similar to: the r package for svdpack?

Hi, I wrote a C extension for R. Within the C code I wanted to invoke the R's sort function, with the argument "index.return = TRUE". I found it is a difficult problem, how can I do that? I have implemented the decreasing sorting by the code "PROTECT(R_fcall = lang3(install("sort"), x, desc));", but how to define the "index.return" argument? Thanks in

I am a relative newcomer to both the R and C/C++ software worlds -- I'm taking a C Programming class currently. I noticed the other day that the C:\Program Files\R1_8_1\src\include\R_ext directory on my WinXP box has the header files BLAS.h Lapack.h Linpack.h RLapack.h I am interested in (perhaps) using one or more of these header files in a straight C program I'm working on in Visual

Hi, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over

I am reading the Mining of Massive Datasets Book by Rajaraman and Ullman. It has a good explanation of Recommendation System at Chapter 9. But what are the relationship between 1) SVD (Singular Decomposition) 2) UV-Decomposition 3) NMF (Non-negative Matrix Factorization) In particular, it seems 2) and 3) can be very similar. Is it right? Thanks. -- View this message in context:

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

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'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <-

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 <-

I am trying to perform Singular Value Decomposition (SVD) on a Term Document Matrix I created using the 'tm' package. Eventually I want to do a Latent Semantic Analysis (LSA). There are 5677 documents with 771 terms (the DTM is 771 x 5677). When I try to do the SVD, it runs out of memory. I am using a 12GB Dual core Machine with Windows XP and don't think I can increase the memory

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-

Hi list, Does anyone know if there is a library in R that does MTM-SVD method for spectral analysis? Thanks ----- Yasir H. Kaheil Columbia University -- View this message in context: http://www.nabble.com/Spectral-analysis-with-mtm-svd-Multi-Taper-Method-Combined-with-Singular-Value-Decomposition-tp21671934p21671934.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have to compute the singular value decomposition of rather large matrices. My test matrix is 10558 by 4255 and it takes about three minutes in R to decompose on a 64bit quadruple core linux machine. (R is running svd in parallel, all four cores are at their maximum load while doing this.) I tried several blas and lapack libraries as well as the gnu scientific library in my C++ programm.

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

Hi, I run a maanova analysis and found this message error: Error in ma.svd(X, 0, 0) : 0 extent dimensions I did a google search and found this: \item ma.svd: function to compute the sigular-value decomposition of a rectangular matrix by using LAPACK routines DEGSVD AND ZGESVD. \item fdr: function to calculate the adjusted P values for FDR control. I did a search for LAPACK and

On two laptops running 32-bit kubuntu, I have found that svd(), invoked within R 2.9.1 as supplied with the current ubuntu package, returns very incorrect results when presented with complex-valued input. One of the laptops is a Dell D620, the other a MacBook Pro. I've also verified the problem on a 32-bit desktop. On these same systems, R compiled from source provides apparently

Hi wizards, I have seen the function svd of R for singular value decomposition, but I need to computes the ``economy size'' or ``thin'' singular value decomposition of a matrix in R. Somebody knows how to do that?. Thanks in advance. -- Web Page http://geocities.com/lord_tyranus_96/

Full_Name: Christian Brechbuehler Version: 2.4.1 Patched (2007-03-25 r40917) OS: Linux 2.6.15-27-adm64-xeon; Ubuntu 6.06.1 LTS Submission from: (NULL) (24.61.47.236) Splus and R have different ideas about what qr.coef(qr()) should return, which is fine... but I believe that R has a bug in that it is not internally consistent, and another separate bug in the documentation. In particular, on

I specifically need rq matrix decomposition (and not qr). Looking at netlib site for LAPACK it does provide rq whereas LINPACK not. Looking at companion qr in R I see how in base it wraps with a .Call but I do not have success in doing that for a similar .Call for rq. Anyone done this or can provide matrix rewrites that allow me to do the rq decomposition with existing R funcs? Regards MJ

Dear all Some computations gave a matrix. The single value decomposition of it worked fine previously, after an system upgrade I get the following error: ----- > La.svd(x,256,256) Error in La.svd(x, nu, nv) : BLAS/LAPACK routine 'DLASCL' gave error code -4 ----- Erroneous Matrix: ftp://usys-ftp.ethz.ch/ITES/STEP/nussbaum/R/matrix-lapack-error4.RData /.csv Operating System: Debian 8

On Thu, 19 Apr 2007, brech at delphioutpost.com wrote: > Full_Name: Christian Brechbuehler > Version: 2.4.1 Patched (2007-03-25 r40917) > OS: Linux 2.6.15-27-adm64-xeon; Ubuntu 6.06.1 LTS > Submission from: (NULL) (24.61.47.236) > > > Splus and R have different ideas about what qr.coef(qr()) should return, > which is fine... but I believe that R has a bug in that it is not