Model4

options(mc.cores=parallel::detectCores())
model4<- stan_glmer(
  ADG~ADF + 
    Challenge + 
    Included2017 + 
    BW_start+Country+
    n_start+
    (ADF|Studycode),
  data = d, family = gaussian,
  prior_intercept = normal(100,3, autoscale = TRUE),
  prior = normal(0, 2.5, autoscale = TRUE), 
  prior_aux = normal(9, 3, autoscale = TRUE),
  prior_covariance = decov(reg = 1, conc = 1, shape = 1, scale = 1),
  chains = 4, iter = 5000*2, seed = 84735)

loo_4 <- loo(model4)
print(loo_4)
loo_compare(loo_2, loo_3, loo_4)
  
mcmc_trace(model4, size = 0.1)

complete_summary <- tidy(model4, 
                         effects = c("fixed", "ran_vals", "aux"), 
                         conf.int = TRUE, conf.level = 0.80)
print(tbl_df(complete_summary), n=41)

mcmc_areas(model4, pars = vars(starts_with("b[(I")), prob = 0.8) + 
  geom_vline(xintercept = 0) + scale_x_continuous(limits = c(-0.5, 0.5))
mcmc_areas(model4, pars = vars(starts_with("b[A")), prob = 0.8) + 
  geom_vline(xintercept = 0)

neff_ratio(model4)
rhat(model4)

pp_check(model4) + labs(x = "ADG", title = "model4")