From: Prof Brian Ripley (email@example.com)
Date: Tue 11 May 2004 - 22:59:17 EST
On 11 May 2004, Peter Dalgaard wrote:
> "Liaw, Andy" <firstname.lastname@example.org> writes:
> > I tried the following on an Opteron 248, R-1.9.0 w/Goto's BLAS:
> > > y <- matrix(rnorm(14000*1344), 1344)
> > > x <- matrix(runif(1344*503),1344)
> > > system.time(fit <- lm(y~x))
> >  106.00 55.60 265.32 0.00 0.00
> > The resulting fit object is over 600MB. (The coefficient compoent is a 504
> > x 14000 matrix.)
> > If I'm not mistaken, SAS sweeps on the extended cross product matrix to fit
> > regression models. That, I believe, in usually faster than doing QR
> > decomposition on the model matrix itself, but there are trade-offs.
Roughly twice as fast but the price is accuracy.
> > could try what Prof. Bates suggested.
> Hmm. Shouldn't be all that much faster, but it will produce the Type I
> SS as you go along, whereas R probably wants to fit the 15 different
Nope, R can read off the Type I SSQs from the QR decomposition so only one
fit is done. (Effectively you remove the effect of one column at a time,
and you get the change in residual/regression SSq as a side effect. Take
a look at anova.lm, which just aggregates squared effects over terms.)
> I'm still surprised that R/S-PLUS manages to use a full 15 minutes on
> a single response variable. It might be due to the singularities --
> the SAS code indicated that there was a nesting issue with the "A"
> factor in the last 4-factor interaction. If so, a reformulation of the
> model might help.
I think we need to understand this better. My guess (but only a guess) is
that the model matrix has very many columns and is highly singular. If
the singularity is by design, a reformulation will help.
-- Brian D. Ripley, email@example.com 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
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