Currently I am trying to see if the autoregression is greater in one group compared to another. In the data I have a time series of people (id) rating their positivity (y), we would hypothesise that in a patient group (p) the correlation of positivity at t with t-1 is lower than it is in controls.

I was trying with `cor_arr()`

but couldnt understand how to extract a correlation coefficient for each group (I also noticed that `cor_arr()`

it is to be deprecated).

The dirty hack way I came up with I just modelled the patients and controls separately then got the posterior draws (using `posterior_samples()`

) for `arr[1]`

to compare between groups.

```
fit.arr.true <- brm(y ~ 1|id, autocor = cor_arr(~ t|id), data = filter(dat, p==TRUE))
fit.arr.false <- brm(y ~ 1|id, autocor = cor_arr(~ t|id), data = filter(dat, p==FALSE))
```

Is there a more *ideal* way of doing this ?

Also if we were to do this with lagged variables as predictors (as suggested in documentation) then how would we go about this ?

The way I figured was just to add a variable to the data which is y_{t-1} meaning the model would look something as follows with x = y_{t-1}

```
fit.basic.neg <- brm(y ~ x*p + (x|gr(id, by=p)), data = dat)
```

Any advice appreciated, it seems like something reasonably common.

Thanks heaps.

- Operating System: Win 10
- brms Version: 2.4