From: Laura Poggio <laura.poggio_at_gmail.com>

Date: Thu, 13 Nov 2008 14:58:12 +0000

R-help_at_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. Received on Thu 13 Nov 2008 - 15:00:28 GMT

Date: Thu, 13 Nov 2008 14:58:12 +0000

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_at_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_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@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/>
**> >> <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_at_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_at_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_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.
**>
*

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