> 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
> <ripley@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 <torvon@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 <jdnewmil@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:<jdnewmil@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 <torvon@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,                  ripley@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
>

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