similar to: Object size in bytes

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, when doing as.double() on an object that is already a double, the object seems to be copied internally, doubling the memory requirement. See example below. Same for as.character() etc. Is this intended? Example: % R --vanilla > x <- double(1e7) > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 234019 6.3 467875 12.5 350000 9.4 Vcells 10103774 77.1

Dear All, I'm developing an R application running inside of a Java daemon on multiple threads, and interacting with the parent daemon via stdin and stdout. Everything works perfectly fine except for having some memory leaks somewhere. Simplified version of the R app: while (TRUE) { con <- file('stdin', open = 'r', blocking = TRUE) line <- scan(con,

Hi, I'm just starting out with R and came across R_inferno.pdf by Patrick Burns just yesterday - I recommend it! His description of how 'growing' objects (e.g. obj <- c(obj, additionalValue) eats up memory prompted me to rewrite a function (which made such calls ~210 times) so that it used indexing into a dimensioned object instead (i.e. obj[i, ] <- additionalValue). This

hello, the question concerning to the memory used and g.c. after having removed objects. What is wrong? bevor ------- > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 313142 8.4 1801024 48.1 1835812 49.1 Vcells 809238 6.2 142909728 1090.4 178426948 1361.3 hier all attached objects

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

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

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

Hi, I am trying to minimize a rather complex function of 5 parameters with gafit and nlm. Besides some problems with both optimization algorithms (with respect to consistantly generating similar results), I tried to run this optimization about a hundred times for yet two other parameters. Unfortunately, as the log below shows, during that batch process R starts to eat up all my RAM,

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 was surprised to observe the following difference between 2.4.1 and 2.6.0 after a long overdue upgrade a few months ago of our departmental server. It wasn't a bug fix, but a subtle improvement. Here's the simplest example I could create. The size is excessive, on the order of the Netflix Competition data. The integer matrix is about 1.12 GB, and if coerced to numeric it is 2.24 GB.

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

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

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

Dear All, I would like some advice on creating R packages, passing by reference and oo R. I have created a package that is neither elegant nor extensible and rather cumbersome (it works). I would like to re write the code to make the package distributable (should it be of interest) and easy to maintain. The package is for Bayesian model determination via a reversible jump algorithm and has

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

Sorry, this might be a really basic question, but I'm trying to interpret the results from memory profiling, and I have a few questions (marked by *Q#*). From the summaryRprof() documentation, it seems that the four columns of statistics that are reported when setting memory.profiling=TRUE are - vector memory in small blocks on the R heap - vector memory in large blocks (from malloc) - memory

Dear R-ers, somehow it is not possible to increase nsize to more than 20000k. When I specify e.g. > R --vsize=10M --nsize=21000K the result is: free total (Mb) Ncells 99658 350000 6.7 Vcells 1219173 1310720 10.0 Maybe I have overlooked s.th.... Marcus -- +------------------------------------------------------- | Marcus Eger | E-Mail: eger.m at gmx.de (NEW) |

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