similar to: Question about Calculation of Cross Product

Hi all, I have been searching all sorts of documentation, reference cards, cheat sheets but can't find why R's crossprod(A, B) which is identical to A%*%B does not produce the same as Matlabs cross(A, B) Supposedly both calculate the cross product, and say so, or where do I go wrong? R is only doing sums in the crossprod however, as indicated by (z <- crossprod(1:4)) # = sum(1 +

Dear list members; The code given below corresponds to the PCA-NIPALS (principal component analysis) algorithm adapted from the nipals function in the package chemometrics. The reason for using NIPALS instead of SVD is the ability of this algorithm to handle missing values, but that's a different story. I've been trying to find a way to improve (if possible) the efficiency of the code,

In a newly downloaded version (today) of R-2-4-0alpha, with all packages from CRAN also installed today, I get: > library(Matrix) Erro en loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'Matrix' methods specified for export, but none defined: BIC, anova, coef, confint, deviance, fitted, fixef, formula, head, lmer, logLik, mcmcsamp, plot,

Dear All, After upgrading to R-2.4.0-dapper2 (my system is ubuntu 6.06 LTS), I often met problems when loading some packages like Matrix. Here is the details: > library(Matrix) Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'Matrix' methods specified for export, but none defined: Arith, Math, Math2, +, %*%, Schur, as.matrix, chol,

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 they have the same size. And the R

This is a Request For Comment, also BCCed to 390 package maintainers of reverse dependencies of the Matrix package. Most users and package authors working with our 'Matrix' package will be using it for numerical computations, and so will be using "dMatrix" (d : double precision) matrix objects M, and indirectly, e.g., for M >= c will also use "lMatrix" (l:

Hi, I am trying to implement Higham's algorithm for correcting a non positive definite covariance matrix. I found this code in R: http://projects.cs.kent.ac.uk/projects/cxxr/trac/browser/trunk/src/library/Recommended/Matrix/R/nearPD.R?rev=637 I managed to understand most of it, the only line I really don't understand is this one: X <- tcrossprod(Q * rep(d[p], each=nrow(Q)), Q) This

Hello everybody When one is working with complex matrices, "transpose" very nearly always means *Hermitian* transpose, that is, A[i,j] <- Conj(A[j,i]). One often writes A^* for the Hermitian transpose. I have only once seen a "real-life" case where transposition does not occur simultaneously with complex conjugation. And I'm not 100% sure that that wasn't a

A new version of pqR is now available at pqR-project.org, which fixes several bugs that are also present in the latest R Core patch release (r66002). A number of bugs found previously during pqR development are also unfixed in the latest R Core release. Here is the list of these bugs that are unfixed in r66002 (including documentation deficiencies), taken from the pqR bug fix and documentation

Hi, I have two lists (c and h - see below) containing matrices with similar cases but different values. I want to split these matrices into multiple matrices based on the values in h. So, I did the following: years<-c(1997:1999) for (t in 1:length(years)) { year=as.character(years[t]) h[[year]]<-sapply(colnames(h[[year]]), function(var)

Hi Stefan, Thank you very much for pointing me to the wordspace package. It does the job a bit faster than my C code but is 100 times more convenient. By the way, since the tcrossprod function in the Matrix package is so fast, the Euclidean distance can be computed very fast: euc_dist <- function(m) {mtm <- Matrix::tcrossprod(m); sq <- rowSums(m*m);? sqrt(outer(sq,sq,"+") -

Despite the need to focus on pbeta, I'm still willing to put in some effort. But I find it really helps to have 2-3 others involved, since the questions back and forth keep matters moving forward. Volunteers? Thanks to Martin for detailed comments. JN On 2020-03-26 10:34 a.m., Martin Maechler wrote: >>>>>> J C Nash >>>>>> on Thu, 26 Mar 2020

Today I built R from source on a 32-bit Ubuntu 10.04.3 LTS box, and saw that the "make check" tests/reg-tests-1b.R failed. From the output at the end of my "tests/reg-tests-1b.Rout.fail" file, the problem is appearing in the "identical(z, x %*% t(y))" test code below. I then tried the stock R provided by the Ubuntu r-base-core binary package, and to my surprise, it

Thanks in advance! A=c(1, 2,3) B=c (9, 10, 11, 12) I want to get C=c(1*9, 1*10, 1*11, 1*12, ....., 3*9, 3*10, 3*11, 3*12)? C is still a vector with 12 elements Is there a way to do that? -- View this message in context: http://r.789695.n4.nabble.com/how-to-calculate-the-product-of-every-two-elements-in-two-vectors-tp2300299p2300299.html Sent from the R help mailing list archive at Nabble.com.

Hi everyone, I would like to compute the sum of cross products of rows of a matrix. Does anyone know how to do this without an explicit loop for computational efficiency? Below is a code example. Thanks! Stephan # I.e., if I have a matrix like this x <- matrix(rep(c(1,2,3),3),ncol=3) ## > x ## [,1] [,2] [,3] ## [1,] 1 1 1 ## [2,] 2 2 2 ## [3,] 3 3

Hi Martin I got stung by this last week. glmnet produces a coefficient matrix of class ?dgCMatrix? If a predictor matrix was created using sparseMatrix as follows, one gets unexpected results, as this simple example shows. My fix was easy (I always convert the predictor matrix to class ?dgCMatrix? now) Trevor > y=Matrix(diag(4)) > y 4 x 4 diagonal matrix of class "ddiMatrix"

Hi Martin, package arules heavily relies on ngCMatrix and uses multiplication and addition for logical operations. I think it makes sense that in a mixed operation with one dgCMatrix and one ngCMatrix the ngCMatrix should be "promoted" to a dgCMatrix. The current behavior of %*% and friends is in deed confusing: > m <- matrix(sample(c(0,1), 5*5, replace=TRUE), nrow=5) >

Dear All, I'm preparing a simple algorithm for matrix multiplication for a specific purpose, but I'm getting some unexpected results. If anyone could give a clue, I would really appreciate. Basically what I want to do is a simple matrix multiplication: (WB) %*% t(WB). The WB is in the disk so I compared to approaches: - Load 'WB' using 'read.table' (put it in WB.tmp) and

-- View this message in context: http://r.789695.n4.nabble.com/Generalized-DCC-GARCH-ML-estimation-tp2245125p2245125.html Sent from the R help mailing list archive at Nabble.com.

Dear all, how can I convert an m x n incidence matrix into an m x m adjacency matrix or an n x n adjacency matrix? The current matrix contains binary data, hence the new matrix would contain counts of common occurrences. Thank you for your help. Phil