R help - Oct 2005 - ML optimization question--unidimensional unfolding scaling

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

There may be a few problems where ML (or more generally Bayes) fails 
to give sensible answers, but they are relatively rare.

	  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).

	  spencer graves

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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http://www.R-project.org/posting-guide.html
-- 
Spencer Graves, PhD
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