Hello everyone,

I’m relatively new to `rstanarm`

, having moved from using`rstan `

. I am trying to use the `stan_glmer()`

function to estimate regression coefficients without pooling information.

After reading the vignettes, I thought I understood, but there’s an edge case I’m unsure about.

I have a continuous outcome Y, as well as indicators X2, and X3. `id`

is the group-identifier.

Here is some example code below:

```
model = rstanarm::stan_glmer(
formula = Y ~ 0 + (1 + X2 + X3|id), # no pooling, each individual has their own coefficients
data = data,
family = "gaussian",
prior_intercept = rstanarm::normal(100, 10),
prior = rstanarm::normal(0, 2.5),
prior_aux = rstanarm::exponential(1, autoscale = TRUE),
prior_covariance = rstanarm::decov(reg = 1, conc = 1, shape = 1, scale = 1)
)
```

The specific priors don’t matter here, but I wanted to ask if anyone knew how the `prior`

and `prior_covariance`

arguments interact in this case. above

I understand that in a typical hierarchical model, the `prior`

argument lets us control the priors for the population-level or fixed coefficients. The `prior_covaraince`

argument lets us specify how the random effects will vary between groups.

In this case where there are no fixed coefficients, will the individual coefficients take their prior from the `prior`

argument or `prior_covariance`

? My feeling is that its the latter, but I wanted to get some insight on how this would be handled.

Thank you in advance!