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 “inner” 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 “problem” 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