New user here!

In **brms**, we can impose spatial simultaneous autoregressive (SAR) structures on residuals using `cor_errorsar`

with a spatial weighting matrix `W`

, as follows:

`brm(y ~ x, autocor = cor_errorsar(W))`

Suppose that my data were grouped into, say, 10 groups, and my spatial weighting matrix was actually a 10x10 matrix specifying the spatial relationships between groups.

If I wanted to deal with grouping using random intercepts, could I continue to use `cor_errorsar`

to deal with spatial relationships between groups? Perhaps by expanding my 10x10 matrix into a larger one, such that all individuals within the same group have identical spatial weights?

`brm(y ~ x + (1 | group), autocor = cor_errorsar(W))`

Or would I have to resort to another method, such as `cov_ranef`

? Though I get the impression that this argument takes covariance matrices, not spatial weighting matrices.

Many thanks in advance!