Re: [R] R-square in robust regression

From: Mark Difford <mark_difford_at_yahoo.co.uk>
Date: Thu, 13 Nov 2008 09:47:29 -0800 (PST)

Hi Laura,

The fact that you had copied Prof. Ripley's response suggested to me that this was so. Nevertheless, I think Martin Maechler was wise to emphasize the problem, just in case.

Bye, Mark.

Laura POggio wrote:

> 
> 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_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@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.
>>
> 
> 	[[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.
> 
> 

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