similar to: QR Decompositon and qr.qty

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,

I am having some difficulty understanding the implication of lazy evaluation mixed with DUP=F in a .Fortran call. In qr.qty from base DUP is not used as an argument so defaults to T. I am calling qr.qty with a very large array and would like to set DUP=F in the .Fortran call so that qr.qty would be defined as copied below. Is there some risk that a variable used as the argument in the original

I need to perform a QR-decomposition of a sparse matrix, so I've been trying to use the Matrix package. Unfortunately I don't seem to be getting exactly the same results as if I had used the qr() command from the base package. Here is an example of what I'm doing. > spdata <-rpois(50,1) > y <- rnorm(10,0,1) > S <-

Hi list, I tried to use nls to do some nonlinear least square fitting on my data with 340 observations and 10 variables, but as I called nls() function, I got this error message: Error in qr.qty(QR, resid) : 'qr' and 'y' must have the same number of rows Then I traced back a little bit into nls() function, the error seemed to happen when calling nlsiter() internal function, but

Dear R-experts! I have y(x) data, dim(y) == dim(x) == c(2000, 2) I'd like to fit them with nls: fit.result <- nls ( y ~ f(x, p1, p2, p3), start = list(p1 = ... , p2 = .. , p3 = ..) ) Actually I want to fit y[,1] ~ x[,1] and y[,2] ~ x[,2] *simulaneously*, with the same parameters set {p1, p2, p3}. I tried to feed R tha above formula, R errors with:

Hi, I have a problem with fitting a model: I made a dataframe with this data: a <- 1:3 b <- 1:3 c <- c(3, 2, 3, 2, 1, 2, 3, 2, 3) df <- expand.grid(a,b) df$result <- c names(df) <- c("A","B", "result") Although I can make a graph of the data: require(lattice) wireframe(result~A*B, data=df) I can't get a model to

Hi, I installed R-3.1.1 on AIX-5.3 and my installation hanged up while installing Matrix Package. so i killed the gmake ; gmake install the software nad tired installing Matrix package manually - [ncmr0202][/gpfs1/home/shivali/gang/R-3.1.1/bin/package]> /gpfs1/home/shivali/gang/R-3.1.1/bin/R CMD INSTALL Matrix the package compiled successfully but while loading Matrix package the

Hi all, Regular and avid readers of this column will know that Don Driscoll and I have recently posted two messages requesting assistance concerning an apparent failure of "se.contrast" to produce an se for a contrast. So far, an ominous silence rings in our ears, but read on Gentle Reader, and see if even the machinations of "debug" doesn't stimulate you to respond with a

The man page for se.contrast, when describing the contrast.obj parameter, states that "Multiple contrasts should be specified by a matrix as returned by contrasts." When doing an unbalanced single factor ANOVA, using a contrast.obj as returned by contrasts results in the following error from qr.qty when se.contrast is called: Error in qr.qty(object$qr, contrast) : qr and y must have

On Thu, 19 Apr 2007, brech at delphioutpost.com wrote: > Full_Name: Christian Brechbuehler > Version: 2.4.1 Patched (2007-03-25 r40917) > OS: Linux 2.6.15-27-adm64-xeon; Ubuntu 6.06.1 LTS > Submission from: (NULL) (24.61.47.236) > > > Splus and R have different ideas about what qr.coef(qr()) should return, > which is fine... but I believe that R has a bug in that it is not

Hi, Please help. Regards, Shivali ---------------------------- Original Message ---------------------------- Subject: Re: [Rd] AIX-5.3 Issue installing Matrix Package From: shivali at mail.ncmrwf.gov.in Date: Wed, October 8, 2014 3:31 pm To: "Ei-ji Nakama" <nakama at ki.rim.or.jp> Cc: "Martin Maechler" <maechler at stat.math.ethz.ch>

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:

Hi all, I wonder if this kind of questions are ok in this list... Quick question: What does it mean than the rank of the QR decomposition of a NxN matrix is N-1 ? m: NxN matrix qr(m)$rank equal to (N-1) Long version: I'm doing a manova on a matrix of 10 variables and 16 observations. > dim(tmp) [1] 16 10 > fit <- manova( tmp ~ treatment*mouse ) >results <-

Hi, I have a least square fitting problem with linear inequality constraints. pcls seems capable of solving it so I tried it, unfortunately, it is stuck with the following error: > M <- list() > M$y = Dmat[,1] > M$X = Cmat > M$Ain = as.matrix(Amat) > M$bin = rep(0, dim(Amat)[1]) > M$p=qr.solve(as.matrix(Cmat), Dmat[,1]) > M$w = rep(1, length(M$y)) > M$C = matrix(0,0,0)

I just detected, that det() is not working on complex matrices any more, due to the fix to the bug reports noted above. I am not happy with this, as determinants are perfectly usable on complex matrices. AFAIUI the bugs resulted from less than optimal behaviour of qr() in certain cases. IMHO this is due to the unhappy decision to use a default for parameter tol to decide whether the 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

Hi, I have noticed different rank values calculated by qr() depending on LAPACK parameter. When it is FALSE (default) a true rank is estimated and returned. Unfortunately, when LAPACK is set to TRUE, the min(nrow(A), ncol(A)) is returned which is only occasionally a true rank. Would not it be more consistent to replace the rank in the latter case by something based on the following pseudo code ?

> Date: Mon, 1 May 2000 16:25:11 +0200 (CEST) > From: gb <gb at stat.umu.se> > > On 1 May 2000, Douglas Bates wrote: > > gb <gb at stat.umu.se> writes: > > > > > How about 'Ainv <- qr.solve(A)'? > > > > > > I happened to read the help page for 'qr.solve' the other day, and there I > > > found that

Le 23/01/2018 ? 08:47, Martin Maechler a ?crit?: >>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>>> on Mon, 22 Jan 2018 17:57:47 +0100 writes: > > 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

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