Hi,

I am trying to fit a longitudinal mediation model using brms. Below I attached a reproducible code

n<-50

J<-5

alpha_m<-4

alpha_y<-3

cp<-2

a<-1.75

b<-1.75

b1<-rnorm(n,0,1.5)

b2<-rnorm(n,0,1)

g1<-rnorm(n,0,1.5)

g2<-rnorm(n,0,1)

g3<-rnorm(n,0,1)

X<-matrix(0,n,J)

M<-matrix(0,n,J)

Y<-matrix(0,n,J)

for(i in 1:n)

{

X[i,]<-rnorm(J,0,1)

M[i,]<-alpha_m+b1[i]+(a+b2[i])*X[i,]+rnorm(J,0,1)

Y[i,]<-alpha_y+g1[i]+(cp+g2[i])*X[i,]+(b+g3[i])*M[i,]+rnorm(J,0,1)

}

X.vec1<-X[1,]

M.vec1<-M[1,]

Y.vec1<-Y[1,]

for(i in 2:n)

{

X.vec1<-c(X.vec1,X[i,])

M.vec1<-c(M.vec1,M[i,])

Y.vec1<-c(Y.vec1,Y[i,])

}

data_med<-data.frame(X.vec1,M.vec1,Y.vec1)

data_med$id<-rep(1:n,each=J)

y_mod <- bf(Y.vec1 ~ X.vec1 + M.vec1+(X.vec1+M.vec1|ID|id))

m_mod <- bf(M.vec1 ~ X.vec1+(X.vec1|ID|id))

```
k_fit_brms<-brm(y_mod+m_mod+set_rescor(FALSE),
data=data_med,
cores=2,chains=2)
```

Question 1.

If I want to get the posterior sample for the parameter Mvec1_X.vec1, I can call the following line of code

```
a <- posterior_samples(k_fit_brms, pars = "Mvec1_X.vec1")
```

How can I get the posterior sample for the variance-co-variance parameters, for example, for βcor(Yvec1_M.vec1,Mvec1_X.vec1)β

Question 2.

If I have more than one mediator coming from a multivariate normal distribution, how can I jointly fit them allowing for their residual error to be correlated?

- Operating System: x86_64-pc-linux-gnu (64-bit)
- brms Version: version 2.3.1