> It **might** also help the OP clarify his intent in > exactly the way you describe (or not).
My intent is to perform a linear regression on a metric dependent variable. Unfortunately, two key assumptions - normal distribution and homoscedasticity - cannot be met. I read that using a robust regression is the way to go in this case, and I wanted to ask whether this is possible in R, and if so, whether there is a robust regression that can handle the lack of both assumptions. On top of that, I get different results using the Huber and Bisquare weighting functions, and don't know what that implies. Thanks > > On Sun, Oct 7, 2012 at 10:47 PM, Prof Brian Ripley > <rip...@stats.ox.ac.uk> wrote: > > On 08/10/2012 00:37, Bert Gunter wrote: > >> > >> Have you checked the Robust task view on CRAN?? Would seem that that > >> should have been the first place to look. > > > > > > It is still a conceptual question. I presume this means an ordered > > response, and then we need to know what is meant by 'regression'. > > > > If you tell us precisely what robust method you want to know about, you > may > > get help about whether it is available in R. But I surmise that you > need > > rather to be looking at ordinal regression (polr in MASS, for example), > and > > you will not find that in the 'Robust' task view. In the task view, > > 'robust' is a technical term and I don't think 'Elko Fried' is using it > in > > the sense the author of the task view is. > > > >> > >> -- Bert > >> > >> On Sun, Oct 7, 2012 at 3:30 PM, Eiko Fried <tor...@gmail.com> wrote: > >>> > >>> Thank you Jeff! Please ignore the first of my two questions then, and > >>> apologies for not making it clear that my second question was about R. > >>> > >>> (2) "Are there ways of using robust regressions with ordered data" ... > in > >>> R? > >>> > >>> Thank you > >>> > >>> > >>> On 7 October 2012 18:26, Jeff Newmiller <jdnew...@dcn.davis.ca.us> > wrote: > >>> > >>>> This does not appear to be a question about R. You should post in a > list > >>>> or forum dedicated to discussing statistics theory, such as > >>>> stats.stackoverflow.com. > >>>> > >>>> > --------------------------------------------------------------------------- > >>>> Jeff Newmiller The ..... ..... Go > >>>> Live... > >>>> DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. Live > >>>> Go... > >>>> Live: OO#.. Dead: OO#.. > >>>> Playing > >>>> Research Engineer (Solar/Batteries O.O#. #.O#. with > >>>> /Software/Embedded Controllers) .OO#. .OO#. > >>>> rocks...1k > >>>> > >>>> > --------------------------------------------------------------------------- > >>>> Sent from my phone. Please excuse my brevity. > >>>> > >>>> Eiko Fried <tor...@gmail.com> wrote: > >>>> > >>>>> I have two regressions to perform - one with a metric DV (-3 to 3), > the > >>>>> other with an ordered DV (0,1,2,3). > >>>>> > >>>>> Neither normal distribution not homoscedasticity is given. I have a > two > >>>>> questions: > >>>>> > >>>>> (1) Some sources say robust regression take care of both lack of > normal > >>>>> distribution and heteroscedasticity, while others say only of normal > >>>>> distribution. What is true? > >>>>> (2) Are there ways of using robust regressions with ordered data, or > is > >>>>> that only possible for metric DVs? > >>>>> > >>>>> Thanks > >>>>> Torvon > >>>>> > >>>>> [[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. > >>>> > >>>> > >>>> > >>> > >>> [[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. > >> > >> > >> > >> > > > > > > -- > > Brian D. Ripley, rip...@stats.ox.ac.uk > > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ > > University of Oxford, Tel: +44 1865 272861 (self) > > 1 South Parks Road, +44 1865 272866 (PA) > > Oxford OX1 3TG, UK Fax: +44 1865 272595 > > > > -- > > 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 > [[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.
