similar to: Loading a "sub-package"

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

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

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

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

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

I'm using external pointers and seemingly leaking memory. My determination of a memory leak is that the R process continually creeps up in memory as seen by top while the usage as reported by gc() stays flat. I have isolated the C code: void h5R_allocate_finalizer(SEXP eptr) { Rprintf("Calling the finalizer\n"); void* vector = R_ExternalPtrAddr(eptr); free(vector);

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

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

Dear list, In using the scatter.smooth() function (modreg) on a small data set (100 obs) the following error was produced: > scatter.smooth(Na, S) Warning message: k-d tree limited by memory. ncmax= 200 I haven't used scatter.smooth much but when I have, I haven't seen this message before. gc() returns > gc() used (Mb) gc trigger (Mb) Ncells 417693 11.2 667722

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

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

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

Hi: I have two matrices, A and B, where A is n x k, and B is m x k, where n >> m >> k. Is there a computationally fast way to count the number of times each row (a k-vector) of B occurs in A? Thanks for any suggestions. Best, Ravi. [[alternative HTML version deleted]]

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

Hi all, I am currently building an application based on R 1.7.1 (+ compiled C/C++ code + MySql + VB). I am building this application to work on 2 different platforms (Windows XP Professional (500mb memory) and Windows NT 4.0 with service pack 6 (1gb memory)). This is a very memory intensive application performing sophisticated operations on "large" matrices (typically 5000x1500

I find that because R functions are call by value, and because there are no pointer or reference types (a la C++), I am making fairly heavy use of lexical scoping to modify variables. E.g. outer <- function() { m <- matrix(0, 2, 2) inner <- function() { m[2,2] <<- 3 ... } } I am not too pleased with this, as it violates basic rules of structured programming, namely

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

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

I have many very large dataframes with 20 columns each. In order to conserve memory, I wish to separate the data frame into 20 vectors, each named the name of the dataframe followed by .1,.2,.3 .20. (For example purposes, one data frame is named ?testa?.) e.g. testa.1, testa.2, testa.3 I have written the code to do this (see below). I am trying to convert this into a function that I can reuse.

Hi, I have some problems with the memory usage of S4-generics. For example, I observed the following behaviour: > gc() used (Mb) gc trigger (Mb) Ncells 432091 11.6 531268 14.2 Vcells 116052 0.9 786432 6.0 > setClass("A",representation(x="numeric")); [1] "A" > setClass("B",representation(x="numeric")); [1] "B"