similar to: using SVD to get an inverse matrix of covariance matrix

Hi,I want to generate a dataset, which have more than one clusters (say 2) and the elements in each cluster have high correlation (say 0.85) andelements among different clusters have low (say 0.1) or zero correlation.The correlation structure of final dataset should have a block-diagonal structure, that likes 0.85 0.85 .. 0.85 0 0 0 0 ...0 0 0...0 0 0

Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 ---

(I tried to send this earlier, but it doesnt seem to have come through, due to problems on my system) Hola: Both cannot be correct: > m <- matrix(1:4, 2) > svd(m) $d [1] 5.4649857 0.3659662 $u [,1] [,2] [1,] -0.5760484 -0.8174156 [2,] -0.8174156 0.5760484 $v [,1] [,2] [1,] -0.4045536 0.9145143 [2,] -0.9145143 -0.4045536 > La.svd(m) $d [1]

Hi everyone. I was running correspondence analysis in R with the package 'ca' and I got a error message that I could not solve. > summary(ca(gui)) Error in svd(S) : infinite or missing values in 'x' #where gui is my dat table as follows: 0 90 1 1 0 90 0 0 0 90 5 1 0 95 1 0 0 0 0 0 85 0 0 0 50 0 0 0 90 0 0 0 75 0 0 0 75 0 0 0 0 0 10 25 0 0 5 5 0 0 50 10 0 0 10 20 0 0 1 10 0 0

I have just installed R 2.4.0 (Windows XP OS) and have tried to run code that had worked with previous versions of R. However, I now get an error message: Error in La.svd(iCmat, method = "dgesvd") : unused argument(s) (method = "dgesvd") The R NEWS page about the release and issue 6/4 of the newsletter states: La.svd(method = "dgesvd") is defunct. As a

Dear R helpers, I am working with R 2.4.1 GUI 1.18 (4038) for MacOSX. I have a matrix of 10 000 genes and try to run the following commands: > model.mix<-makeModel (data=data, formula=~Dye+Array+Sample+Time, random=~Array+Sample) > anova.mix<-fitmaanova (data, model.mix) > test.mix<-matest (data, model=model.mix, term="Time", n.perm=100, test.method=c(1,0,1,1))

Hi all, A good new year for everybody. Could somebody help me on a question? The Singular Value Decomposition of a matrix A gives A = U * D * t(V) I A is a M X N matrix, U is the left singular matrix (M X N), D is a diagonal singular values matrix (N X N) and V is the transpose right singular ortogonal matrix (N X N). By taking the first l columns of V, with gives a (l X l) matrix, i know

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:

Dear All, Is it possible to manage a svd analysis within a matrix containing NA values. If not how do I could overcome this problem. Thanks in advance Antonio

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

Hi, I need to computed the GSVD of two matrices. For doing so I used in my C code the lapack routine dggsvd. But when I source my file gsvd.R I have the following error: Error in eval.with.vis(expr, envir, enclos) : BLAS/LAPACK routine 'DGGSVD' gave error code -1 Is there a problem with the parameters passed through the gsvd.R script? Is there a problem within the C script?

Hi, Im currently dealing with large datasets of some climatic parameters and I'm performing some EOF analysis on them. The problem is that for one of the datasets, the continents are labelled as NA's (since the data was gathered over the oceans). I don't know to which extent the dropping of those NA's from the matrix will affect the future calculus, and how to maintain the

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.

Dear R-developers I'm not very sure whether this is really a bug and not a feature: > is.matrix(svd(matrix(1:12,nrow=1))$v) [1] FALSE In all other cases the $v component is a matrix. Also, the $u component always seems to be a matrix as indicated in the doc. My R-version: > version _ platform i686-pc-linux-gnu arch i686 os linux-gnu

Generator Microsoft Word 11 (filtered medium) Hi, I'm the mannova package maintainer. We used La.svd(X, method="dgesvd") in maanova package before. After R-2.3.0, the old La.svd() method was deprecated for option method="dgesvd". I changed maanova code correspondingly, which will call method="dgesdd" instead. But after that, we keep getting below error message

Dear R Developers: Why is it that the singular value decomposition is used when running regression with arima, please? I've been looking for a reference for that but have come up empty so far. Thank you for any help. Sincerely, Erin Erin M. Hodgess, PhD Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: hodgesse@uhd.edu

Dear expeRts, I have run some simulations under R 2.15.1 on a Mac, and I have rerun a sample of them under R 3.0.1 on Windows (and also for comparison under R2.14.1 on Windows). For most cases, I get exactly the same results in all three runs. However, for those cases that depend on principal components computed with prcomp, where the particular choice of the orthogonalization is arbitrary

Dear expeRts, I have run some simulations under R 2.15.1 on a Mac, and I have rerun a sample of them under R 3.0.1 on Windows (and also for comparison under R2.14.1 on Windows). For most cases, I get exactly the same results in all three runs. However, for those cases that depend on principal components computed with prcomp, where the particular choice of the orthogonalization is arbitrary

Is there a way to determine the SVD of a complex matrix using R? (I'm using v1.0.1 and svd() won't do the trick). I know LAPACK has a function to do this. Thanks -- Ben Stapley Biomolecular Modelling Lab Imperial Cancer Research -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Dear R-users, I computed determinant of a square matrix "var.r" using the SVD output: detr _ 1 d _ svd(var.r)$d for (i in 1:length(d)) { detr _ detr*d[i] } print(detr) 30.20886 BUT when I tried : det(var.r) I got : -30.20886 Is this because SVD output will only give absolute of the eigenvalues ?, If this is the case how can I get the original eigenvalues? Thanks, Agus