x 150)
and
is diagonal.
That might not solve it, but it should help.
Max
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Florian Nigsch
Sent: Thursday, July 26, 2007 2:07 PM
To: r-help@stat.math.ethz.ch
Subject: [R] Large dataset
I compiled the newest R version on a Redhat Linux (uname -a =
Linux .cam.ac.uk 2.4.21-50.ELsmp #1 SMP Tue May 8 17:18:29 EDT 2007
i686 i686 i386 GNU/Linux) with 4GB of physical memory. The step when
the whole script crashed is within the randomForest() routine, I do
know that because I
[Please CC me in any replies as I am not currently subscribed to the
list. Thanks!]
Dear all,
I did a bit of searching on the question of large datasets but did
not come to a definite conclusion. What I am trying to do is the
following: I want to read in a dataset with approx. 100 000 rows
@stat.math.ethz.ch
Subject: [R] Large dataset + randomForest
[Please CC me in any replies as I am not currently subscribed to the
list. Thanks!]
Dear all,
I did a bit of searching on the question of large datasets but did
not come to a definite conclusion. What I am trying to do
JENNIFER HILL jh1030 at columbia.edu writes:
Hi, I need to analyze data that has 3.5 million observations and
about 60 variables and I was planning on using R to do this but
I can't even seem to read in the data. It just freezes and ties
up the whole system -- and this is on a Linux box
at
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/76583.html
HTH,
Rogerio Porto.
-- Cabeçalho original ---
De: [EMAIL PROTECTED]
Para: r-help@stat.math.ethz.ch
Cópia:
Data: Sun, 2 Jul 2006 10:12:25 -0400 (EDT)
Assunto: [R] large dataset!
Hi, I need to analyze data that has
Hi, I need to analyze data that has 3.5 million observations and
about 60 variables and I was planning on using R to do this but
I can't even seem to read in the data. It just freezes and ties
up the whole system -- and this is on a Linux box purchased about
6 months ago on a dual-processor PC
Jennifer,
it sounds like that's too much data for R to hold in your computer's
RAM. You should give serious consideration as to whether you need all
those data for the models that you're fitting, and if so, whether you
need to do them all at once. If not, think about pre-processing
steps, using
Hello Jennifer,
I'm writing a package SQLiteDF for Google SOC2006, under the
supervision of Prof. Bates Prof. Riley. Basically, it stores data
frame into sqlite databases (i.e. in a file) and aims to be
transparently accessible to R using the same operators for ordinary
data frames.
Right now,
Dear useRs
We have a data-set (comma delimited) with 12Millions of rows, and 5
columns (in fact many more, but we need only 4 of them): id, factor 'a'
(5 levels), factor 'b' (15 levels), date-stamp, numeric measurement. We
run R on suse-linux 9.1 with 2GB RAM, (and a 3.5GB swap file).
on
Christoph Lehmann a écrit :
Dear useRs
We have a data-set (comma delimited) with 12Millions of rows, and 5
columns (in fact many more, but we need only 4 of them): id, factor 'a'
(5 levels), factor 'b' (15 levels), date-stamp, numeric measurement. We
run R on suse-linux 9.1 with 2GB RAM, (and a
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