I was wondering how to incorporate measurement error for a predictor in which I am also implementing a piecewise / broken-stick regression scheme. If I just fit a measurement error model for my predictor I could do it easily as
brm(Y ~ 1 + me(X, sd))
But I now want to fit a model with an upper threshold for X at which below this threshold the association between X and Y is zero, and above is nonzero. Since the threshold is known I want to fit it along the lines of
Y ~ 1 + (me(X, sd) - threshold) * I(me(X, sd) - threshold > 0)
I wassw ondering if this is possible in brms or if I should just switch to STAN and code this directly?