[R] ML optimization question--unidimensional unfolding scalin g

[Liaw, Andy]

Alternatively, just type debug(optim) before using it, then step through it
by hitting enter repeatedly...

When you're done, do undebug(optim).

Andy

> From: Spencer Graves
> 
> 	  Have you looked at the code for "optim"?  If you 
> execute "optim", it 
> will list the code.  You can copy that into a script file and walk 
> through it line by line to figure out what it does.  By doing 
> this, you 
> should be able to find a place in the iteration where you can 
> test both 
> branches of each bifurcation and pick one -- or keep a list 
> of however 
> many you want and follow them all more or less 
> simultaneously, pruning 
> the ones that seem too implausible.  Then you can alternate between a 
> piece of the "optim" code, bifurcating and pruning, adjusting 
> each and 
> printing intermediate progress reports to help you understand 
> what it's 
> doing and how you might want to modify it.
> 
> 	  With a bit more effort, you can get the official 
> source code with 
> comments.  To do that, I think you go to "www.r-project.org" 
> -> CRAN -> 
> (select a local mirror) -> "Software:  R sources".  From there, just 
> download "The latest release:  R-2.2.0.tar.gz".
> 
> 	  For more detailed help, I suggest you try to think of 
> the simplest 
> possible toy problem that still contains one of the issues 
> you find most 
> difficult.  Then send that to this list.  If readers can copy a few 
> lines of R code from your email into R and try a couple of things in 
> less than a minute, I think you might get more useful replies quicker.
> 
> 	  Best Wishes,
> 	  Spencer Graves
> 
> Peter Muhlberger wrote:
> 
> > Hi Spencer:  Thanks for your interest!  Also, the posting 
> guide was helpful.
> > 
> > I think my problem might be solved if I could find a way to 
> terminate nlm or
> > optim runs from within the user-given minimization function 
> they call.
> > Optimization is unconstrained.
> > 
> > I'm essentially using normal like curves that translate 
> observed values on a
> > set of variables (one curve per variable) into latent 
> unfolded values.  The
> > observed values are on the Y-axis & the latent (hence 
> parameters to be
> > estimated) are on the X-axis.  The problem is that there 
> are two points into
> > which an observed value can map on a curve--one on either 
> side of the curve
> > mean.  Only one of these values actually will be optimal 
> for all observed
> > variables, but it's easy to show that most estimation 
> methods will get stuck
> > on the non-optimal value if they find that one first.  
> Moving away from that
> > point, the likelihood gets a whole lot worse before the 
> routine will 'see'
> > the optimal point on the other side of the normal curve.
> > 
> > SANN might work, but I kind of wonder how useful it'd be in 
> estimating
> > hundreds of parameters--thanks to that latent scale.
> > 
> > My (possibly harebrained) thought for how to estimate this 
> unfolding using
> > some gradient-based method would be to run through some 
> iterations and then
> > check to see whether a better solution exists on the 'other 
> side' of the
> > normal curves.  If it does, replace those parameters with 
> the better ones.
> > Because this causes the likelihood to jump, I'd probably 
> have to start the
> > estimation process over again (maybe).  But, I see no way 
> from within the
> > minimization function called by NLM or optim to tell NLM or optim to
> > terminate its current run.  I could make the algorithm 
> recursive, but that
> > eats up resources & will probably have to be terminated w/ an error.
> > 
> > Peter
> > 
> > 
> > On 10/11/05 11:11 PM, "Spencer Graves" 
> <spencer.graves at pdf.com> wrote:
> > 
> > 
> >> 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
> > 
> > 
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html
> 
> -- 
> Spencer 
> Graves, PhD
> Senior Development Engineer
> PDF Solutions, Inc.
> 333 West San Carlos Street Suite 700
> San Jose, CA 95110, USA
> 
> spencer.graves at pdf.com
> www.pdf.com <http://www.pdf.com>
> Tel:  408-938-4420
> Fax: 408-280-7915
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
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> PLEASE do read the posting guide! 
> http://www.R-project.org/posting-guide.html
> 
>