I have a mixed model built using brms that has many variables. It looks something like this:
brm(Y ~ (1 + A + B + C + D | E) + A + B + F + G + H + I + J, data=Data)
I am trying to get a sense of which independent variables cause the dependent variable to have a wide variance in its distribution (ie. a large difference between Q2.5 and Q97.5). For example, are the predicted Y values more uncertain when A is very low, F is very high, etc? Any advice or references on how to go about quantifying this?