I am aware of the limits of the parameter R^2 in this case. However often it
is required for many different reasons. And it is helpful to have a function
that does it. The most important is to know the drawback of the"number", I
think.

Laura


2008/11/13 Martin Maechler <maechler@stat.math.ethz.ch>

> >>>>> "LP" == Laura Poggio <laura.poggio@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@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@stats.ox.ac.uk>
>    >> > Subject: Re: [R] R-square in robust regression
>    >> > To: PARKERSO <sophie.parker@vuw.ac.nz>
>    >> > Cc: r-help@r-project.org
>    >> > Message-ID:
>    >> >        <alpine.LFD.2.00.0810200609590.21177@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@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/>
>    >> <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]]
>    >> >
>    >> > ______________________________________________
>    >> > R-help@r-project.org mailing list
>    >> > https://stat.ethz.ch/mailman/listinfo/r-help
>    >> > PLEASE do read the posting guide
>    >> > http://www.R-project.org/posting-guide.html
>    >> > and provide commented, minimal, self-contained, reproducible code.
>    >> >
>    >> >
>    >>
>    >> --
>    >> View this message in context:
>    >>
> http://www.nabble.com/Re%3A-R-square-in-robust-regression-tp20478161p20478307.html
>    >> Sent from the R help mailing list archive at Nabble.com.
>    >>
>    >> ______________________________________________
>    >> R-help@r-project.org mailing list
>    >> https://stat.ethz.ch/mailman/listinfo/r-help
>    >> PLEASE do read the posting guide
>    >> http://www.R-project.org/posting-guide.html
>    >> and provide commented, minimal, self-contained, reproducible code.
>    >>
>
>     LP> [[alternative HTML version deleted]]
>
>    LP> ______________________________________________
>    LP> R-help@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.
>

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