I am fitting a Bayesian Glmm model, using this R code: model1 ← stan_glmer(formula =Multimorbidity ~ age_category + Occupation + Education + DrinkingWater + WashingHand +
MaritalStatus + SleepingHour + RedMeatEat + Habits_New +
SoftDrinks + FastFood + Vegetables_New + Fruits + FinancialSupport +
TakingCare + MaritalStatus + Childhood_trauma +
Calcium_vitD + eggs_catf + Salt + Sugar + PassiveSmoking + MainMeal + Exercise + Pressure+BMI_cat+ (1 | hhid ),
prior = normal(0,5),
prior_intercept = cauchy(0,1),
prior_aux = student_t(3,0,5),
prior_covariance = decov(),
family = binomial(),
data = ncdan8,
na.action = getOption(“na.action”, “na.omit”),
iter = 10000,
chains = 2
)
summary(model1)
But I am not sure which one specifying prior for random effect. I have a set of combination for the priors for intercept, fixed effect and random effect. How to specify prior for random effect? also when I change prior_covriance to other than decov to exactly specify a prior I am getting this error: “Error in rstan::summary(stanfit, probs = probs, digits = 10)summary :
operator is invalid for atomic vectors”