Piece-wise PD model

NMusers, In a somewhat similar theme to Hauke's post, I am having an issue with conditional assignment statements in $ERROR. I am trying to fit a piece-wise PD model for a KPD system. A linear PD model is ok, but saturating models, while I expect these to provide a better fit, converge with parameters that essentially replicate a linear model (tried Emax, Power, Exponential). My last ditch attempt was to try a piece-wise model, with 2 linear slopes, estimating the change point. Something like this $PK ... CHANGE=THETA(.) SLOPE1=THETA(.) SLOPE2=THETA(.) ... $ERROR A(.)=AEFF ;amount in effect compartment IF(AEFF.LT.CHANGE) THEN SLOPE=SLOPE1 ELSE SLOPE=SLOPE2 ENDIF EFFECT=AEFF*SLOPE I always find minimization is terminated early, and the gradient for CHANGE is zero at first iteration. The gradient does have a value at subsequent iterations, but the final estimate of CHANGE (at termination anyway) is usually not far off the initial estimate...I suspect it is not actually being estimated, just floating a bit. Even if I fix CHANGE to a reasonable value, I see minimization is terminated early. Is there some trick I am missing here? or is it not possible to estimate a parameter within a conditional assignment statement in $ERROR? (seems like you can do this in $PK when a covariate or time is used in the IF statement) Thanks for any help, Brendan Johnson GlaxoSmithKline, RTP