the package is called "quantreg" and yes, it solved my problem. LAD regression was actually the method I was looking for, thanks a lot.
However, some of my problems are rather large and even if I use the method 'fn' and 'pfn' recommended for large problems in 'rq' I get an error:
> res <- rq(o ~ y + s -1,tau=0.5,method="fn",contrasts=list(s=("contr.sum")))
Error: cannot allocate vector of size 1950212 Kb
Is there are way to avoid that? The least square procedures are able to handle these large problems.
best wishes,
joerg
Patrick Burns wrote:
Two possible alternatives are:
1) Use least absolute deviation regression, which you can get from Roger Koenker's package that I think is called "quantile".
2) The LAD regression is essentially equivalent to median polish (see, for instance, "Understanding Robust and Exploratory Data Analysis" by Hoaglin, Mosteller and Tukey). To gain some more efficiency for nearly Gaussian data, you could replace the median by a location estimator that is more efficient, such as a trimmed mean.
Good luck,
Patrick Burns
Burns Statistics [EMAIL PROTECTED] +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and "A Guide for the Unwilling S User")
Joerg Schaber wrote:
Hi,
trying to do a robust regression of a two-way linear model, I keep getting the following error:
> lqs(obs ~ y + s -1,method="lms", contrasts=list(s=("contr.sum"))) Error: lqs failed: all the samples were singular
Robust regression with M-estimators works (also regular least square fits, of course):
rlm.formula(formula = obs ~ y + s - 1, method = "M", contrasts = list(s = ("contr.sum")))
I tried an exact sampling (psamp="exact"), but I keep getting syntax errors. Any idea how I can make the first one work?
Thanks,
joerg
-- ---------------------------------------------------------- J�rg Schaber Instituto Cavanilles de Biodiversidad y Biologia Evolutiva Universidad de Valencia Tel.: ++34 96 354 3666 A.C. 22085 Fax.: ++34 96 354 3670 46071 Valencia, Espa�a email : [EMAIL PROTECTED]
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