similar to: bigmatrix and irlba

When running irlba on a big.matrix as in the example from the vignette (on Windows R 2.15.2 64 bit) it returns with an error (see below). irlba works fine on a regular R matrix > library(bigalgebra) > library(irlba) Loading required package: Matrix Loading required package: lattice > matmul <- function(A, x, transpose=FALSE) + { + if(transpose) {return(t( t(x) %*% A))}# i.e.,

Dear R-help list members, I am hoping to get you help in reproducing a problem I am having That is only reproducible on a large-memory machine. Whenever I run the following lines, get a segfault listed below: *** caught segfault *** address 0x7f092cc46e40, cause 'invalid permissions' Traceback: 1: La.svd(x, nu, nv) 2: svd(X) 3: ginv(bigmatrix) Here is the code that I run:

Hola como andan? estuve leyendo un poco la documentacion de la libreria BigMemory y no me quedan claro algunas cosas, le planteo mi problema, tengo un matriz en disco que pesa 2gb con coeficientes float´s, solo numeros, y tengo que hallar la descomposicion en valores singulares de esta matriz, basicamente la pregunta seria: como hacer para leer la matriz, calcular su DVS y como escribirla en

This is kind of like asking for a solution to x+1=x+1. Go back to linear algebra and look up Singular Value Decomposition, and decide if you really want to proceed. See also ?svd and package irlba. -- Sent from my phone. Please excuse my brevity. On April 20, 2016 4:22:34 AM PDT, A A via R-help <r-help at r-project.org> wrote: > > > >I have a situation in R where I would like

Dear All, I need to perform a SVD on a very large data matrix, of dimension ~ 500,000 x 1,000 , and I am looking for an efficient algorithm that can perform an approximate (partial) SVD to extract on the order of the top 50 right and left singular vectors. Would be very grateful for any advice on what R-packages are available to perform such a task, what the RAM requirement is, and indeed what

Hello I'm a population ecologist and use R for all my stats and modelling. Recently I have been using R to numerically solve integral projection models. This involves constructing several large matrices. The current code by Easterling (Size-specific sensitivity: Applying a new structured population model. Ecology, 2000, 81, 694-708) uses nested loops to construct the matrices. To speed up the

Dear all, bigKRLS, which has been on CRAN for a couple of years, had to be pulled recently due to what seems to be a sanitizer issue stemming from its use of bigmemory. bigKRLS works fine (we?ve used it ourselves on many different platforms and have had over 15,000 downloads without an end user reporting difficulties because of this issue). Unfortunately, we have been unable to reproduce the

Hi all, I have a vector that contains the row numbers of data taken from several filtering operations performed on a large data frame (20,000rows x 500cols). In order to output this subset of data, I've been looping through the vector containing the row numbers (keepRows). output <- data.frame(row.names = rownames(bigMatrix)) for(i in keepRows) { output <- rbind(output,

Hello! I suppose this is more a matrix theory question than a question on R, but I will give it a try... I am using La.svd to compute the singular value decomposition (SVD) of a variance matrix, i.e., a symmetric nonnegative definite square matrix. Let S be my variance matrix, and S = U D V' be its SVD. In my numerical experiments I always got U = V. Is this necessarily the case? Or I might

Hi, I have read several threads about memory issues in R and I can't seem to find a solution to my problem. I am running a sort of LASSO regression on several subsets of a big dataset. For some subsets it works well, and for some bigger subsets it does not work, with errors of type "cannot allocate vector of size 1.6Gb". The error occurs at this line of the code: example <-

Evaluating session variables inside a markaby paragraph tag always returns false. For example welcome.mab -- p "Good morning Mr. #{session[:user]}." -- displays: Good mornig Mr. How can I access session variables in maraby? -- Best Regards, -Larry "Work, work, work...there is no satisfactory alternative." --- E.Taft Benson -------------- next part

I have a large file backed big. matrix, with millions of rows and 20 columns. The columns contain data that I simply need to tabulate. There are a few dozen unique values. and I just want a frequency count Test code with a small "big" matrix. library(bigmemory) library(bigtabulate) test <- big.matrix(nrow = 100, ncol = 10) test[,1:3]<- sample(150) test[,4:6]<-

I'm using samba Version 3.0.24-1.fc5 with cups 1.2.8. I've 23 printers installed and my clients are w2k and WinXP. This mornig i got in logs: [2007/05/23 10:35:16, 0] rpc_server/srv_lsa_hnd.c:create_policy_hnd(111) create_policy_hnd: ERROR: too many handles (1025) on this pipe. After restart the samba service it works fine. # ulimit -n 1024 i don't find solution in samba

Dear Philipp, this is just a tentative answer because debugging is really not possible without a reproducible example (or, at a very bare minimum, the output from traceback()). Anyway, thank you for reporting this interesting numerical issue; I'll try to replicate some similar behaviour on a similarly dimensioned artificial dataset when I have some time (which might not be soon). As for now,

Dear R-devel readers: I am really stuck trying resolving an issue with the use of the Matrix in one of my packages, irlba, with R-2.14.0. When I use crossprod with at least one sparse argument in the packaged code I receive the error: Error in crossprod(x, y) : requires numeric/complex matrix/vector arguments However, when I run the code outside of the context of the package it works fine.

Lately I'm getting this error quite a bit: Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd' I'm running R 2.5.0 on a 64 bit Intel machine running Fedora (8 I think). Maybe the 64 bit platform is more fragile about declaring convergence. I'm seeing way more of these errors than I ever have before. From R-Help I see that this issue comes up from time to

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

In summary.manova the qr decomposition of a NxN matrix is calculated and for some cases is giving me a rank < N. However, following suggestions of professor Ripley to calculate the rank of a Matrix On 7 Jun 2002, Brian Ripley wrote: > For a more reliable answer, look at the SVD > (function svd) and look at the > singular values. For example (from lda.default) X.s <-

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi all, this is probably simple and I'm just doing something stupid, sorry about that :-) I'm trying to convert words (strings of letters) into a fairly small dimensional space (say 10, but anything between about 5 and 50 would be ok), which I will call a feature vector. The the distance between two words represents the similarity of the