search for: penrose

...e 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 from the R help mailing list archive at Nabble.com.

...t; 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 definition) and I get a correct > result. Whats wrong about qr in this situation? What is a correct result, by the way? There are infinitely many solutions for the regression of y on X, and the Moore-Penrose one is just one choice (that assumes that the coefficients...

Hi all, We''ve installed RoR on Solaris 9, and are attempting to install Mongrel using the following command:gem install mongrelgetting error "SSL is not installed on this system".All other gems (including Rake) installed fine.Any suggestions?Thanks,Brian _________________________________________________________________ Boo!?Scare away worms, viruses and so much more! Try

...erence. 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 the Moore-Penrose generalized inverse, but I can't find any reference to an appropriate publication from Moore and Penrose in MASS either. Admittedly, MASS is inadequate as a reference for ginv, and it is desirable that the reference be corrected. Is this an isolated incident, or a general practice? (Perhaps...

..."doTryCatch" 13.94 2.56 2.72 0.50 ".deparseOpts" 12.42 2.28 7.92 1.46 I *Think* this means that a bottlleneck here is svd, which is being called by this function that calculates generalized inverses: ## Moore-Penrose Inverse function (aka Generalized Inverse) ## X: symmetric matrix ## tol: convergence requirement mpinv <- function(X, tol = sqrt(.Machine$double.eps)) { s <- svd(X) e <- s$d e[e > tol] <- 1/e[e > tol] s$v %*% diag(e,nrow=length(e)) %*% t(s$u) } That is from the A...

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...

I just moved my app from development to test, and now getting the following error on my form: undefined method ''find'' for Status:Class The only difference between the two environments is that test is using Mongrel and we were running WEBrick in dev. We are on Rails 1.2.5 . Any suggestions? Thanks, Brian -- Posted via http://www.ruby-forum.com/.

...t;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 a generalized >inverse of A. This is not unique, but a unique one >that satisfy some >additional properties is the Moore-Penrose inverse. I >don't know if this is >what ginv() in MASS returns, as I have not used it >before. Andy The inverse of a Matrix A is defined as a Matrix B such that B*A=A*B=I and not just B*A=I. But there are matrices B for singular matrices A such that B*A=I but A*B != I, therefore th...

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 http://www.univ-li...

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 *****************...

...g 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 delighted to see the computation time drop on my Debian system that I boasted to the WIndows users and gave them a test case. They answered back "there's no benefits, plus Windows is faster than Linux"....

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 this due to the large values in A? I am using v.1.8.1 on Windows XP. > A &l...

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 428.8266383 165.9809056 284.0561017 -74.35007018 -210.3373981 440.6537582 42.44715935 290.4538488 -58.7436662...

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 these scenarios and using R? 2. Or is there something I should take care by using ginv() you would suggest me please? Many thanks in advance. Best regards, W...

...rning that the LLVM web site and CVS server are likely to be affected by the following maintenance work on May 21. Many of our software utilities are automounted from DCSfiles. --Vikram http://www.cs.uiuc.edu/~vadve http://llvm.cs.uiuc.edu/ Begin forwarded message: > From: "Frank E Penrose" <fep at uiuc.edu> > Date: May 9, 2006 9:01:38 AM CDT > To: <tsg-announce at cs.uiuc.edu> > Subject: [TSG-Announce] major CS department IT services outage on > May 21 starting at 9pm > > TSG will be performing a firmware upgrade and other maintenance > work...

Hello, I have a matrix equation, Ax=b, that I need to solve for x. x should be a vector of positive numbers (between 0 and 1). A is not a square matrix in general. This lead me to using the SVD. However, using the SVD gives me positive and negative numbers, as well. I have some constraints included in the A matrix itself (i.e., that the sum of some xi should be equal to 1) but I do not know how

...T^2 test ist computed and also p-values are available (an approximation of the null distribution by Laeuter is used). The summary method "summary.multilm" offers the computation of stabilized multivariate tests: SS, PC-1, PC-q and CS. Unlike "lm" this package uses the Moore-Penrose inverse for least square fitting (see the MP, QR, lm, anova discussion in June). I'm not sure if those basic methods fit most users needs, maybe wrappers for special models (MANOVA) are required. I'm very grateful for any hint and discussion, mails of common interest please to the R-list...

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 sa...

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The lm() function in R seems to handle the inversion of singular X'X matrices (where there is collinearity between regression inputs) in a way where one of the inputs is dropped and this also seems to be the default behavior in SAS (please let me know if i'm wrong about this). In some other packages (i.e. octave ols() function) the pseudo inverse is computed where singular values less