase|subject) + (1|item),

data=Con, sample_prior = TRUE, save_all_pars = TRUE, iter=10000)

- Y is a continous variable
- Phase is a categorical variable (factor) with three levels (0, 1, 2)

With the default contrast coding, I get estimates for the following fixed effects:

- Intercept (estimate at Phase = 0)
- Slope 1 (difference between 0 and 1)
- Slope 2 (difference between 0 and 2).

I also get estimates for the following random effects

- Correlation between random intercepts and random slopes 1
- Correlation between random intercepts and random slopes 2
- Correlation between random slopes 1 and random slopes 2

**My question**

I would like to test the hypothesis that the correlation between random slopes 1 and random slopes 2 is different from 0. Iâ€™d like to run a model comparison (e.g., by using bayes_factor), in which I compare a model with all three correlations to a model where the third correlation is set to 0.

Is that possible?

From the brms documentation I learned that it is possible to set parameters to constants in the prior specification. Is that also possible for (parts of the) variance-covariance matrix?

Thank you!

Julia