[R] R versus SAS: lm performance

[Peter Dalgaard]

"Liaw, Andy" <andy_liaw at merck.com> 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))
> [1] 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.  You
> 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
models. 

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. 

-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
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