similar to: memory usage of S4 methods

Hi R-devel, If you did read my survey on Rhelp about reporting, you may have seen that I am implementing a way to handle outputs for R (mainly target output destinations: xHTML and TeX). In fact: I does have something that works for basic objects, entirely done with S4 classes, with the results visible at: http://www.stat.ucl.ac.be/ROMA/sample.htm http://www.stat.ucl.ac.be/ROMA/sample.pdf To

Hello everyone - I have a couple of questions about memory management of large objects. Thanks in advance for your response. I'm running R version 1.9.1 on solaris 8, compiled as a 32 bit app. My system has 12.0 GB of memory, with usually ~ 11GB free. I checked system limits using ulimit, and there is nothing set that would limit the maximum amount of memory for a process (with the

I must be reading the help file for gc() wrong. I thought it said that gc(verbose=FALSE) will run the garbage collection without printing the Ncells/Vcells summary. However, this is what I get: gc(verbose = FALSE) used (Mb) gc trigger (Mb) max used (Mb) Ncells 267097 14.3 531268 28.4 531268 28.4 Vcells 429302 3.3 20829406 159.0 55923977 426.7 I'm embedding this in an

Hi, I have quite some trouble with the package methods. "Environments" in R are a convenient way to emulate pointers (and avoid copies of large objects, or of large collections of objects). So far, so good, but the package methods is becoming more (and more) problematic to work with. Up to version R-1.7.0, slots that were environments were still references to an environment, but I

Is R file IO slow in general or am I missing something? It takes me 5 minutes to do a load(MYFILE) where MYFILE is a 27 MB Rdata file. Is there any way to speed this up? The one idea I have is having R call a C or Perl routine, reading the file in that language, converting the data in to R objects, then sending them back into R. This is more work that I want to do, however, in loading Rdata

I have a question concerning memory. I understood that R takes a fixed amount of memory at startup (which I can influence with --vsize --nsize) and that gc() shows the memory still free of the total memory reserved for R. However, if I create a long vector of character data, gc() only seem to reflect the space needed for a vector of pointers to char, the space used for the character data itself

Is it expected behavior for gc() to return shrinking values as it gets called multiple times? Here's what I've got: > gc() used (Mb) gc trigger (Mb) Ncells 221754 6.0 467875 12.5 Vcells 3760209 28.7 14880310 113.6 > gc() used (Mb) gc trigger (Mb) Ncells 221760 6.0 467875 12.5 Vcells 3016206 23.1 11904247 90.9 > gc() used (Mb) gc

Please, I have a 2GB computer and a huge time-series to embedd, and i tried increasing memory.limit() and memory.size(max=TRUE), but nothing. Just before the command: > memory.size(max=TRUE) [1] 13.4375 > memory.limit() [1] 1535.875 > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 209552 5.6 407500 10.9 350000 9.4 Vcells 125966 1.0 786432 6.0 496686 3.8

I came across this in a simulation I ran under 1.6.0: If I do something like R> x <- rnorm(10) R> rval <- NULL R> for(i in 1:100000) rval <- t.test(x)$p.value then the process size remains at about 14M under 1.5.1, but it seems to be almost linearly growing up to more than 100M under 1.6.0. I know that the above simulation is nonsense, but it was the simplest I could come up

Have searched through the archives but have been unable to find any related issues - hopefully I'm not bringing up an old topic. Am using RW1041 on a Windows NT on a machine with 1Gb of memory. Have a function doit() that reads in a chunk of data using readBin, performs a regression, saves out coeffs and then returns. When using Rgui with the default memory limit of 256Mb I'm able to

Dear useRs? I have the following problem! I have a function that calls one or more functions, depending on the input parameters. I am searching for the fastest way to select and execute the selected functions and return their results in a list. The number of possible functions is 10, however usually only 2 are selected (although sometimes more, even all). For examples, if I have function

I am getting "Error: cannot allocate vector of size 197 MB". I know that similar problems were discussed a lot already, but I didn't find any satisfactory answers so far! Details: *** I have XP (32bit) with 4GB ram. At the time when the problem appeared I had 1.5GB of available physical memory. *** I increased R memory limit to 3GB via memory.limit(3000) *** I did gs() and got

hello! ------ i use 32bit.Linux(SuSe)Server, so i'm limited with 3.5Gb of memory i demonstrate, that there is times to times a problem with allocating of objects of large size, for example 0.state (no objects yet created) ------------------------------------ > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 162070 4.4 350000 9.4 350000

I'm working with a very large matrix ( 22k rows x 2k cols) of RNA expression data with R v.2.5.0 on a RedHat Enterprise machine, x86_64 architecture. The relevant code is below, but I call a function that takes a cluster of this data ( a list structure that contains a $rows elt which lists the rows (genes ) in the cluster by ID, but not the actual data itself ). The

Hello all. I have a large .txt file whose variables are fixed-columns, ie, variable V1 goes from columns 1 to 7, V2 from 8 to 23 etc. This is a 60GB file with 90 variables and 60 million observations. I'm working with a Pentium 4, 1GB RAM, Windows XP Pro. I tried the following code just to see if I could work with 2 variables but it seems not possible: R : Copyright 2005, The R Foundation

How do people deal with R and memory issues? I have tried using gc() to see how much memory is used at each step. Scanned Crawley R-Book and all other R books I have available and the FAQ on-line but no help really found. Running WinXP Pro (32 bit) with 4 GB RAM. One SATA drive pair is in RAID 0 configuration with 10000 MB allocated as virtual memory. I do have another machine

I've seen several posts over the past 2-3 weeks about memory issues. I've tried to carefully follow the suggestions, but remain baffled as to why I can't load data into R. I hope that in revisiting this issue that I don't exasperate the list. The setting: 1 gig RAM , Linux machine 10 Stata files of approximately 14megs each File contents appear at the end of this boorishly long

I've seen several posts over the past 2-3 weeks about memory issues. I've tried to carefully follow the suggestions, but remain baffled as to why I can't load data into R. I hope that in revisiting this issue that I don't exasperate the list. The setting: 1 gig RAM , Linux machine 10 Stata files of approximately 14megs each File contents appear at the end of this boorishly long

dear R experts: I am of course no R experts, but use it regularly. I thought I would share some experimentation with memory use. I run a linux machine with about 4GB of memory, and R 2.5.0. upon startup, gc() reports used (Mb) gc trigger (Mb) max used (Mb) Ncells 268755 14.4 407500 21.8 350000 18.7 Vcells 139137 1.1 786432 6.0 444750 3.4 This is my baseline. linux

Hi developers, After some investigation I have found there can be large discrepancies in the same object being saved as an external "xx.RData" file. The immediate repercussion of this is the possible increased size of your .RData workspace for no apparent reason. The function and its three scenarios below highlight these discrepancies. Note that the object being returned is exactly