similar to: reference for ginv

Dear All, While inverting a matrix the following error appears on my console: Error in solve.default(my_matrix) : Lapack routine dgesv: system is exactly singular With this respect, I have been replacing the solve() function with ginv(): the Moore-Penrose generalized inverse of a matrix. These are the questions I would like to ask you: 1. Would you also replace solve() with ginv() in

Dear all; I'm kind of confused with the results obtained using the ginv function from package MASS and pinv function from Matlab. Accroding to the documentation both functions performs a Moore-Penrose generalized inverse of a matrix X. The problem is when I change the tolerance value, say to 1E-3. Here is some output from ginv 195.2674402 235.6758714 335.0830253 8.977515484 -291.7798965

I am using the ginv function from MASS and have run across this problem that I do not understand. If I define the matrix A as below, its g-inverse does not satisfy the Moore-Penrose condition A %*% ginv(A) %*% A = A. The matrix A is X'WX in a quadratic regression using some very large dollar values. The much simpler matrix B does satisfy the MP condition. Am I doing something wrong? Is

>I'm rusty, but not *that* rusty here, I hope. > >If W (=Z*Z' in your case) is singular, it can not have >inverse, which by >definition also mean that nothing multiply by it will >produce the identity >matrix (for otherwise it would have an inverse and >thus nonsingular). > >The definition of a generalized inverse is something >like: If A is a >non-null

I'm implementing a function to compute the moore-penrose inverse, using a code from the article: Fast Computation of Moore-Penrose Inverse Matrices. Neural Information Processing - Letters and Reviews. Vol.8, No.2, August 2005 However, the R presents an error message when I use the geninv. The odd thing is that the error occurs for some arrays, however they have the same size. And the R

Thorsten is right. There is a direct formula for computing the Moore-Penrose inverse using the singular value composition of a matrix. This is incorporated in the following: mpinv <- function(A, eps = 1e-13) { s <- svd(A) e <- s$d e[e > eps] <- 1/e[e > eps] return(s$v %*% diag(e) %*% t(s$u)) } Hope it helps. Dietrich

Is there a function in R to calculate the generalized determinant of a singular matrix? - similar to the ginv() used to compute the generalized inverse. I can't seem to find any R related posts at all. Thanks in advance, Sean O'Riordain Trinity College Dublin -- View this message in context: http://r.789695.n4.nabble.com/Moore-Penrose-Generalized-determinant-tp4471629p4471629.html Sent

I cannot find what is the function label for matrix inversion in R. I have found 'ginv' for the moore-penrose in the MASS package, but there is probably a simple inversion operator in the base package. Where can I find it? ____________________________________________ Yvonnick Noel, PhD. University of Lille 3 Department of Psychology F-59653 Villeneuve d'Ascq Cedex (+33) 320 41 63 48

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

Why do x<-b%*%ginv(A) and x<-solve(A,b) give different results?. It seems that I am missing some basic feature of matrix indexing. e.g.: A<-matrix(c(0,-4,4,0),nrow=2,ncol=2) b<-c(-16,0) x<-b%*%ginv(A);x x<-solve(A,b);x Thanks in advance, Angel

since ginv can deal with both singular and non-singular conditions, is there any other difference between them? if I use ginv only, will be any problem? thanks [[alternative HTML version deleted]]

Speaking of optimization and speeding up R calculations... I mentioned last week I want to speed up calculation of generalized inverses. On Debian Wheezy with R-2.15.2, I see a huge speedup using a souped up generalized inverse algorithm published by V. N. Katsikis, D. Pappas, Fast computing of theMoore-Penrose inverse matrix, Electronic Journal of Linear Algebra, 17(2008), 637-650. I was so

Generalised Inverse: The Moore-Penrose Generalisied Inverse is probably better defined as a pseudo-Inverse that arises in solving least squares problems. Another well known pseudo-Inverse is the so-called Drazin pseudo-Inverse. If memory serves (and it's been 10-12 years!) it can be obtained via a diagonalisation. Anyway, I dare say Prof. Ripley (among others) probably has "all the

Is there any routine to obtain a g-inverse of a matrix in R or S-PLUS? Tapio Nummi University of Tampere Finland -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

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:

I'm rusty, but not *that* rusty here, I hope. If W (=Z*Z' in your case) is singular, it can not have inverse, which by definition also mean that nothing multiply by it will produce the identity matrix (for otherwise it would have an inverse and thus nonsingular). The definition of a generalized inverse is something like: If A is a non-null matrix, and G satisfy AGA = A, then G is called

I have a simple system of linear equations to solve for X, aX=b: > a [,1] [,2] [,3] [,4] [1,] 1 2 1 1 [2,] 3 0 0 4 [3,] 1 -4 -2 -2 [4,] 0 0 0 0 > b [,1] [1,] 0 [2,] 2 [3,] 2 [4,] 0 (This is ex Ch1, 2.2 of Artin, Algebra). So, 3 eqs in 4 unknowns. One can easily use row-reductions to find a homogeneous solution(b=0) of: X_1

### An numeric problem in R ######## ###I have two matrix one is########## A <- matrix(c(21.97844, 250.1960, 2752.033, 29675.88, 316318.4, 3349550, 35336827, 24.89267, 261.4211, 2691.009, 27796.02, 288738.7, 3011839, 31498784, 21.80384, 232.3765, 2460.495, 25992.77, 274001.6, 2883756, 30318645, 39.85801, 392.2341, 3971.349, 40814.22, 423126.2,

Dear friends, I am writing a package that I think may be of interest to many people so I am in the process to build-check-write-thedocumentation for it. I have some questions regarding the "rules" that a package should abide in order to be consistent with the other packages on CRAN. I have read and reread the Writing R extension manual and googled the mailing list and I have found

> Date: Wed, 30 Jun 1999 11:12:24 +0200 (MET DST) > From: Torsten Hothorn <hothorn at amadeus.statistik.uni-dortmund.de> > > yesterday I had a little shock using qr (or lm). having a matrix > > X <- cbind(1,diag(3)) > y <- 1:3 > > the qr.coef returns one NA (because X is singular). So I computed the > Moore-Penrose inverse of X (just from the