Suppose I have a following formula for a mixed effects model:
Performance ~ 1 + WorkingHours + Tenure + (1 + WorkingHours + Tenure || JobClass)
then I can specify priors for fixed slopes and fixed intercept as:
prior = normal(c(mu1,mu2), c(sd1,sd2), autoscale = FALSE)
prior_intercept = normal(mean, scale, autoscale = FALSE)
But how do I specify the priors for random slopes and intercept using
prior_covariance = decov(regularization, concentration, shape, scale)
(or)
lkj(regularization, scale, df)
if I know the variance …