The way to time things in R is system.time().
Without knowing much more about your problem we can only guess where R is
spending the time. But you can find out by profiling -- see `Writing R
Extensions'.
If you want multiple fits with the same design matrix (do you?) you
could look at the
PROTECTED]
Subject: Re: [R] R versus SAS: lm performance
The way to time things in R is system.time().
Without knowing much more about your problem we can only
guess where R is
spending the time. But you can find out by profiling -- see
`Writing R
Extensions'.
If you want multiple
[EMAIL PROTECTED] writes:
Hello,
A collegue of mine has compared the runtime of a linear model + anova in SAS and S+.
He got the same results, but SAS took a bit more than a minute whereas S+ took 17
minutes. I've tried it in R (1.9.0) and it took 15 min. Neither machine run out of
, Arne PH/FR
Cc: [EMAIL PROTECTED]
Subject: Re: [R] R versus SAS: lm performance
The way to time things in R is system.time().
Without knowing much more about your problem we can only
guess where R is
spending the time. But you can find out by profiling -- see
`Writing R
2004 09:08
To: Muller, Arne PH/FR
Cc: [EMAIL PROTECTED]
Subject: Re: [R] R versus SAS: lm performance
The way to time things in R is system.time().
Without knowing much more about your problem we can only
guess where R is
spending the time. But you can find out by profiling
I would be curious to know how sparse the model.matrix for this problem
is...
Unless it is quite dense, or as Brian implies quite singular, I might
suggest
computing a Cholesky factorization in SparseM.
url:www.econ.uiuc.edu/~rogerRoger Koenker
email [EMAIL PROTECTED]
Liaw, Andy [EMAIL PROTECTED] 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
On 11 May 2004, Peter Dalgaard wrote:
Liaw, Andy [EMAIL PROTECTED] 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
Prof Brian Ripley [EMAIL PROTECTED] writes:
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.
Nope, R can read off the Type I SSQs from the QR decomposition so only one
fit is done.
Pharma
arne dot muller domain=aventis com
-Original Message-
From: Liaw, Andy [mailto:[EMAIL PROTECTED]
Sent: 11 May 2004 14:20
To: Muller, Arne PH/FR; [EMAIL PROTECTED]
Cc: [EMAIL PROTECTED]
Subject: RE: [R] R versus SAS: lm performance
I tried the following on an Opteron 248, R
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