Thanks, On 1) this is an idea that's been explored, but X in my case is driven by a variety of class and continuous variables, with the weights of them being derived during the regression. It is not possible to know the specific X for an observation prior to the regression as it is binary data, therefore bands for C cannot be predetermined. One idea being considered is to perform a 2 step regression, deriving X without C, then using that output to allocate C-bands for each observation, before re-running the regression with X (components) & C. However this is not ideal as every time C changes, so will X. Potentially repeating the process should converge on a stable solution for X and C eventually, but the hope with modifying the link function was that this could all be done in one step. For 2) as X is not a single variable, but a combination of variables derived in the regression I don't believe is is possible to ask it to derive an overall spline applied on top of individual variables with their weights also being derived at the same time? (similar issue to 1)) 3) Is very interesting, I've not come across the NLIN procedure, the example of probit binomial regression in the documentation suggests it might do the job. I notice the overview recommends other procedures for maximum likelihood estimations, but I assume that's just because they are easier to use for the majority of applications, whereas PROC NLIN is more complex but offers much more flexibility? But I think this holds the most potential for achieving everything I am trying to do in a single step procedure. I will see where I can get with it thank you.
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