similar to: adding a row to a matrix

I am trying to divide the columns of a matrix by the first row in the matrix. I have tried to get this using apply and I seem to be missing a concept regarding the apply w/o calling a function but rather command args %*% / etc. Would using apply be more efficient than this approach? I have observed examples in the archives using this type of approach. Does anybody have a snippet of a call

I was looking for an R-package that would reduce matrices to row-echelon form, but Google was not my friend; any leads? If not, I wonder if the problem could be expressed in terms of constraint satisfaction...

Hi, Although this is not directly an R-related question, it is relevant as I am trying to port some R code to C to speed things up in a computation. I am working through my first attempts to generate and link compiled C code in R. I could make the 'convolve' function to work and similar functions that take vectors as arguments. In my application I need to pass a couple of matrices to

Dear R users I have a problem regarding an addition of an extra "row" to a matrix. e.g. i have a matrix a <- matrix(1:6,2,3) > a [,1] [,2] [,3] [1,] 1 3 5 [2,] 2 4 6 I want to add a matrix having just one row. e.g. b <- matrix(7:9,1,3) > b [,1] [,2] [,3] [1,] 7 8 9 Now i want to get result like this [,1] [,2] [,3] [1,] 1 3 5

Why anything but sweep? The fundamental data type in Matlab is a matrix... they don't have vectors, they have Nx1 matrices and 1xM matrices. Vectors don't have any concept of "row" vs. "column". Straight division is always elementwise with recycling as needed, and matrices are really vectors in row-major order: 1 2 3 4 5 6 is really 1 4 2 5 3 6 and when you do

> t(t(NN)/lambda) [,1] [,2] [,3] [1,] 0.5 0.6666667 0.75 [2,] 2.0 1.6666667 1.50 > R matrices are column-based. MATLAB matrices are row-based. > On Feb 27, 2024, at 14:54, Evan Cooch <evan.cooch at gmail.com> wrote: > > So, trying to convert a very long, somewhat technical bit of lin alg > MATLAB code to R. Most of it working, but raninto a stumbling block

is there a way to do element-by-element multiplication as in Gauss and MATLAB, as shown below? Thanks. --- a 1.0000000 2.0000000 3.0000000 x 1.0000000 2.0000000 3.0000000 2.0000000 4.0000000 6.0000000 3.0000000 6.0000000 9.0000000 a.*x 1.0000000 2.0000000 3.0000000 4.0000000

Dear R users I have a 4-dimensional matrix (actually several 3d (x,y, slices) matrices appended over time (volumes)) say, e.g. I want to z-transform the data (subtract the mean and divide by the std-deviation) for (slice in 1:slices) { for (x in 1:x.dim) { for (y in 1:y.dim) { t <- as.matrix(my.matrix[x,y,slice,1:volumes]) for (vol in 1:volumes) {

So, trying to convert a very long, somewhat technical bit of lin alg MATLAB code to R. Most of it working, but raninto a stumbling block that is probaably simple enough for someone to explain. Basically, trying to 'line up' MATLAB results from an element-wise division of a matrix by a vector with R output. Here is a simplified version of the MATLAB code I'm translating: NN = [1,

On Tue, 27 Feb 2024 13:51:25 -0800 Jeff Newmiller via R-help <r-help at r-project.org> wrote: > The fundamental data type in Matlab is a matrix... they don't have > vectors, they have Nx1 matrices and 1xM matrices. Also known as column vectors and row vectors. :) > Vectors don't have any concept of "row" vs. "column". They do in (numerical) linear

I'm trying to calculate the row-wise kronecker product A \Box B of two sparse matrices A and B, and am struggling to find a quick way to do this that takes advantage of sparseness. I thought a good idea would be to use "rep" to construct 2 matrices of the same dimension of the end product, and multiply these two together: library(Matrix) A<-Matrix(c(1,0,0,0,0,1,2,0), 2, 4)

Submitted for perusal, comment, improvements, and/or critique. The presentation is in 3 sections: motivation, code, and comment. Motivation: As a new-comer to R from matrix oriented Gauss and Mata, I miss the tools for using a vector (and operator) to ‘sweep’ across a matrix. Here is how these work. If M is I rows by J columns, then one entry corresponding to

there are really two related problems here I have a 2D matrix A <- matrix(1:100,nrow=20,ncol =5) S <- matrix(1:10,nrow=2,ncol =5) #I want to subtract S from A. so that S would be subtracted from the first 2 rows of #A, then the next two rows and so on. #I have a the same problem with a 3D array # where I want to subtract Q for every layer (1-10) in Z # I thought I solved this one

I am working with transition matrices of sequences of animal behaviours. What I would like to do is parse the original matrices, adjusting row/column order so that the matrix has its main values in blocks surrounding the diagonal. This would cause behaviours involved in functional groupings (e.g. grooming, resting, foraging etc) to appear as blocks. This can be done manually by applying

Hello again, Let say I have a matrix: Mat <- matrix(1:12, 4, 3) And a vector: Vec <- 5:8 Now I want to do following: Each element of row-i in 'Mat' will be divided by i-th element of Vec Is there any direct way to doing that? Thanks for your help

Dear expeRts, I have two matrices A and B. They have the same number of columns but possibly different number of rows. I would like to compare each row of A with each row of B and check whether all entries in a row of A are less than or equal to all entries in a row of B. Here is a minimal working example: A <- rbind(matrix(1:4, ncol=2, byrow=TRUE), c(6, 2)) # (3, 2) matrix B <-

Hi, I was wondering if there is a direct approach for lining up 2-column matrices according to the values of the first column. An example and a brute-force approach is given below: x <- cbind(1:10, runif(10)) y <- cbind(5:14, runif(10)) z <- cbind((-4):5, runif(10)) xx <- seq( min(c(x[,1],y[,1],z[,1])), max(c(x[,1],y[,1],z[,1])), 1) w <- cbind(xx, matrix(rep(0, 3*length(xx)),

Hi, I'm a new user. I've been reading through the manual and looking at various examples but am still trying to make sense of the most efficient ways to handle matrices of data. If I have a 2D matrix of data, how do I get the mean, min, max value of each row? I see the "mean" function on a matrix will give me averages by row, but min and max give me the value for the entire

I have matrices stored within a list like something as follows: a <- list(matrix(rnorm(50), ncol=5), matrix(rnorm(50), ncol=5)) b <- list(matrix(rnorm(50), nrow=5), matrix(rnorm(50), nrow=5)) I don't recall how to perform matrix multiplication on each list element such that the result is a new list result <- list(a[[1]]%*%b[[1]], a[[2]]%*%b[[2]]) I think I'm close with

Hi, when I assign a one row matrix to another variable, then somehow R automatically convert that varaible into a list. For example: > a = matrix (12, 3, 4) > b = a[1,] > b [1] 12 12 12 12 Is there a way to enable R automatically make b as a one row matrix , rather than explicitly assign like matrix (b, ncol=1). I have come across a couple of time, that when I try to check how many