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?