I’m using rstanarm version 2.18.2 to do inference on a hierarchical binomial model. I have some non-default priors (using info from previous experiments), so I run it as:

```
fit = stan_glmer(
data = a
, family = binomial(link='logit')
, formula = obs ~ condition + (1+condition|subj)
, chains = 5
, cores = 5
, iter = 1e3
, refresh = 0
, prior_intercept = normal(qlogis(mean(a$obs)),1, autoscale = FALSE) #centering intercept prior on the observed mean with a reasonably narrow scale
, prior = normal(0,1, autoscale = FALSE) #pretty sure the effects are small
, prior_covariance = decov(4) #we're pretty confident there should be identity correlation matrix
, prior_aux = normal(0,1, autoscale = FALSE) #pretty sure there's fairly low variability
)
```

I thought that setting `prior_aux = normal(0,1,autoscale=FALSE)`

would give me half-normal priors on the Sigma parameters (reflecting the variability amongst latent intercept and condition parameter associated with each subject), but when I look at `posterior_vs_prior(fit)`

the output seems to show very broad priors on two of the Sigma terms:

If I’m correct in interpreting this to mean my intended prior is not being used, what am I doing wrong?