similar to: svd and eigen

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

Dear R-helpers, could anybody explain me briefly what is the difference between eigenvectors returned by 'eigen' and 'svd' functions and how they are related? Thanks in advance Ondrej Mikula

At 01:37 PM 5/10/00 +0200, ralle wrote: >Hi, >I have a problem understanding what is going on with eigen() for >nonsymmetric matrices. >Example: >h<-rnorm(6) >> dim(h)<-c(2,3) >> c<-rnorm(6) "c" is not a great choice of identifier! >> dim(c)<-c(3,2) >> Pi<-h %*% c >> eigen(Pi)$values >[1] 1.56216542 0.07147773 These could

Hello list, (PC Windows 2000, R 1.2.3) i want to compile C source file for R on windows 2000. I'm working with Metrowerks CodeWarrior for creating and compiling C files. I've see that R.lib file must be obtain with R.ddl by Visual C++. I don't have Visual C++ (and i don't want to buy it !!) but i want to obtain R.lib file. Is there a solution to my problem ??? Any help would

Hello, i' m looking for a function to compute geary statistic for spatial correlation. Thanks. -- St?phane DRAY --------------------------------------------------------------- Biom?trie et Biologie ?volutive - Equipe "?cologie Statistique" Universite Lyon 1 - Bat 711 - 69622 Villeurbanne CEDEX - France Tel : 04 72 43 27 56 Fax : 04 78 89 27 19 04 72 43 27 57 E-mail

Hello list, i have a glm with family=binomial, link=logit but there is over-dispersion. So, in order to take into account for this problem i choose to do a glm with family=quasibinomial(). I'm not an expert on this subject and i ask if someone could validate my approach (i'm not sure for the tests) : quasi_glm(myformula,quasibinomial(),start=mystart) summary(quasi) # test t for

Hello list, my question is related to svd of a matrix: b=matrix(rnorm(50),10,5) mysvd=svd(b) I would like to compute each xi where xi = di* ui %*% t(vi). I do it by : xlist=sapply(1:ncol(b), function(x1,y) y$d[x1]*y$u[,x1]%*%t(y$v[,x1]),y=mysvd,simplify=F) # result is a list xarray=array(sapply(1:ncol(b), function(x1,y) y$d[x1]*y$u[,x1]%*%t(y$v[,x1]),y=mysvd),c(nrow(b),ncol(b),ncol(b))) #

Hello, I have a question that is not directly related to R ... but I try to do it in R ;-) : I would like to generate a matrix Q satisfying (for a given Z, X and W) the two following conditions: t(Q)%*%Q=Z (1) XQ=W (2) where: Q is m rows and r columns X is p rows and m columns D is p rows and r columns C is r rows and r columns with m>p,r e.g: m=6, p=2 r=3

Hello List, I would like to orthonormalize vectors contained in a matrix X taking into account row weights (matrix diagonal D). ie, I want to obtain Z=XA with t(Z)%*%D%*%Z=diag(1) I can do the Gram-Schmidt orthogonalization with subsequent weighted regressions. I know that in the case of uniform weights, qr can do the trick. I wonder if there is a way to do it in the case of non uniform

Hello, I am trying to play with RODBC library and Excel Files. In my file (doubs.xls) there are 2 spreadsheets: > library(RODBC) > connection<-odbcConnect("Excel Files") > sqlTables(connection) TABLE_CAT TABLE_SCHEM TABLE_NAME TABLE_TYPE REMARKS 1 F:\\Th?se\\R\\Doubs NA Faune$ SYSTEM TABLE NA 2 F:\\Th?se\\R\\Doubs NA Milieu$

(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]

We had been using R1.2.3 "print tip lowess function" as described by Terry Speed's group to normalize microarray data and up until this week the commands we were using were working fine. First, we load our data using arrayname<-read.table("filename", header=T) and then check that the table is OK. We format the data list using setup1<-init.data() and usually get a

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 <-

--=====================_24736660==_ Content-Type: text/plain; charset="iso-8859-1"; format=flowed Content-Transfer-Encoding: quoted-printable SVD-Error on R 1.1.0 Windows 98 I get the following error applying svd on a positive definite matrix : > sk2 [,1] [,2] [,3] [,4] [,5] [1,] 1.0460139783 0.084356992 -2.810553e-04

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

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

Hi, I met a probem recently and need your help. I would really appreciate it. I kept receiving the following error message when running a program: 'Error in svd(X) : infinite or missing values in x'. However, I did not use any svd function in this program though I did include the function pseudoinverse. Is the problem caused by doing pseudoinverse? Best regards, Tongtong

Dear R list users, the singluar value decomposition of a symmetric matrix M is UDV^(T), where U = V. La.svd(M) gives as output three elements: the diagonal of D and the two orthogonal matrices u and vt (which is already the transpose of v). I noticed that the transpose of vt is not exactly u. Why is that? thank you for your attention and your help Stefano AVVISO IMPORTANTE: Questo messaggio di

Dear R-devel, I had attempted to compile r-devel (dated Oct. 31, 2001) on WinNT with link to ATLAS, with mostly success. However, when I tried the following, I got a visit from Dr. Watson: R : Copyright 2001, The R Development Core Team Version 1.4.0 Under development (unstable) (2001-10-31) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under

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