I am very very new to Stan, brms, bayesian modelling.
I have been trying to model kilograms against 2 unscaled continuous variables and 1 random effect (2 categories) using a glmm, and the data looks to fit a gamma distribution. Kilograms cannot be <0.
I set the following prior and ran the following model using brms in RStudio.
prGam = prior=c(prior(normal(0,2),class="Intercept"), prior(normal(0,2),class="b"), prior(gamma(0.5, 0.01),class="shape")) brm( kilograms ~ b + c + (1 | d), data = dataFrame, chains = 4, iter = 1500, warmup = 500, control = list(adapt_delta = 0.90, max_treedepth = 20), cores = 1, seed = 12, inits = 0, prior = prGam, family = Gamma(link="log"), )
The pp_check of the model looks the following way:
It looks like the model is doing an okay job, but as you can see there is quite a long tail in the yreps, which seems to exceed the data by 4x the highest value.
I was wondering if there is anything I can change in the model to reduce the tail?
Perhaps with the priors or with a different distribution family.
I get a similar pp_check with higher iterations, and when setting the family as lognormal.
Thank you very much for any advice!