Group-level and population level monotonic predictor

Interfaces brms
1

I am trying to estimate a psychometric function by condition, where some conditions are within and some between participants.

Y is a binomial outcome
X1 is a binomial within participant predictor
X2 is a binomial between participants predictor
X3 is an ordinal within participant predictor

My model formula is

Y ~ 0 + X1 : X2 + X1:X2:mo(X3) + (0 + X1 + X1:mo(X3) | SubjectID)

Brms gives me a very understandable:

Error: Special group-level terms require corresponding population-level terms

However, this was not an issue when X3 was modeled as a continuous variable, and I am not sure how to overcome the issue, given that X2 does not belong to the varying effects.

Is the only solution to change the formula to

Y ~ 1 + X1 * X2 * mo(X3) + (1 + X1 * mo(X3) | SubjectID)

which is more inconvenient for my purpose?

2

Why can’t you include all the varying terms in the population terms with

Y ~ 0 + X1 + X1:X2 + X1:X2:mo(X3) + X1:mo(X3) + (0 + X1 + X1:mo(X3) | SubjectID)

Then you’d have all the varying effects in the population-level terms as well. It might also make sense to include X3 (or mo(X3)) along with its interactions, but that depends on the data and questions.

3

Thanks. That runs. It’d be very convenient for me to have separate intercepts and slopes per each condition (the 4 combinations of x1,x2 values), which motivated the question. But I can always calculate them post-hoc.

4

Just to make sure, when having:

X1:X2:mo(X3) + X1:mo(X3)

in the model I get two values for X1:mo(X3) and 2 values for X1:X2:mo(X3).
Would the two values for X1:mo(X3) correspond to the mean between the two X2 conditions or the parameter estimate for when X2 is 0 (or the baseline value for X2)?