similar to: S4 method selection based on second argument

Hello all, A colleague at work set me the challenge to convert some MATLAB code into R, to see which is faster. We'd seen that benchmark comparing MATLAB 6.5 to R1.90 (and others), and so I thought that I should be able to get roughly comparable speeds. The code has lots of multiplications of matrixes, transposes, and MATLAB's "repmat". I did the code conversion, and R was about

Hello, I'm trying to switch from Matlab to R-project, and having some difficulties. I make a use of multidimensional matrices. For example, I need to extract mean from one of the dimensions: % we have matrix data of size: 130 x 11 x 350 x 2 data = data - repmat(mean(data,3),[130 1 1 1]); In R project I managed to do that in a very pervarsive way: # mean(data,3) in R base <-

Hi there, I have a piece of Matlab code I use to optimise a trding strategy. If there are any Matlab/R specialists out there, I would appreciate your help in doing the exact same optimisation in R. I suspect I would use nlm() in R but am not sure where to define my constraints. I have attached my Matlab code below for reference. Many thanks. Constraints function [c,ceq]=TriskellConstraints(X)

Hello, I'm at the very beginning of the learning process of this language. Sorry in advance for the (possible but plausible) stupidity of my question. I would like to find a way to permute the DIMENSIONS of an array. Something that sounds like the function "permute()" in matlab. Given an array C of dimensions c x d x T , for instance, the command permute(C, [2 1 3]) would provide

Hi, The function Repmat under S allows to represent a matrix of data. Is there an equivalent function under R ? thanks for your reply Bruno -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not

Hi, I have noticed that aperm is very slow, and I wondered if there was a way of speeding it up. Let me tell you a bit about the context of my problem, because perhaps I shouldn't be using aperm at all. The context is probabilistic inference in graphical models. One of the most fundamental operations is two compute an element-wise multiplication of two arrays of different sizes, say A and B.

Dear All, I have a table as following position type count 1 2 100 1 3 51 1 5 64 1 8 81 1 6 32 2 2 41 2 3 85 and so on Normally if would have a vector of 2,3,4,5... by position position and plot them by position. But now i have counts of these types. Is there a way to compute boxplot of such

For an application, I have formulas defined in terms of a left Kronecker product of matrices, A,B, meaning A \otimes_L B = {A * B[i,j]} -- matrix on the left multiplies each element on the right. The standard kronecker() function is the right Kronecker product, A \otimes_R B = {A[i,j] * B} -- matrix on the right multiplies each element on the left. The example below shows the result of

Hi I'm using kronecker() with a matrix and a vector. I'm interested in the column names that kronecker() returns: > a <- matrix(1:9,3,3) > rownames(a) <- letters[1:3] > colnames(a) <- LETTERS[1:3] > b <- c(x=1,y=2) > kronecker(a,b,make.dimnames=TRUE) A: B: C: a:x 1 4 7 a:y 2 8 14 b:x 2 5 8 b:y 4 10 16 c:x 3 6 9 c:y 6 12 18 > The

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)

Here is a couple of very simple data.frames: X<-data.frame(A=1:10, B=0, C=1, Ob1=1:10, Ob2=2:11, Ob3=3:12, Ob4=4:13, Ob5=3:12, Ob6=2:11) Y<-data.frame(A=1:20, B=0, C=1, D=5, Ob1=1:10, Ob2=2:11, Ob3=3:12, Ob4=4:13, Ob5=3:12, Ob6=2:11, Ob7=5:9) Z<-data.frame(A=1:30, B=0, C=1, D=6, E=1:2, Ob1=1:10, Ob2=2:11, Ob3=3:12, Ob4=4:13, Ob5=3:12, Ob6=2:11, Ob7=1:10, Ob8=3:12) Each row in the

kronecker, with make.dimnames=TRUE uses a hardwired sep=":" in the line tmp <- outer(dnx[[i]], dny[[i]], FUN = "paste", sep = ":") For an application in which dimnames arise from an n-way array, where different dimensions have different roles, and I would like to be able to use kronecker in the form kronecker(A, B, make.dimnames=TRUE,

Dear All, Could someone please suggest how to find the Kronecker sum of two 2x2 matrices, i.e. given two matrices: -A A a -a and -B B b -b I need: -A-B A B 0 a -a-B 0 B b 0 -A-b A 0 b a -a-b Many thanks, Lara [[alternative HTML version deleted]]

Hi, I'm trying to compute  the spatiotemporal correlation matrix by using Delta Kronecker products of spatial and temporal correlation matrix  in R, but didn't find any delta Kronecker's operator in R. The operators in matrix such as multiplication, addition, eigen values/vector and etc is easily to find and used. Could someone help me, please? Cheers. Firdaus

Hi R users all , I have a clean install of R-2.15.0 in a win32 machine and a problem loading Matrix 1.0-6 that looks like this: > library(Matrix) Loading required package: lattice Error : object ?kronecker? is not exported by 'namespace:methods' Error: package/namespace load failed for ?Matrix? > I understand that kronecker() now has an S4 generic in package methods. Is that a

Hi I want to write a little function that takes a matrix X of size m-by-n, and a list L of length "m", whose elements are matrices all of which have the same number of columns but possibly a different number of rows. I then want to get a sort of dumbed-down kronecker product in which X[i,j] is replaced by X[i,j]*L[[j]] where L[[j]] is the j-th of the "m" matrices. For

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 <-

Dear R-users, I coded two equivalent ways to perform (in a simplified version) some matrix multiplications I would like to use in a more general framework. In the first case I used Kronecker product and vectorization of a certain matrix. This approach takes less time, but, as you may guess, I run out of memory when dimensions are large. In the second approach, I profited of sparseness and

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.

Hello everybody. I am trying to reproduce a particular matrix in an elegant way. If I have jj1 <- structure(c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2, 3,1,2,3,1,2,3,1,2,3,2,3,1,2,3,1,2,3,1,2,3, 1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,3,1,2,3,1, 2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1, 2),.Dim = as.integer(c(9,9))) [ image(jj1) is good here ] then I can get this with