At 08:31 AM 7/26/99 +0200, Lorenz Gygax wrote:
>
>
>Dear all,
>
>Here a summary of the responses that I got to the following problem:
>
>****************** original problem ***********************************
>
>I am currently trying to run a couple of robust models in Splus
>Version 5.0 Release 2 for Sun SPARC, SunOS 5.5 with the function
>lmRobMM () with the final aim of comparing these models with the
>function anova(,test="RF").
>
>One of the smaller models that I am trying to run has about 450 cases
>and uses one factor as the explanatory variable (with almost 40
>levels; the larger models have one to six additional continuous
>variables). The calculation of this model is running now for almost 20
>hours of CPU time [it is up to over 100 hours by now].
>
>Does anybody else have experience with the efficiency of this
>function, any idea whether this is normal behaviour and I just need to
>be patient or on how to speed things up?
>
>***********************************************************************
>
>Most answers recommended to move to Splus 5.1 and see whether the
>general increase in efficiency also helps with my problem (Bert Gunter
><bert_gunter@merck.com>, Brian Ripley <ripley@stats.ox.ac.uk>, Sylvia
>Isler <sisler@statsci.com>). I am currently trying to locate our
>shipment of version 5.1 and may soon report on my experience regarding
>lmRobMM.
>
>Doug Martin <doug@statsci.com> provided some additional thoughts and
>hints:
>
>1. With 40 levels, you have in effect p = 40 (dummy) variables. The
>default resampling algorithm is set at 4.6*2^p which is 5.058e+12 for
>p = 40. This default rule provides a high breakdown point (BP = .5)
>with probability .999. You can choose to use fewer samples. But then
>you lose this high probability of high breakdown point. The details
>may be found in Section 3 of Yohai, Stahel and Zamar (1991) - see
>the Bibliography of the On-Line User Manual Supplement for 4.5 (or
>equivalent for UNIX) for the source of this reference. Perhaps we
>can provide the details via email on Monday, and check a bit to
>see how many samples are required for lower probabilities such as
>.9, etc.
>
>2. Another possiblity is to try the genetic algorithm instead of the
>resampling algorithm, experimenting with the algorithm parameters. I do
>not believe there are any high-probability of high-breakdown point
>properties for the genetic algorithm. But some people believe it works
>well (a study we did several years ago with a small number of variables
** More precisely, a study that Pat Burns did. And I may have recalled
it somewhat incorrectly, as on second thought maybe the genetic
algorithm did better - at least for Pat's specific study. Sorry for
that. Maybe Pat can provide an accurate summary if he is reading this.
Else, I could look at the report again.
Doug
>showed that it was very similar to the resampling method). In any event,
>though highly desirable, high-breakdown point is not a be-all and end-all.
>
>3. On UNIX S-PLUS 5.1 is faster than 5.0.
>
>4. More importantly: For models with (some) factor variables there is a
>much better algorithm than the current resampling algorithm, due to
>Maronna and Yohai (submitted, but not yet published). It turns out that
>we already started implementing the algorithm, and hope to have a beta
>version soon. We regard this as a very important improvement to lmRobMM,
>and although it does not solve your problem today, I hope you might want
>to be a beta tester as soon as the new version is available?
>
>5. Finally for regression with many variables, e.g., 50 or more, there
>is another "fast" algorithm described by Pena and Yohai in JASA, that
>we will also implement soon.
>
>
>Thank you all for your generous help and advice! I will let you know
>when and how I succeeded to solve the problem.
>-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>Lorenz Gygax LGygax@amath.unizh.ch; room: 36-L-40
> Department of Applied Mathematics
> University of Zuerich-Irchel
> Winterthurerstr. 190; CH-8057 Zurich
> voice: 41-1-635-58-52 fax: 41-1-635-57-05
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>
>
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