Context: I have data from a respose surface experiment. I've modeled the responses and obtained the desirability function (e.g. I used the profiler to "save desirabilities" got a new column in my table and can double click the column header and see the function). I know about as much as one can about how that function comes about up to what I think is proprietary --- that it is Exp(w_1Log(d_1) + w_2Log(d_2) + ... + w_k Log(d_k)) where each d_i is a specific desirability function for a specific response, e.g. a smooth piecewise function that is 2 exponentials and a cubic for minimize/maximize and a couple of scaled normal densities for targets.
Question: I want to evaluate the Desirability on a set of parameters not run in the experiment --- just like JMP already does for finding optimal parameters, e.g. it uses algorithms to move across the the parameter space, uses the models to predict response values for parameter combinations and then computes the desirability of that combination. I want to specify parameters and have it compute the predicted responses and desirability for a particular set of models I've fit. Can I do this? If yes how? One option would be to do it myself since I have the model parameters I can write those models down, get predicted values and then plug those into the desirability function, but then I would need to have the specific smooth piecewise functions and that seems hard given that the details are proprietary (or seem to be and if I was JMP I would make them that way). Any ideas?
