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 <[EMAIL PROTECTED]> > >>>>> "LP" == Laura Poggio <[EMAIL PROTECTED]> > >>>>> 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 <[EMAIL PROTECTED]> > > >> > >> 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 <[EMAIL PROTECTED]> > >> > Subject: Re: [R] R-square in robust regression > >> > To: PARKERSO <[EMAIL PROTECTED]> > >> > Cc: r-help@r-project.org > >> > Message-ID: > >> > <[EMAIL PROTECTED]> > >> > 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, [EMAIL PROTECTED] > >> > 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. > [[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.
