Re: [R] R-square in robust regression

From: Laura Poggio <laura.poggio_at_gmail.com>
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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