search for: ncells

...e ccprox(nexmn,nitem,respmat2,covmatr,covmatt, $ cormatr,cormatt) !ccccc This subroutine will calculate all the pairwise !ccccc conditional covariances. An ending r means for !ccccc rest score, an ending t means for total score. parameter (maxitem=100,maxexmn=10000,maxcats=10,ncells=1001) !ccccc ncells must be maxitem*maxcats+1 integer nexmn,nitem,scoretemp,mscore integer respmat(maxexmn,maxitem) integer tscore(maxexmn) /maxexmn*0/ integer tstemp(maxexmn) /maxexmn*0/ integer natscore(ncells) /ncells*0/ integer natemp(ncells) /ncells*0/ integer...

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 trigger (Mb) Ncells 221760 6.0 467875 12.5 Vcells 2420993 18.5 9523397 7...

Dear all, I am working on a 64 bits Linux system. I issue the following R commands: > rm(list=ls()) # To remove all objects in the workspace. > gc() # To free memory. used (Mb) gc trigger (Mb) max used (Mb) Ncells 124250 6.7 350000 18.7 350000 18.7 Vcells 124547 1.0 786432 6.0 476934 3.7 > gc() # I had to do it again, don't know why! used (Mb) gc trigger (Mb) max used (Mb) Ncells 124257 6.7 350000 18.7 350000 18.7 Vcells 124574 1.0 786432 6.0 476934 3.7 > gc() # Just to be sure things have stabil...

...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 ... hidden!? Here the data: I have 6 MB heap, and can do > x <- rep(1, 350000); gc(); rm(x); gc() free total (Mb) Ncells 61034 250000 4.8 Vcells 350955 786432 6.0 free total (Mb) Ncells 61036 250000 4.8 Vcells 700956 786432 6.0 which is using about 2.67 MB RAM for double numbers. Trying for 3.05 MB fails (well, rep() obviously needs twice the memory, x <- double(700000) works) > x <- rep(1...

...ble() 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 11476770 87.6 10104223 77.1 > x <- as.double(x) > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 234113 6.3 467875 12.5 350000 9.4 Vcells 10103790 77.1 21354156 163.0 20103818 153.4 However,...

...tly the same new objects. 2. is the modest decrease in memory consumed by new method expected? (having read R_Inferno I was, perhaps naively, expecting more of an improvement) ? or am I missing something (more than likely! ) Thanks M > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 786300 21.0 1265230 33.8 1166886 31.2 Vcells 948412 7.3 3244126 24.8 3766604 28.8 > cat("old version") Virtual size before call: 881692 user system elapsed 131.872 14.417 159.653 Virtual size after call: 899404 > 899404-881692 [1] 17712 ################## &gt...

...ocessed: >> I am working on a 64 bits Linux system. >> I issue the following R commands: >> > rm(list=ls()) # To remove all objects in the workspace. >> > gc() # To free memory. >> used (Mb) gc trigger (Mb) max used (Mb) >> Ncells 124250 6.7 350000 18.7 350000 18.7 >> Vcells 124547 1.0 786432 6.0 476934 3.7 >> > gc() # I had to do it again, don't know why! >> used (Mb) gc trigger (Mb) max used (Mb) >> Ncells 124257 6.7 350000 18.7 350000 18.7 >> Vcells 124574 1....

...ata that my function needs is loaded into the environment prior to optimization. Either algorithm seems to accumulate cons cells. Any idea what could be the cause? My code maybe? Is this a memory leak within R? Is there any other way to force garbage collection (or something equivalent) on the ncells? Thanks. - Jan P.S. I run R v1.5.1 on i386 SuSE Linux v7.3 using the precompiled binaries from CTAN for SuSE v7.3. ----------------- > fName ="designOpt"; Sweave(paste(fName, "rnw", sep="."), driver=Rtangle()); sourc e(paste(fName, "R", sep="....

...on't believe this is the case in this situation. I verified that I don't have any objects in memory when R starts up, and that memory limits are set to NA. Here is some output: > ls() character(0) > mem.limits() nsize vsize NA NA > gc() used (Mb) gc trigger (Mb) Ncells 432197 11.6 531268 14.2 Vcells 116586 0.9 786432 6.0 > v<-rep(0,268435431) Error: cannot allocate vector of size 2097151 Kb > v<-rep(0,268435430) > object.size(v) [1] 2147483468 > gc() used (Mb) gc trigger (Mb) Ncells 432214 11.6 741108 19.8 Vc...

...form sparc-sun-solaris2.8 arch sparc os solaris2.8 system sparc, solaris2.8 status major 1 minor 3.1 year 2001 month 08 day 31 language R A little example: Start a new section of R, with no objects: > ls() character(0) > gc() used (Mb) gc trigger (Mb) Ncells 187633 5.1 407500 10.9 Vcells 36061 0.3 786432 6.0 > And the top commands shows: PID USERNAME THR PRI NICE SIZE RES STATE TIME CPU COMMAND 18068 gardar 1 32 0 15M 12M sleep 0:02 0.01% R.bin Creating few objects: > x <- numeric(1e7) > gc()...

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 detached and other no more used ones removed; also the objects which could change their size (like .Traceback) are checke...

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,

...g Rgui with the default memory limit of 256Mb I'm able to call this function twice before running out of memory. After the allocation fails the GC is called & everything is freed, ie we end up with the same memory as before. Here is an example: > gc() used (Mb) gc trigger (Mb) Ncells 217415 5.9 467875 12.5 Vcells 63668 0.5 786432 6.0 > doit() > gc() used (Mb) gc trigger (Mb) Ncells 342932 9.2 667722 17.9 Vcells 7966401 60.8 14760173 112.7 > doit() > gc() used (Mb) gc trigger (Mb) Ncells 468373 12.6 818163 21.9 Vce...

...on<-file('stdin',open='r',blocking=TRUE);line<-scan(con,what=character(0),nlines=1,quiet=TRUE);close(con);rm(con);a <- gc(); i <- i+1; if(i %% 1000 == 1) {cat('i=',i,'\\n'); print(a)} }" 11059 i= 1 used (Mb) gc trigger (Mb) max used (Mb) Ncells 83216 4.5 10000000 534.1 213529 11.5 Vcells 172923 1.4 16777216 128.0 562476 4.3 i= 1001 used (Mb) gc trigger (Mb) max used (Mb) Ncells 83255 4.5 10000000 534.1 213529 11.5 Vcells 172958 1.4 16777216 128.0 562476 4.3 ....... ..........................................

Hi, I have used the mgcv library to generate a simple additive model. I want to know how to plot the function on the raw data with confidence intervals whan I have TWO variables in the model. I get it to work with one variable but not with two. I am on the limit for what I understand in R, so be gentle. I have read the help file on predict.gam, but did not get any help out of it. #My model:

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 Sweave/TeX file, so I *really* can't have this...

...r, 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 9.4 Vcells 59921 0.5 786432 6.0 281974 2.2 1.state: let create now a vector of large size -------------------------------------------------- > my.vector<-rnorm(100000*500) > object.size(my.vector)/1024^2 [...

I am running R version ??? under Redhat 5.2. It seems as though the --nsize object has no effct on the size of the allocated Ncells as determined using gc(). Yes, I have that much data.... That is if I envoke R with R --vsize 100 --nsize 5000000 then type gc() I get free total Ncells 92202 200000 Vcells 12928414 13107200 Thanks Tony Long Ecology and Evolutionary Biology Steinhaus Hall University of California at Ir...

Everyone, I have an array of integer values which are located on a uniform 2-D grid. I want to plot the integer values at the node locations. The closest I have come is with the following code: for (i in 1:ny) { for (j in 1:nx) { ncell <- nx*(i - 1) + j ch <- as.character(ncount[ncell]) tx <- j*dx - dx/2 ty <- ny*dy - (i-1)*dy - dy/2 points(tx, ty, pch = ch, col =

...n 1 GB (roughly) but still within the boundaries of the RAM. It may be a useful developer tool for big-data problems. ------------------------ R version 2.4.1 (linux): > x <- matrix(as.integer(0), 1e+08, 3) > x <- x + 1 > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 233754 12.5 467875 25 350000 18.7 Vcells 300119431 2289.8 787870506 6011 750119944 5723.0 ------------------------ R version 2.6.0 (linux): > x <- matrix(as.integer(0), 1e+08, 3) > x <- x + 1 > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells...