yes thank you! it is perfect. I was using lmrob in package robustbase and it did not have that option in the summary.
Laura 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/> > > 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. > [[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.
