similar to: glm.fit to use LAPACK instead of LINPACK

Hello. I am trying to invert a matrix, and I am finding that I can get different answers depending on whether I set LAPACK true or false using "qr". I had understood that LAPACK is, in general more robust and faster than LINPACK, so I am confused as to why I am getting what seems to be invalid answers. The matrix is ostensibly the Hessian for a function I am optimizing. I want to get

Dear R Developers, I should have had the Design library running in R by now but have kept putting off changing some calls to LINPACK routines to use those builtin to R. Specifically I call dqrsl1 and dqr. Would it be an easy task to put those in the next release of R? If not I'll finally bite the bullet and get back into reading LINPACK documentation (which I have but haven't examined

Ordinary functions should not alter their inputs but in R-2.15.2 svd(LINPACK=TRUE,X) does. (It worked in 2.15.0 but not in 2.15.1 or 2.15.2 and became deprecated in 2.15.2.) > X <- matrix(c(1,2,3, 5,7,11, 13,17,19), 3, 3) > X [,1] [,2] [,3] [1,] 1 5 13 [2,] 2 7 17 [3,] 3 11 19 > svd(X, LINPACK=TRUE)$d [1] 31.9718214 2.3882717 0.3143114 Warning message:

Hi all, I don't know if this is the correct venue for this question, but I am sure that someone will correct me if I am in the wrong list. I have been searching throughout R for a function that can find the intersection of multiple sets of "things". Say for instance, I have a list of $n$ character vectors and would like to find the intersection of all $k$ subsets. I don't

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

I want to speed up computations (involving matrices) by writing some C-code to be loaded. In the C-code, I need to invert matrices etc. As I've understood the "writing R extensions" doc, I can use use #include <R_ext/Linpack.h> in my .c-file and get access to linpack's facilities within my C-code. Is that correct? If so, can anyone point me to examples on how this is

In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); if (*info == 0){ F77_CALL(dpodi)(*hessian, &bdim, &bdim, det, &job); ........ This usually works OK, but with an ill-conditioned data

Hi all, I am wondering if this is a bug in the Matrix package or if it something that I am just getting wrong...here is an example: > m = matrix(0,4,4) > dimnames(m) = list(letters[1:4], letters[5:8]) > r = c("a","b","a","d") > m[r,2] = 1 > m e f g h a 0 1 0 0 b 0 1 0 0 c 0 0 0 0 d 0 1 0 0 > M = Matrix(0,4,4) > dimnames(M) =

> On 10 Feb 2017, at 14:53, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > Thanks to all who answered my third question. I learned something, but: > > On 2017-02-09 17:44, Martin Maechler wrote: >> >>>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>>> >>>> In my package 'glmmML'

> > On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > > if (*info == 0){ > >

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: Ravi Varadhan Version: 2.8.1 OS: Windows Submission from: (NULL) (162.129.251.19) Inverting a matrix with solve(), but using LAPACK=TRUE, gives erroneous results: Here is an example: hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") } h5 <- hilbert(5) hinv1 <- solve(qr(h5)) hinv2 <- solve(qr(h5, LAPACK=TRUE)) all.equal(hinv1, hinv2) #

I am a relative newcomer to both the R and C/C++ software worlds -- I'm taking a C Programming class currently. I noticed the other day that the C:\Program Files\R1_8_1\src\include\R_ext directory on my WinXP box has the header files BLAS.h Lapack.h Linpack.h RLapack.h I am interested in (perhaps) using one or more of these header files in a straight C program I'm working on in Visual

r-devel list, I have recently written an R package that solves a linear least squares problem, and computes the multivariate normal density function. The bulk of the code is written in C, with interfacing code to the BLAS and Lapack libraries. The motivation here is speed. I ran into a problem computing the determinant of a symmetric matrix in packed storage. Apparently, there are no explicit

Hello, I was building a R addin for Excel. I was able to build R.dll, export to Rdll.lib and wrap it in a Excel addin (.xll) format. Most basic functions work fine within Excel except functions that use LAPACK, e.g. eigen, solve etc. But if you use solve(..., LINPACK = T). That's fine. So apparently the problem is with loading the shared library ../modules/lapack.dll. I read one of the

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d

Dear developers, I am trying to reproduce the results of applying opt -O3 to a source file in the form of LLVM IR. I want to get the same IR by manually ordering the passes used by O3 and passing them to opt. To illustrate what I am doing on an example, as an input I use linpack benchmark from the LLVM test suite[1]: 1. First I produce the intermediate representation using clang: clang -O3

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 trouble using R routines from the RExcel add-in, that use lapack.dll. As an example if I start the R kernel from within Excel and execute "x<-rbind(c(1,2),c(1,-1)) z<-solve(x)", I get the following error: "Error in solve.default(x): lapack routines could not be loaded. In addition: Warning message: unable to load shared library "C:\Program