[R] pros and cons of "robust regression"? (i.e. rlm vs lm)

Thu Apr 6 21:47:04 CEST 2006

There are several kinds of standardization, and 'normalization' is
only one of them. For some details you could check
http://support.sas.com/91doc/getDoc/statug.hlp/stdize_index.htm
(see Details for standardization methods).

Standardization is required prior to clustering to control for the
impact of scale. (Variables with large variances tend to have more
effect on the resulting clusters than those with small variances.) I
don't know how valuable standardization may be in other areas.

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of roger bos
> Sent: Thursday, April 06, 2006 1:15 PM
> To: Berton Gunter; Liaw, Andy
> Cc: rhelp
> Subject: Re: [R] pros and cons of "robust regression"? (i.e.
> rlm vs lm)
>
> I'm asking this question purely for my own benefit, not to
> try to correct
> anyone.  The procedure you refer to as "normalization" I have
> always heard
> referred to as "standardization".  Is the former the proper
> term?  Also, you
> say its not necessary given today's hardware, but isn't it
> beneficial to get
> all the variables in a similar range?  Is thre any other
> transformation that
> you would suggest?  I use rlm (and "normalization") in my
> models I use every
> day, so I was happy to read the above comments.
>
> Thanks,
>
> Roger
>
>
>
> On 4/6/06, Berton Gunter <gunter.berton at gene.com> wrote:
> >
> > Thanks, Andy. Well said. Excellent points. The final
> weights from rlm
> > serve
> > this diagnostic purpose, of course.
> >
> > -- Bert
> >
> >
> > > -----Original Message-----
> > > From: Liaw, Andy [mailto:andy_liaw at merck.com]
> > > Sent: Thursday, April 06, 2006 9:56 AM
> > > To: 'Berton Gunter'; 'r user'; 'rhelp'
> > > Subject: RE: [R] pros and cons of "robust regression"? (i.e.
> > > rlm vs lm)
> > >
> > > To add to Bert's comments:
> > >
> > > -  "Normalizing" data (e.g., subtracting mean and dividing by
> > > SD) can help
> > > numerical stability of the computation, but that's mostly
> > > unnecessary with
> > > modern hardware.  As Bert said, that has nothing to do with
> > > robustness.
> > >
> > > -  Instead of _replacing_ lm() with rlm() or other robust
> > > procedure, I'd do
> > > both of them.  Some scientists view robust procedures that
> > > omit some data
> > > points (e.g., by assigning basically 0 weight to them) in
> > > automatic fashion
> > > and just trust the result as bad science, and I think they
> > > have a point.
> > > Use of robust procedure does not free one from examining the
> > > data carefully
> > > and looking at diagnostics.  Careful treatment of outliers is
> > > esspecially
> > > important, I think, for data coming from a confirmatory
> > > experiment.  If the
> > > conclusion you draw depends on downweighting or omitting
> certain data
> > > points, you ought to have very good reason for doing so.  I
> > > think it can not
> > > be over-emphasized how important it is not to take outlier
> > > deletion lightly.
> > > I've seen many cases that what seems like outlier originally
> > > turned out to
> > > be legitimate data, and omission of them just lead to overly
> > > optimistic
> > > assessment of variability.
> > >
> > > Andy
> > >
> > > From: Berton Gunter
> > > >
> > > > There is a **Huge** literature on robust regression,
> > > > including many books that you can search on at e.g. Amazon. I
> > > > think it fair to say that we have known since at least the
> > > > 1970's that practically any robust downweighting procedure
> > > > (see, e.g "M-estimation") is preferable (more efficient,
> > > > better continuity properties, better estimates) to trimming
> > > > "outliers" defined by arbitrary threshholds. An excellent but
> > > > now probably dated introductory discussion can be found in
> > > > "UNDERSTANDING ROBUST AND EXPLORATORY DATA ANALYSIS" edited
> > > > by Hoaglin, Tukey, Mosteller, et. al.
> > > >
> > > > The rub in all this is that nice small sample inference
> > > > results go our the window, though bootstrapping can help with
> > > > this. Nevertheless, for a variety of reasons, my
> > > > recommendation is simply to **never** use lm and **always**
> > > > use rlm (with maybe a few minor caveats). Many would disagree
> > > > with this, however.
> > > >
> > > > I don't think "normalizing" data as it's conventionally used
> > > > has anything to do with robust regression, btw.
> > > >
> > > > -- Bert Gunter
> > > > Genentech Non-Clinical Statistics
> > > > South San Francisco, CA
> > > >
> > > > "The business of the statistician is to catalyze the
> > > > scientific learning process."  - George E. P. Box
> > > >
> > > >
> > > >
> > > > > -----Original Message-----
> > > > > From: r-help-bounces at stat.math.ethz.ch
> > > > > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of r user
> > > > > Sent: Thursday, April 06, 2006 8:51 AM
> > > > > To: rhelp
> > > > > Subject: [R] pros and cons of "robust regression"? (i.e.
> > > rlm vs lm)
> > > > >
> > > > > Can anyone comment or point me to a discussion of the
> > > > > pros and cons of robust regressions, vs. a more
> > > > > "manual" approach to trimming outliers and/or
> > > > > "normalizing" data used in regression analysis?
> > > > >
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