similar to: Cost of garbage collection seems excessive

Hi, I am using "*Maanova* package" to do anova. I have created *datafile* with probeID as the first column, which is a tab limited text file and also created *designfile*. I have created *readma object* which is named as abf1. >From that readma object, i have to create data object by using *createData*function and also i hav to create model object by using *makemodel* function,

I'm working with snow and created a local cluster. So far, the same code has always worked (please see below). However, now I receive a message that the connection with the nodes cannot be opened. I restarted my workstation but that didn't help. Is there a known solution for this problem? Thanks a lot for any help. bram foubert library(snow) cl =

Hi, I am using "*Maanova* package" to do anova. I have created *datafile* with probeID as the first column, which is a tab limited text file and also created *designfile*. I have created *readma object* which is named as abf1. >From that readma object, i have to create data object by using *createData*function and also i hav to create model object by using *makemodel* function,

Hi All, This is a very important issue. It would be very sad to leave most users unaware of a free speedup of this size. These options don't appear in the R --help output. They really should be added there. Additionally, if the garbage collector is working very hard, might it emit a note about better setting for these variables? It's not really my place to comment on design philosophy,

>>>>> Peter Haverty <haverty.peter at gene.com> >>>>> on Mon, 19 Jan 2015 08:50:08 -0800 writes: > Hi All, This is a very important issue. It would be very > sad to leave most users unaware of a free speedup of this > size. These options don't appear in the R --help > output. They really should be added there. Indeed,

On Thu, Jan 15, 2015 at 3:55 PM, Michael Lawrence <lawrence.michael at gene.com> wrote: > Just wanted to start a discussion on whether R could ship with more > appropriate GC parameters. I've been doing a number of similar measurements, and have come to the same conclusion. R is currently very conservative about memory usage, and this leads to unnecessarily poor performance on

hi, I have a dataset (the netflix dataset) which is basically ~18k columns and well variable number of rows but let's assume 25 thousand for now. The dataset is very sparse. I was wondering how to do kmeans/nearest neighbors or kernel density estimation on it. I tired using the spMatrix function in "Matrix" package. I think I'm able to create the matrix but as soon as I pass

Dear R people, I wonder if some kind person can tell me the correct syntax to set the R_VSIZE environmental variable. I tried R_VSIZE = 10M in ~/.Renviron and also export R_VSIZE = 10M. These don't seem to work. I scrounged around looking for details about this, couldn't find any, got fed up. Faheem.

Dear useRs, First off, sorry about the long post. Figured it's better to give context to get good answers (I hope!). Some time ago I wrote an R function that will get all pairwise interactions of variables in a data frame. This worked fine at the time, but now a colleague would like me to do this with a much larger dataset. They don't know how many variables they are going to have in the

Hi all, I'm having a very minor problem. Most every time I use the pager (I think) I get the following error about character 32. It works fine, but the error is a bit annoying. > ?version sh: invalid character 32 in exportstr for export R_NSIZE sh: invalid character 32 in exportstr for export R_VSIZE R.Version package:base R Documentation (and then the

Full_Name: John Brzustowski Version: R-devel trunk and R-2.4.0 OS: linux Submission from: (NULL) (76.10.152.79) Here are two minor potential issues in pointer protection stack (PPS) code which could arise in debugging R with valgrind. Only the second could possibly cause a problem in normal use of R, and only under laughably implausible circumstances. (1) valgrind is given a wrong address when

For some of my packages I am getting a segmentation fault in Linux when I use R CMD check. (Using R 1.0.1) The segmentation fault does not happen in Solaris and in some cases it does not happen in Linux if I set R_NSIZE and R_VSIZE much higher than I need in Solaris. Should I expect a segmentation fault if there is not enough memory for R CMD check, or are these unrelated? Paul Gilbert

Hi, I am trying to run an analysis with the package maanova and I am not getting success. I suppose that I am wrong on set up the formula, so the issue may not be related to R, properly. I have two varieties of plants (V1 and V2). A group of each ones were treated and another was not treated. After treatment, in three different time RNA was collected from treated and from not treated plants for

Hello. I'm still working on my OCaml-R binding and I get a segfault in the GetNewPage() function of memory.c. For the record, the OCaml-R binding seems to work fine with OCaml bytecode. The segfault here is the main issue I have with OCaml native code. OCaml-R can be found on the following links. Source code: http://yziquel.homelinux.org/gitweb/?p=ocaml-r.git;a=summary

[RFC = Request for Comments] { Yes, the documentation for .Renviron is really not there (but the FAQ...); the rest is in ?Startup } In R's Startup (on Unix only??) {unless --no-environ is specified} ~/.Renviron (if there) is read as an 'sh' script before R is called, then R looks ((for the site-wide Rprofile and then)) for .Rprofile in the current directory and then for

Dear R users we're having a problem reading a largish data file using read.table(). The file consists of 175000 lines of 4 floating pt numbers. Here's what happens: > dat_read.table('sst.dat') Error: memory exhausted (This is line 358 of src/main/memory.c). Cutting down the file to around 15000 lines allows read.table() to work OK. I edited the memory limits in Platform.h

A pre-release of R-0.64.1 for Windows is available at CRAN/bin/windows/windows-NT/pre-0.64.1. Since there are a lot of Windows specific changes (see below for a list), we hope that many Windows users of R will test this version and make comments. REMARK: I am posting this message on r-help hoping to reach a larger number of Windows users. But, please, replay or directly to Brian D. Ripley

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and

The Rprofmem facility is currently enabled only if the configuration option --enable-memory-profiling is used. However, the overhead of having it enabled is negligible when profiling is not actually being done, and can easily be made even smaller. So I think it ought to be enabled all the time. I've attached a patch doing this, which also makes a number of other improvements to Rprofmem,

Just wanted to start a discussion on whether R could ship with more appropriate GC parameters. Right now, loading the recommended package Matrix leads to: > library(Matrix) > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 1076796 57.6 1368491 73.1 1198505 64.1 Vcells 1671329 12.8 2685683 20.5 1932418 14.8 Results may vary, but here R needed 64MB of N cells and 15MB