I am attempting to simulate data from a multilevel model with known fixed effects and random effect correlations. Take a simple case such as

bf(y ~ x + (x|subject)) + gaussian()

It is straightforward to “fix” most of the parameters in the model with priors like e.g.

c(prior(normal(0.8,0.001), class=“b”, coef=“x”),

prior(normal(0.6,0.001), class=“sd”, coef=“Intercept”, group=“subject”),

prior(normal(0.2,0.001), class=“sd”, coef=“x”, group=“subject”) )

I can then simulate responses with posterior_predict().

Is it possible to also “fix” the correlation of the (x|subject) random slope and intercept effects in a brms model? It seems the only prior available for the random effect correlation is the lkj(), and it is unclear to me how I could specify e.g., r = 0.3 (with low uncertainty).

Please also provide the following information in addition to your question:

- Operating System: Windows 10
- brms Version: 2.10