Thanks all. This is tremendously helpful. Best, Adam
On Feb 29, 2012, at 12:58 PM, David Reiner wrote: > My understanding is that TLS, EIV, and orthogonal regression are closely > related but separate concepts. > If you read the 'Talk' at the Wikipedia page referenced below, you will see > that many people have > terminology problems as well. > My take is that TLS is a special case of EIV and orthogonal linear regression > is a special case of TLS. > ** If your data is centered, then the orthogonal regression slope is just the > ratio of the standard deviations of the two variables. ** > You can get the same thing from PCA if you first scale by the SD's and then > restore them after finding the first eigenvector. > The TLS and EIV approaches are more general, but assuming that the relative > errors in the variables are equal, and things are 'nice' gives the simple > result above. > > The page Mark refers to from Sabine van Huffel's book on TLS is visible in > Google books. > > HTH, > -- David > > > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Mark Leeds > Sent: Wednesday, February 29, 2012 12:37 PM > To: Adam Waytz > Cc: <r-help@r-project.org>; Bert Gunter > Subject: Re: [R] orthogonal distance regression package? > > Hi: I can't find it anywhere on the internet but I have a book that shows > that, as long as the SVD of the X matrix can be obtained, then the > coefficient solution to TLS ( least angle regression ) is only a function of > the eigenvectors. > Therefore, principal components can be used to obtain the coefficients in TLS > which could be why there may not be an R package out there. > > The book is titled "The Total Least Squares Problem" Huffel and Vandewalle. > > Paul Teetor's paper ( see link below ) has an example of using principal > components to calculate the coefficients in a univariate TLS. > > Disclaimer: I've never used TLS regression and never studied it so there > could be subtlleties where the result doesn't hold. The result is on page > 37 of the book and the book is almost 300 pages so the SVD approach must not > work all the time. > > https://docs.google.com/viewer?a=v&q=cache:h5YT7w7fQXkJ:quanttrader.info/public/betterHedgeRatios.pdf+&hl=en&gl=us&pid=bl&srcid=ADGEESjbXq-o_3J148Ex376HqUTLCTbDyuH921wEkyze_uT8wlwhvpK8ywgp9ZBNPFTe9p7TbxTgHdNhD3BwjFSPD6H9ln1mIKDN1y0yKXOb9c3zHYhQnAuCtVx3aptuL7P2FtvIrl-0&sig=AHIEtbRl0WGG4c551EHnuOYP3cQ1RaEsBA&pli=1 > '' > > > > > > > > On Wed, Feb 29, 2012 at 1:19 PM, Adam Waytz < > a-wa...@kellogg.northwestern.edu> wrote: > >> >> In the age of google, I have found that concepts such as these are >> more complex than what Wikipedia provides. Going far beyond a cursory >> search, it appeared to me there are subtle differences between these >> terms. I was hoping this knowledgeable community could provide insight >> on an R package to perform ODR. Thank you. >> >> On Feb 29, 2012, at 12:07 PM, "Bert Gunter" <gunter.ber...@gene.com> >> wrote: >> >>> On Wed, Feb 29, 2012 at 7:53 AM, Adam Waytz >>> <a-wa...@kellogg.northwestern.edu> wrote: >>>> >>>> Hello, >>>> >>>> I am extremely new to R and have found some leads to this question >>>> in >> the archives, but I am still a bit uncertain. >>>> I am looking for an R package to carry out orthogonal distance >> regression. I found some answers regarding Deming >>>> regression and Total Least Squares regression, but I was unclear if >> these are identical terms. >>> >>> In the age of Google?! >>> >>> Searching on "orthogonal regression" brought up: >>> >>> http://en.wikipedia.org/wiki/Total_least_squares >>> >>> which provides info. Sheesh! >>> >>> I suggest you also check the ChemPhys and Econometrics task views on >>> CRAN to see what they have to offer. >>> >>> Incidentally, my very limited understanding is that orthogonal >>> regression (for errors in variables) can be problematic. The >>> wikipedia article provides more details. >>> >>> -- Bert >>> >>> Please let me know if >>>> a package is available. >>>> >>>> Thank you, >>>> Adam >>>> >>>> ______________________________________________ >>>> 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. >>> >>> >>> >>> -- >>> >>> Bert Gunter >>> Genentech Nonclinical Biostatistics >>> >>> Internal Contact Info: >>> Phone: 467-7374 >>> Website: >>> >> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb >> -biostatistics/pdb-ncb-home.htm >> >> ______________________________________________ >> 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. > > > This e-mail and any materials attached hereto, including, without limitation, > all content hereof and thereof (collectively, "XR Content") are confidential > and proprietary to XR Trading, LLC ("XR") and/or its affiliates, and are > protected by intellectual property laws. 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