similar to: qr and Moore-Penrose

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

Hi, Why the qr() produces a negative Q compared with Gram-Schmidt? (note example below, except Q[2,3]) Here is an example, I calculate the Q by Gram-Schmidt process and compare the output with qr.Q() a <- c(1,0,1) b <- c(1,0,0) c <- c(2,1,0) x <- matrix(c(a,b,c),3,3) ########################## # Gram-Schmidt ########################## A <- matrix(a,3,1) q1 <-

man for `qr` says that the function uses LINPACK's DQRDC, while it in fact uses DQRDC2. ``` The QR decomposition of the matrix as computed by LINPACK or LAPACK. The components in the returned value correspond directly to the values returned by DQRDC/DGEQP3/ZGEQP3 ```

The following snippet suggests that there is either a bug in qr(,LAPACK=T), or some bug in my understanding. Note that the detected rank is correct (= 2) using the default LINPACK qr, but incorrect (=3) using LAPACK. This is running on Linux Redhat 9.0, using the lapack library that comes with the Redhat distribution. I'm running R 1.7.1 compiled from the source. If the bug is in my

Full_Name: David Pleydell Version: 2.2.1 OS: Debian Etch Submission from: (NULL) (193.55.70.206) There appears to be a conflict between the chol functions from the Matrix and the SparseM packages. chol() can only be applied to a matrix of class dspMatrix if SparseM is not in the path. with gratitude David > library(Matrix) > sm <- as(as(Matrix(diag(5) + 1), "dsyMatrix"),

Might it be possible to tighten the definition of "is.qr". I noticed that after I mistakenly typed example(lm) # make lm object named lm.D9 qr.Q(lm.D9) which exhausted the heap memory and produced two warning messages. As an object of class "lm" has a "qr" component, "is.qr" failed to detect that "lm.D9" was not a "qr" object. The

I am testing 'qr' with an admittedly contrived matrix and I am getting different results than I am from another package. The matrix that I am using is: x <- matrix(seq(.1, by=.1, length.out=12), 4) So the whole test is: x <- matrix(seq(.1, by=.1, length.out=12), 4) qr(x) And the output from 'R' is: $qr [,1] [,2] [,3] [1,] -0.5477226 -1.2780193

I suggest adding a 'pivot' argument to qr.R, to obtain columns in the same order as the original x, so that a <- qr(x) qr.Q(a) %*% qr.R(a, pivot=TRUE) returns x. -------------------------------------------------- # File src/library/base/R/qr.R qr.R <- function(qr, complete = FALSE, pivot = FALSE) { # Args: # qr: a QR decomposition, produced by qr() # complete:

2017-06-22 19:49 GMT+02:00 Uwe Ligges <ligges at statistik.tu-dortmund.de>: > On 22.06.2017 17:11, Bernd Funovits wrote: >> >> Hello, >> >> I experienced some unexpected behaviour while determining the rank of matrices (sometimes 1x1 matrices): >> base::qr(matrix(1e-20))$rank returns 1 (incorrect) >> base::qr(diag(c(1, 1e-20)))$rank returns 2

2017-06-22 20:31 GMT+02:00 Uwe Ligges <ligges at statistik.tu-dortmund.de>: > > > On 22.06.2017 20:09, I?aki ?car wrote: >> >> 2017-06-22 19:49 GMT+02:00 Uwe Ligges <ligges at statistik.tu-dortmund.de>: >>> >>> On 22.06.2017 17:11, Bernd Funovits wrote: >>>> >>>> >>>> Hello, >>>> >>>> I

In the manual page for prcomp(), it says that sdev is "the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix)." ?However, this is not what I'm finding. ?The values appear to be the standard deviations of a reprojection of

On 22.06.2017 20:09, I?aki ?car wrote: > 2017-06-22 19:49 GMT+02:00 Uwe Ligges <ligges at statistik.tu-dortmund.de>: >> On 22.06.2017 17:11, Bernd Funovits wrote: >>> >>> Hello, >>> >>> I experienced some unexpected behaviour while determining the rank of matrices (sometimes 1x1 matrices): >>> base::qr(matrix(1e-20))$rank returns 1

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

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

Le 22/01/2018 ? 17:40, Keith O'Hara a ?crit?: > This behavior is noted in the qr documentation, no? > > rank - the rank of x as computed by the decomposition(*): always full rank in the LAPACK case. For a me a "full rank matrix" is a matrix the rank of which is indeed min(nrow(A), ncol(A)) but here the meaning of "always is full rank" is somewhat confusing. Does it

Here is a modified version of qraux.Rd, an edited version of R-2.14.0/src/library/base/man/qraux.Rd This gives some details and an example for the case of pivoting. In this case, it is not true that X = QR; rather X[, pivot] = QR. It may save some other people bugs and time to have this information. Tim Hesterberg -------------------------------------------------- % File

[I thought I'd submitted this bug report some time ago, but it's never showed up on the bug tracking system, so I'm submitting again.] qr.solve() gives incorrect results when dealing with complex matrices or with qr objects that have been computed with LAPACK=TRUE, whenever the b argument has more than one column. This bug flows from qr.coef(), which has a similar problem. I believe

> sessionInfo() R version 2.13.1 (2011-07-08) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] Matrix_0.999375-50 lattice_0.19-30 loaded via a namespace (and not attached): [1] grid_2.13.1

With this example set.seed(123) A <- matrix(runif(40), nrow = 8) y <- 1:nrow(A) A.laqr <- qr(A, LAPACK=TRUE) both qr.qy(A.laqr,y) and qr.qty(A.laqr,y) give the respective error messages Error in qr.qy(A.laqr, y) : 'b' must be a numeric matrix Error in qr.qty(A.laqr, y) : 'b' must be a numeric matrix However when Lapack is not used as in A.liqr <- qr(A,

Dear maintainers, I'm reporting a bug in qr.coef that mishandles the colnames of matrix. A minimal reproducible example is as follows: x <- cbind(rep(0, 10), rep(0, 10)) y <- rep(0, 10) q <- qr.default(x) qr.coef(q, y) [1] NA NA If x has colnames, then qr.coef will end up with an error: x <- cbind(x1 = rep(0, 10), x2 = rep(0, 10)) y <- rep(0, 10) q <- qr.default(x)