Hi all! I am wondering how to get cluster-specific autocorrelation terms in brms?
I am interested in conducting time series analyses of data where multiple individuals were measured over time, but don’t know how to specify varying autocorrelation parameters (fixed or random [I know…]) for the individuals. Currently, I find it easy to do an AR(1) model for a single individual:
brm(y ~ 1 + ar(), ...)
But my data consists of multiple individuals (id), and I want them to have their own intercepts and autocorrelation terms. I can specify
brm(y ~ 1 + (1 | id) + ar(gr = id), ...)
but that assumes that the autocorrelation term is identical for all individuals. I can also manually construct a lagged response variable y_lag, and specify
brm(y ~ 1 + y_lag + (1 + y_lag | id), ...)
but unfortunately this would lead to missing data in y_lag.
Presumably I could do something about the missingness in y_lag with mi(), but that doesn’t seem like the best solution either. Alternatively, I could dive into the Stan code of the second model above, and make the ar term a varying effect manually, but it has now been about 3 years since I wrote any Stan and I might have forgotten anything beyond “Hello, one beer please.”
I’d appreciate any thoughts or comments!
PS. Just to see what would happen, I also tried
brm(y ~ 1 + (1 + ar(gr = id) | id) + ar(gr = id), ...)
but this just returned a varying intercepts model, ignoring the ar() call inside the varying effects specification. It might be useful to just fail with an error in this case.