> 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.co...@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 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, 2, 3; 4, 5, 6]; % Example matrix > lambda = [2, 3, 4]; % Example vector > result_matlab = NN ./ lambda; > > which yields > > 0.50000 0.66667 0.75000 > 2.00000 1.66667 1.50000 > > > So, the only way I have stumbled onto in R to generate the same results > is to use 'sweep'. The following 'works', but I'm hoping someone can > explain why I need something as convoluted as this seems (to me, at least). > > NN <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, byrow = TRUE) # Example matrix > lambda <- c(2, 3, 4) # Example vector > sweep(NN, 2, lambda, "/") > > > [,1] [,2] [,3] > [1,] 0.5 0.6666667 0.75 > [2,] 2.0 1.6666667 1.50 > > First tried the more 'obvious' NN/lambda, but that yields 'the wrong > answer' (based solely on what I'm trying to accomplish): > > > [,1] [,2] [,3] > [1,] 0.500000 0.5 1.0 > [2,] 1.333333 2.5 1.5 > > So, why 'sweep'? > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.r-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.