similar to: Large database help

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

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

Hi. As shown in the simplified example below, I'm having trouble allocating memory for large vectors, even though it would appear that there is more than enough memory available. That is, even with a memory limit of 1500 MB, R 2.6.1 (Win) will allocate memory for a first vector of 285 MB, but not for a second vector of the same size. Forcing garbage collection does not seem

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 believe there is a memory leak in isoreg in the current version of R, as I believe the following shows > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 120405 3.3 350000 9.4 350000 9.4 Vcells 78639 0.6 786432 6.0 392463 3.0 > for(k in 1:100) { + + y <- runif(10000) + isoreg(x,y) + } > rm(x) > rm(y) > 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 have always been using ebitbin to set the 3GB switch in the windows binary, but version 2.2.1 has this set as default (which I verified using dumpbin). However, when I generate junk data to fill up my memory and read the memory usage using gc(), it seems that I am not getting as good results with 2.2.1 patched as I was with 2.2.0 after I edited the header. Under R 2.2.0 I was able to use over

Dear all, I am using ubuntu linux 32 with 4 Gb.  I am running a very small script and I always got the same error message:  CAN NOT ALLOCATE A VECTOR OF SIZE 231.8 Mb. I have reading carefully the instruction in ?Memory.  Using the function gc() I got very low numbers of memory (please sea below).  I know that it has been posted several times at r-help

Dear all, I am using ubuntu linux 32 with 4 Gb.  I am running a very small script and I always got the same error message:  CAN NOT ALLOCATE A VECTOR OF SIZE 231.8 Mb. I have reading carefully the instruction in ?Memory.  Using the function gc() I got very low numbers of memory (please sea below).  I know that it has been posted several times at r-help

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

Dear all, I am using ubuntu linux 32 with 4 Gb.  I am running a very small script and I always got the same error message:  CAN NOT ALLOCATE A VECTOR OF SIZE 231.8 Mb. I have reading carefully the instruction in ?Memory.  Using the function gc() I got very low numbers of memory (please sea below).  I know that it has been posted several times at r-help

Hello, After experiencing some difficulties with large arrays, I was surprised to see the apparent need for class to gc() after creating fairly large arrays. For example, calling a<-array(2, dim=c(10,10,10,10,10,100)) makes the memory usage of a fresh session of R jump from 13.8 Mb to 166.4 Mb. A call to gc() brought it down to 90.8 Mb, > gc() used (Mb) gc trigger (Mb)

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

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

Hi, I am not sure if this is a bug and I apologize if it is something I didn't read carefully in the R extension manual. My initial search on the R help and R devel list archive didn't find useful information. I am using .Call (as written in the R extension manual) for the C code and have found that the .Call didn't release the memory claimed by allocVector. Even after applying

Hi, I am not sure if this is a bug and I apologize if it is something I didn't read carefully in the R extension manual. My initial search on the R help and R devel list archive didn't find useful information. I am using .Call (as written in the R extension manual) for the C code and have found that the .Call didn't release the memory claimed by allocVector. Even after applying

I haven't quite figured out how I can change the environment of a function. I have a main function and want to use different auxillary functions, which I supply as parameter (or names). What I want to do is something like this: main.fun=function(aux.fun,dat){ x <- 1 fun.dat() } aux.fun.one=function(){ mean(dat)+x } aux.fun.one=function(){ median(dat)-x } I don't want to

Hi, All I have a problem of R memory space. I am getting "Error: cannot allocate vector of size 198.4 Mb" ------------------------------ I've tried with: > memory.limit(size=2047); [1] 2047 > memory.size(max=TRUE); [1] 12.75 > library('RODBC'); > Channel<-odbcConnectAccess('c:/test.MDB'); # inputdata:15 cols, 2000000

Hello, this is probably something silly which I am doing, but I cannot understand why this allocation is not happening. Here is a my C code which tries to allocate a list of size 333559, and then a matrix of size 8*333559 I thought I might be running into memory problems, but R is not even using that much (I start R with more memory and it stays constant) Also, I start R as I normally do and I

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.