[RsR] Maximum number of variables with lmRob? Error: singular matrix encountered

[Fabricio Vasselai]

Dears, Thank you very much for your replies so far.

First, about my dataset, I am not allowed to publish it here yet. But I am
arranging to simulate something similar to show you a data-baase example of
the problem.

Manuel: you just gave me a good idea. And indeed, the problem does not
happen when I use lmrob! Awesome. The only problem is: when I run smaller
models, with 10 variables for instance, so lmRob does work for me, then the
results from lmRob and from lmrob are substantialy different. Sorry for the
silly question, but I would be very interesting in understanding why those
two versions of the package could give such a difference. It can be very
interesting to asnwer many theoretical qusetions.

S.Ellison: very right, it would make sense. But the problem still happens
with only one interaction, unfortunattely.

Thanks a lot right now for the insights.

FABRICIO



2012/8/28 S Ellison <S.Ellison using lgcgroup.com>

> One 'obvious' possibility with interactions for a lot of predictors is
> that you might have no cells in the full interaction table with more than 1
> observation. That would result in a singular model due to exact fitting.
>
> S Ellison
>
>
> > -----Original Message-----
> > From: r-sig-robust-bounces using r-project.org
> > [mailto:r-sig-robust-bounces using r-project.org] On Behalf Of
> > Fabricio Vasselai
> > Sent: 26 August 2012 06:33
> > To: r-sig-robust
> > Subject: [RsR] Maximum number of variables with lmRob? Error:
> > singular matrix encountered
> >
> > Dear list,
> >
> > I've been trying different models with lmRob for the past
> > days and I was
> > wondering: is there a maximum number of variables to include
> > in a model so to avoid the following error in lmRob?
> > The error message is:
> >
> > Error in lmRob.fit.compute(x2, y, x1 = x1, x1.idx = x1.idx,
> > nrep = nrep,  :
> >   Singular matrix encountered.Erro em ans[[i]]$call : $
> > operator is invalid for atomic vectors
> >
> >
> > It seems to me that there is a limit of 14 independent
> > variables plus the intercept. It can run any combination of
> > variables in any dataset I try, as far as final parameters
> > are not beyond this limit.
> > Is it right? I did not find information about this yet. Is
> > there a way to go beyond this number of variables?
> >
> > Thanks in advance!
> > Best,
> >
> > FABRICIO
> >
> >       [[alternative HTML version deleted]]
> >
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