similar to: ginv vs. solve

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

?ginv provides 'Modern Applied Statistics with S' (MASS), 3rd, by Venables and Ripley as the sole reference. I happen to have this book (4th ed) on loan from our library, and as far as I can see, ginv is mentioned there twice, and it is *used*, not *explained* in any way. (It is used on p. 148 in the 4th edition.) ginv does not appear in the index of MASS. ginv is an implementation of

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]]

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

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

>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

Hi, I posted a question (bellow) a few weeks ago and had a reply (thanks Christian) that partly solves the problem, but I still would like to be able to restrict some of the independent variables in a nls model to be always >0, (is there a way to do it)?? Thanks, Angel >From: "Christian Ritz" <ritz at dina.kvl.dk> >To: "Angel -" <angel_lul at

### 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

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

Hey, I am modelling a linear regression Y=X*B+E. To compute the effect of ?group? the B-values of the regressors/columns that code the interaction effects (col. 5-8 and col. 11-14, see below) have to be weighted with non-zero elements within the contrast "Group 1" minus "Group 2" (see below). My first understanding was that the interaction effects add up to zero in each group.

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

Which is the best editor for .R files? I currently use kate on my linux as it has R highlighting and allows me to split the window into two: in one I edit the .R file and in the other I have a shell so I run R and can easily copy and paste the code. There are some features that I don't like and I am having a look on some alternatives. I've heard wonders of emacs with ess but I am a

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

Hello R users: I'm trying now to apply the package strucchange to see whether there is a structural change in linear regression. I have noted the following problem that arises in my case with recursive-based CUSUM: generic function recresid() in efp() generates an error, since (probably) it cannot compute the inverse matrix of (X^(i-1)^T)*(X^(i-1)) at each step (i-1), because the matrix

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:

Hi, I am trying to fit a sigmoid curve to some data with nls but I am getting into some trouble. Seems that the optimization method is getting down to some parameter estimates that make the equation unsolvable. This is an example: >growth<-data.frame(Time=c(5,7,9,11,13,15,17,19,21,23,25,27),BodyMass=c(45,85,125,210,300,485,570,700,830,940,1030,1120))

I want to 1) Select for each of the n points in a matrix A, those of the m points in B that lay within a given radius. 2) Of those points within the radius, select the k nearest ones. What I now do is 1) Create an n*m matrix C were I put the distances from all the points in B to the points in A and make NA those cells were the distance is larger than the radius. (The points are geographical

Dear R group, I have to solve a hessian matrix 40*40, called M, in order to obtain the standart deviations of estimators. When I use the function solve(M), I have the following error message: "Error in solve.default(M) : Lapack routine dgesv: system is exactly singular" Do you know an alternative approach which could succeed? I have found some information about the function