Hi all,

I’m trying to investigate if spatially correlated models are better in fitting the data compared to non correlated models.

If I got the correlated model formula correct I would do something like

```
corr_model <- bf(mvbind(Y1,Y2,Y3,Y4)~ X1 + X2 + X3) + set_rescore(TRUE)
```

and then fit the model through the brms interface.

Now, I need to compare these models to the models:

```
m1 <- bf(Y1~ X1 + X2 + X3)
m2 <- bf(Y2~ X1 + X2 + X3)
m3 <- bf(Y3~ X1 + X2 + X3)
m4 <- bf(Y4~ X1 + X2 + X3)
```

Is there a way to do for example bayes_factor between the corr_model and all the other models? or is there a better way that i could use to formulate the problem?

Thanks in advance.