Model3

Book<- read_excel("X:/StatisticsPlatform/Website/ELearning/Workshops/Bayesian Analysis/Data/Book1.xlsx")
d<-Book%>%dplyr::select(ADG,ADF,Country,Days,BW_start,n_start,Challenge,Studycode,Included2017)
d<-d[complete.cases(d), ]
d$Studycode<-as.factor(d$Studycode)
d$Challenge<-as.factor(d$Challenge)
d$Country<-as.factor(d$Country)

model3<- stan_glmer(
  ADG~ADF + 
    Challenge + 
    Included2017 + 
    BW_start+Country+
    n_start+
    (1|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_2 <- loo(model2)
loo_3 <- loo(model3)
loo_compare(loo_2, loo_3)

complete_summary <- tidy(model3, 
                         effects = c("fixed", "ran_vals", "aux"), 
                         conf.int = TRUE, conf.level = 0.80)