rlm includes an MM estimator. S Ellison
> -----Original Message----- > From: Maheswaran Rohan [mailto:mro...@doc.govt.nz] > Sent: 22 July 2012 23:08 > To: Valentin Todorov; S Ellison > Cc: r-sig-rob...@r-project.org; r-help > Subject: RE: [RsR] How does "rlm" in R decide its "w" weights > for each IRLSiteration? > > Hi Valentin, > > If the contamination is mainly in the response direction, > M-estimator provides good estimates for parameters and rlm > can be used. > > Rohan > > -----Original Message----- > From: r-sig-robust-boun...@r-project.org > [mailto:r-sig-robust-boun...@r-project.org] On Behalf Of > Valentin Todorov > Sent: Saturday, 21 July 2012 6:57 a.m. > To: S Ellison > Cc: r-sig-rob...@r-project.org; r-help > Subject: Re: [RsR] How does "rlm" in R decide its "w" weights > for each IRLSiteration? > > Hi Michael, S Ellison, > > I do not actually understand what you want to achieve with > the M estimates of rlm in MASS, but why you do not give a try > of lmrob in 'robustbase'. Please have a llok in the > references (?lmrob) about the advantages of MM estimators > over the M estimators. > > Best regards, > Valentin > > > > > On Fri, Jul 20, 2012 at 5:11 PM, S Ellison <s.elli...@lgcgroup.com> > wrote: > > > > > >> -----Original Message----- > >> Subject: [RsR] How does "rlm" in R decide its "w" weights for each > >> IRLS iteration? > >> I am also confused about the manual: > >> > >> a. The input arguments: > >> > >> wt.method are the weights case weights (giving the relative > >> importance of case, so a weight of 2 means there are two of > >> these) or the inverse of the variances, so a weight of two > means this > >> error is half as variable? > > > > When you give rlm weights (called 'weights', not 'w' on > input, though > you can abbreviate to 'w'), you need to tell it which of > these two possibilities you used. > > If you gave it case numbers, say wt.method="case"; if you gave it > inverse variance weights, say wt.method="inv.var". > > The default is "inv.var". > > > > > >> The input argument "w" is used for the initial values of > the rlm IRLS > >> weighting and the output value "w" is the converged "w". > > There is no input argument 'w' for rlm (see above). > > The output w are a calculated using the psi function, so between 0 > and 1. > > The effective weights for the final estimate would then be something > like w*weights, using the full name of the input argument > (and if I haven't forgotten a square root somewhere). At > least, that would work for a simple location estimate (eg rlm(x~1)). > > > >> If my understanding above is correct, how does "rlm" > decide its "w" > >> for each IRLS iteration then? > > It uses the given psi functions to calculate the iterative weights > based on the scaled residuals. > > > >> Any pointers/tutorials/notes to the calculation of these "w"'s in > >> each IRLS iteration? > > Read the cited references for a detailed guide. Or, of > course, MASS - > the package is, after all, intended to support the book, not > replace it. > > > > > > > > S Ellison > > > > ******************************************************************* > > This email and any attachments are confidential. Any > u...{{dropped:6}} > > _______________________________________________ > r-sig-rob...@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-robust > ############################################## > This e-mail (and attachments) is confidential and may be > legally privileged. > ############################################## > ******************************************************************* This email and any attachments are confidential. Any use...{{dropped:8}} ______________________________________________ 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.
