similar to: solve() versus ginv()

?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

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

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

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

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]]

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

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

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

Hi R-help, I have a database of 10 students who have written an overall of 78 essays. The challenge? I would like to identify who wrote the 79th essay. Has anybody used R in this context? Even if not, would you suggest me which pattern recognition technique I might possibly apply? Thanks a lot and regards, Tom --------------------------------- [[alternative HTML version

I have a matrix of, say, M and N dimensions: my_matrix=matrix(c(1:60),nrow=6,ncol=10) I have two "id" vectors corresponding to the rows and columns, e.g.: id_m=seq(10,60,by=10) id_n=seq(100,1000,by=100) I would like to create a "proper" database (let's say a data.frame for this example -- i'm going to be loading these into an SQLite database, but we'll leave

Dear All, Is this right? > floor((5.05-floor(5))*100) [1] 4 I would expect 5, or am I wrong? Thanks and regards, W --------------------------------- [[alternative HTML version deleted]]

Hi R-Help, I have just downloaded RSPerl_0.7-0.tar.gz under SuSe. If I type owner@linux: ~work> R CMD INSTALL -c RSPerl_0.7-0.tar.gz i get the following error message makedir: cannot create directory '/user/lib/R/library/00LOCK' : Permission denied ERROR: failed to lock directory '/usr/lib/R/library' for modifing I would really appreciate if somebody can help me

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

Hi R-users I can not change the name of one column only of my matrix. my_matrix <- matrix (1:12,ncol=3) colnames(my_matrix)[1] <- 'myname' Error in dimnames(x) <- dn : length of 'dimnames' [2] not equal to array extent thank you for your help Lorenzo [[alternative HTML version deleted]]

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

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

Hello, I saw this nice trick I want to replicate but I lost the source and I hope one of you can point me to the solution. My problem is that I don't know the correct words to query this. When I print to screen a matrix or data.frame the columns are split and printed below the previous ones; even though I have plenty of screen left. E.g., > my_matrix = matrix(runif(30),nrow=3,ncol=10)