similar to: problem to solve a matrix

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

Hi, I'm trying to use the solve() function in R to invert a matrix. I get the following error, "Lapack routine dgesv: system is exactly singular" However, My matrix doesn't appear to be singular. [,1] [,2] [,3] [,4] [1,] 0.99252358 0.93715047 0.7540535 0.4579895 [2,] 0.01607797 0.09616267 0.2452471 0.3088614 [3,] 0.09772828 0.58451468 1.4907090

Dear all, The background is that I'm trying to fix this bug in the geometry package: https://r-forge.r-project.org/tracker/index.php?func=detail&aid=1993&group_id=1149&atid=4552 Boiled down, the problem is that there exists at least one matrix X for which det(X) != 0 and for which solve(X) fails giving the error "system is computationally singular: reciprocal condition

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

Hi there! I´m a noob when it comes to R and I´m using it to run statisc analysis. With the code for ARIMA below I´m getting this error: Error in solve.default(res$hessian * n.used) :Lapack routine dgesv: system is exactly singular The code is: > s.ts <- ts(x[,7], start = 2004, fre=12) > get.best.arima <- function (x.ts, maxord=c(1,1,1,1,1,1)) + { + best.aic <- 1e8 + n <-

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]]

Dear R-devel, I am trying to use Fortran DGESV subroutine into C. Here it is the relevant part of the C file I am currently writing #include<stdio.h> #include<R.h> #include<Rmath.h> #include<math.h> void F77_NAME(DGESV)( int*, int*, double*, int*, int*, double*, int*, int*); void solve( int *p, double *A, double *Ainv) { ... F77_CALL(DGESV)(p, p, Ain, p, ipiv,

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

?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

### 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,

Hi, Excuse me for asking this silly question. But I really couldn't understand why cov() and ccov() don't work for my calculation of covariance matrix. a <- matrix(1:8, 2, 4) a [,1] [,2] [,3] [,4] [1,] 1 3 5 7 [2,] 2 4 6 8 > ccov(a) Error in solve.default(cov, ...) : Lapack routine dgesv: system is exactly singular I also tried colume bind, but it

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

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

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-listers, I have a dxr matrix Z, where d > r. And the product Z*Z' is a singular square matrix. The problem is how to get the left inverse U of this singular matrix Z*Z', such that U*(Z*Z') = I? Is there any to figure it out using matrix decomposition method? Thanks a lot for your help. Fred

Dear list, I am a little puzzled by computing time in connection with calling C functions. With the function mysolve1 given below I solve Ax=B, where the actual matrix operation takes place in mysolve2. Doing this 5000 times takes 3.51 secs. However, if I move the actual matrix inversion part into mysolve1 (by uncommenting the two commented lines and skip the call to mysolve2) then the

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 am having a problem with the foreign library correctly reading some integer data. Specifically, d _ read.dta('aptaa.dta') > d[1:5,] scenario metcode yr ginv cons gocc abs dvac gmre gmer 1 1 AA 2002 0.007 1377 -0.071 51710 0.071 -0.011 -0.127 2 1 AA 2003 0.000 0 -0.016 62568 0.014 -0.043 -0.538 3 1 AA 2004 0.000 0 -0.002