you haven't answered how much resource you have available when you try
reading in the data.
with the mouse exon chip, the math is the same i mentioned before.
having 8 GB, you should be able to read in 70 samples of this chip. if
you can't, that's because you don't have enough resources when trying
to read.
best,
b
On Nov 7, 2009, at 10:12 AM, Peng Yu wrote:
On Fri, Nov 6, 2009 at 8:19 PM, Benilton Carvalho
<bcarv...@jhsph.edu> wrote:
this is converging to bioc.
let me know what your sessionInfo() is and what type of CEL files
you're
trying to read, additionally provide exactly how you reproduce the
problem.
Here is my sessionInfo(). pname is 'moex10stv1cdf'.
for (f in list.celfiles('.',full.names=T,recursive=T)) {
+ print(f)
+ pname=cleancdfname(whatcdf(f))
+ print(pname)
+ }
sessionInfo()
R version 2.9.2 (2009-08-24)
x86_64-unknown-linux-gnu
locale:
LC_CTYPE
=
en_US
.UTF
-8
;LC_NUMERIC
=
C
;LC_TIME
=
en_US
.UTF
-8
;LC_COLLATE
=
en_US
.UTF
-8
;LC_MONETARY
=
C
;LC_MESSAGES
=
en_US
.UTF
-8
;LC_PAPER
=
en_US
.UTF
-8
;LC_NAME
=
C
;LC_ADDRESS
=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] pd.moex.1.0.st.v1_2.4.1 RSQLite_0.7-2 DBI_0.2-4
[4] oligo_1.8.3 preprocessCore_1.6.0 oligoClasses_1.6.0
[7] Biobase_2.4.1
loaded via a namespace (and not attached):
[1] affxparser_1.16.0 affyio_1.12.0 Biostrings_2.12.9
IRanges_1.2.3
[5] splines_2.9.2
it appears to me, i'm not sure, that you start a fresh session of R
and then
tries to read in the data - how much resource do you have available
when you
try reading in the data? having 8GB RAM does not mean that you have
8GB when
you tried the task.
b
On Nov 7, 2009, at 12:08 AM, Peng Yu wrote:
On Fri, Nov 6, 2009 at 5:00 PM, Marc Schwartz <marc_schwa...@me.com>
wrote:
On Nov 6, 2009, at 4:19 PM, Peng Yu wrote:
On Fri, Nov 6, 2009 at 3:39 PM, Charlie Sharpsteen
<ch...@sharpsteen.net>
wrote:
On Fri, Nov 6, 2009 at 1:30 PM, Peng Yu <pengyu...@gmail.com>
wrote:
I run R on a linux machine that has 8GB memory. But R gives me
an
error "Error: cannot allocate vector of size 3.4 Gb". I'm
wondering
why it can not allocate 3.4 Gb on a 8GB memory machine. How to
fix the
problem?
Is it 32-bit R or 64-bit R?
Are you running any other programs besides R?
How far into your data processing does the error occur?
The more statements you execute, the more "fragmented" R's
available
memory pool becomes. A 3.4 Gb chunk may no longer be available.
I'm pretty sure it is 64-bit R. But I need to double check. What
command I should use to check?
It seems that it didn't do anything but just read a lot of files
before it showed up the above errors.
Check the output of:
.Machine$sizeof.pointer
If it is 4, R was built as 32 bit, if it is 8, R was built as 64
bit.
See
?.Machine for more information.
It is 8. The code that give the error is listed below. There are 70
celfiles. I'm wondering how to investigate what cause the problem
and
fix it.
library(oligo)
cel_files = list.celfiles('.', full.names=T,recursive=T)
data=read.celfiles(cel_files)
You can also check:
R.version$arch
and
.Platform$r_arch
which for 64 bit should show x86_64.
HTH,
Marc Schwartz
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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
