[R] R-square in robust regression

[Martin Maechler]

>>>>> "LP" == Laura Poggio <laura.poggio at gmail.com>
>>>>>     on Thu, 13 Nov 2008 10:43:14 +0000 writes:

    LP> yes thank you! it is perfect.
    LP> I was using lmrob in package robustbase and it did not have that option in
    LP> the summary.

Yes....

lmRob() from "robust" is from a company which -- often being excellent --
has at times listened much more to its not-so-professional
customers instead of its expert advisors.

So, yes indeed, summary(lmRob(..)) happily reports
something like
  "Multiple R-Squared: 0.620538"  (number: for the stack loss example)

But the question is if the customer should get  R^2  even in
casses where its definition is very doubtful and indeed
somewhat *counter* to the purpose of using methods that are NOT
least-squares based....

Martin Maechler, ETH Zurich

    LP> 2008/11/13 Mark Difford <mark_difford at yahoo.co.uk>

    >> 
    >> Hi Laura,
    >> 
    >> >> I was searching for a way to compute robust R-square in R in order to
    >> get
    >> >> an
    >> >> information similar to the "Proportion of variation in response(s)
    >> >> explained
    >> >> by model(s)" computed by S-Plus.
    >> 
    >> There are several options. I have had good results using wle.lm() in
    >> package
    >> wle and lmRob() in package robust. The second option is perhaps closest to
    >> what you want.
    >> 
    >> Regards, Mark.
    >> 
    >> 
    >> Laura POggio wrote:
    >> >
    >> > I was searching for a way to compute robust R-square in R in order to get
    >> > an
    >> > information similar to the "Proportion of variation in response(s)
    >> > explained
    >> > by model(s)" computed by S-Plus. This post is dealing with that. Would be
    >> > possible to have some hints on how to calculate this parameter within R?
    >> >
    >> > Thank you very much in advance.
    >> >
    >> > Laura Poggio
    >> >
    >> >
    >> >
    >> -----------------------------------------------------------------------------
    >> > Date: Mon, 20 Oct 2008 06:15:49 +0100 (BST)
    >> > From: Prof Brian Ripley <ripley at stats.ox.ac.uk>
    >> > Subject: Re: [R] R-square in robust regression
    >> > To: PARKERSO <sophie.parker at vuw.ac.nz>
    >> > Cc: r-help at r-project.org
    >> > Message-ID:
    >> >        <alpine.LFD.2.00.0810200609590.21177 at gannet.stats.ox.ac.uk>
    >> > Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed
    >> >
    >> > On Sun, 19 Oct 2008, PARKERSO wrote:
    >> >
    >> >>
    >> >> Hi there,
    >> >> I have just started using the MASS package in R to run M-estimator
    >> robust
    >> >> regressions. The final output appears to only give coefficients, degrees
    >> > of
    >> >> freedom and t-stats. Does anyone know why R doesn't compute R or
    >> >> R-squared
    >> >
    >> > These as only valid for least-squares fits -- they will include the
    >> > possible outliers in the measure of fit.
    >> >
    >> > And BTW, it is not 'R', but the uncredited author of the package who made
    >> > such design decisions.
    >> >
    >> >> and why doesn't give you any other indices of goodness of fit?
    >> >
    >> > Which ones did you have in mind?  It does give a scale estimate of the
    >> > residuals, and this determines the predition accuracy.
    >> >
    >> >> Does anyone know how to compute these in R?
    >> >
    >> > Yes.
    >> >
    >> >> Sophie
    >> >
    >> >
    >> > --
    >> > Brian D. Ripley,                  ripley at stats.ox.ac.uk
    >> > Professor of Applied Statistics,
    >> > http://www.stats.ox.ac.uk/~ripley/<http://www.stats.ox.ac.uk/%7Eripley/>
    >> <http://www.stats.ox.ac.uk/%7Eripley/>
    >> > University of Oxford,             Tel:  +44 1865 272861 (self)
    >> > 1 South Parks Road,                     +44 1865 272866 (PA)
    >> > Oxford OX1 3TG, UK                Fax:  +44 1865 272595
    >> >
    >> >       [[alternative HTML version deleted]]
    >> >
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    >> >
    >> >
    >> 
    >> --
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    >> 
    >> ______________________________________________
    >> R-help at r-project.org mailing list
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    LP> [[alternative HTML version deleted]]

    LP> ______________________________________________
    LP> R-help at r-project.org mailing list
    LP> https://stat.ethz.ch/mailman/listinfo/r-help
    LP> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
    LP> and provide commented, minimal, self-contained, reproducible code.