[R] ML optimization question--unidimensional unfolding scaling

From: Peter Muhlberger <pmuhl830_at_gmail.com>
Date: Tue 04 Oct 2005 - 04:33:20 EST

I'm trying to put together an R routine to conduct unidimensional unfolding scaling analysis using maximum likelihood. My problem is that ML optimization will get stuck at latent scale points that are far from optimal. The point optimizes on one of the observed variables but not others and for ML to move away from this 'local optimum', it has to move in a direction in which the likelihood is decreasing, which it won't.

It's not hard to know where to look for a more optimal value--it'll be just on the other side of the mean of a curve. So, I can find better values, but these values need to be fed back into ML for continued optimization. Problem is, optim or nlm don't allow me to feed them new values for parameters and in any event ML will likely choke w/ parameters jumping around.

One solution I've thought of is to restart optim or nlm w/ the new values whenever a point jumps. Is there any good way to get optim or nlm to prematurely terminate, return control to the calling program, while retaining a copy of the estimates?

Perhaps ML isn't the best approach for this kind of problem. Suggestions welcome!


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