Dear all,

First of all, I am not sure if it is (mathematical) possible and useful to fit a Dirichlet Regression with an AR-1 term. However, my data might come from such a data generating procedure. I try to fit a Dirichlet regression with an AR-1 process:

d_bf = bf(

bind(y_d1, y_d2, y_d3) ~ 0,

muyd2 ~ x + (1|id) + (1|year),

muyd3 ~ x + cat + (1|id) + (1|year),

autocor = cor_ar(form = ~ year | id, 1, cov=T)

)

And that is how I call it:

brm(d_bf, AR_df,

family=dirichlet(),

prior = priors,

chains=4)

brms allows this kind of specification and it also adds an AR1 parameter to the summary output. But: I dont think that it takes the AR term into account in its â€śinnerâ€ť estimation. The summary looks like the unchanged prior distribution:

`Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS`

ar[1] -0.01 0.59 -0.96 0.96 1.00 7684 2017

If I estimate the model without the AR term, then I get the exact estimates. The same â€śproblemâ€ť occurs when fitting categorical regressions.

Thank you

Please also provide the following information in addition to your question:

- Operating System: Win 10
- brms Version: 2.10.3