# Re: [R] ML optimization question--unidimensional unfolding scaling

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Wed 12 Oct 2005 - 13:11:30 EST

What is your likelihood? How many parameters are you trying to estimate?

Are you using constrained or unconstrained optimization? If constrained, I suggest you remove the constraints by appropriate transformation. When considering alternative transformations, I consider (a) what makes physical sense, and (b) which transformation produces a log likelihood that is more close to being parabolic.

Hou are you calling "optim"? Have you tried all "SANN" as well as "Nelder-Mead", "BFGS", and "CG"? If you are using constrained optimization, I suggest you move the constraints to Inf by appropriate transformation and use the other methods, as I just suggested.

If you would still like more suggestions from this group, please provide more detail -- but as tersely as possible. The posting guide is, I believe, quite useful (www.R-project.org/posting-guide.html).

Peter Muhlberger wrote:

> 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!
>
> Cheers,
> Peter
>
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