search for: elementwise

...13 18 [2,] 13 20 27 [3,] 18 27 36 because there are only sums involved, however, I cant work out ALL of them for the 3D example, something like [,1] [,2] [,3] [1,] 1*4+4*1 ? ? # #sum of elementwise multiplication of first column of first matrix with first column of second matrix of second matrix [2,] ? 2*5+5*2 ? ? cant figure out how these were derived, definitely this is NOT identical to e.g. http://en.wikipedia.org/wiki/Cross_product I can work it out...

I have two lists of equal lengths. Each one consists of vectors. Is there an elegant way to combine them elementwise? Elegant means: without loops. For instance, for > jja_list(1:3,4:7) > jjb_list(rep(0,4),(2,5)) I would like to get: > list(c(1:3,rep(0,4)),c(4:7,rep(2,5))). --- D.Trenkler --- ************************************************************************ ********* Dietrich Trenkle...

All, Is there one liner way to obtain the max per observation for two vectors? I looked at apply and lapply but it seems that groundwork would have to be done before applying either of those. The code below does it but seems like overkill. Thanks! Dave x = rnorm(10) y = rnorm(10) ind = which(x < y) z = x z[ind] <- y[ind] ## z now contains the max's

Hi How do I concatenate two lists element-by-element? Example: list.1 <- list(temperature=c("hot","cold") , size=c("big","medium")) list.2 <- list(temperature=c("lukewarm") , size=c("massive","tiny")) list.wanted <- list(temperature=c("hot","cold","lukewarm") ,

Hello fellow R users, I have a matrix computation that I imagine should be relatively easy to do, however I cannot figure out a nice way to do it. I have two matrices, for example mat1 <- matrix(c(1:5,rep(NA,5), 6:10), nrow=3, byrow=T) mat2 <- matrix(c(2:6, 6:10, rep(NA,5)), nrow=3, byrow=T) I'd like to compute the element-wise average for non-NA entries. Of course (mat1+mat2)/2

Dear all, I want to concatenate the elements of two vectors such as a<-c("a1","a2") b<-c("b1","b2") and obtain "a1b1", "a1b2","a2b1","a2b2" I tried the paste and paste0 functions, but they yielded elementwise concatenation such as "a1b1","a2b2" I am wondering that is there an efficient way of doing my wish, for instance without using for loop etc. Best Ozgur -- View this message in context: http://r.789695.n4.nabble.com/concatenating-two-vectors-tp4643139.html Sent from the R...

`outer` (and related functions like kronecker) require that their functional argument operate elementwise on arrays. This means for example that outer( 1:2, 3:4, list) or outer(1:2,3:4,function(a,b){1}) gives an error. Is there a version of `outer`/`kronecker`/etc. that takes arbitrary functions and does its own elementwise mapping? In the first example above, I'd expect the result...

...ipt: Using Version 2.12.0 on a dual core dual processor Mac: > source("http://www.bio.umass.edu/biology/kunkel/pub/R/CuriousResult.R") matrix multiplication 43.543 1.308 14.788 tcrossprod 41.147 1.286 11.9 transposition and reuse 40.407 3.525 43.606 elementwise after reshape 21.474 1.828 23.124 columnwise sapply 34.695 32.35 66.592 for loop over columns 37.237 29.471 67.2 On the same day upgrading to 2.12.1 on the same dual core dual processor Mac: > source("http://www.bio.umass.edu/biology/kunkel/pub/R/CuriousResult.R&...

Hi, this is maybe not a real R problem but I want to solve this in R ;-) Consider the set of all permutations of 1:N (=: S, say) and a fixed element a from S. I now need to compute the number of permutations s from S which are elementwise less or equal to a: | { s \in S | s <= a } | Of cource, backtracking using a tree structure is possible. Does anyone know an efficient way? Torsten -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/...

...linux Submission from: (NULL) (213.140.16.187) In 'outer' man page, there is no mention of the Vectorize function. Moreover, I think it isn't underlined enough that the FUN argument to 'outer' must be a vectorized function (doc speaks about a function which has to 'operate elementwise'). A cross-reference from outer to Vectorize (which already has 'outer' usage examples) would be great. > R.version _ platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 2 minor 6...

Without 'collapse', 'paste' pastes (concatenates) its arguments elementwise (separated by 'sep', " " by default). New in R devel and R patched, specifying recycle0 = FALSE makes mixing zero-length and nonzero-length arguments results in length zero. The result of paste(n, "th", sep = "", recycle0 = FALSE) always have the same length as...

I have a huge matrix on which I need to do a simple (elementwise) transformation. Two of these matrices cannot fit in the memory, so I cannot do this in R. I thought of writing some C code to do this and calling it using .C with DUP=FALSE. All I need is a simple for loop that replaces elements with their new value, something like void transform(double *a, in...

...nt it to do. i hav a function only defined for scalars fun(n,k) and i want to do outer(1:5,1:5,function(x,y) fun(x,y)) (i know this is written somewhat clumsy) to get an array with the values of fun to use it as an input for image this does not work since outer assumes then fun works on arrays elementwise. what is the easiest way of getting what i want? outer in some lisp like variants and in mathematica will work with the function argument being a scalar argument only function. r is different here. -- Erich Neuwirth, Computer Supported Didactics Working Group Visit our SunSITE at http://sunsi...

...ouple of (~200) 3x3 transition probability matrices (ie each defines a Markov chain). They are all estimated from the same underlying process, so it ie meaningful to take their elemetwise mean and standard deviation. [1] First question: supposing that they are given in a list l, how do I get their elementwise mean and standard deviation? Fortunately, the mean of trans. prob. matrices also remains a transition probability matrix. I'm not sure if taking the standard deviation is meaningful, though. Any suggestions? Second (more important): what would be the best way of visualizing the 3x3 mean and st...

Wouldn't it make programming more error-resistant if vectors were initialized with missing data, instad of zeroes or ""? That way, if you assign values to a vector elementwise and you miss some elements (because their indices were not selected or because the assignment didn't work out, see below for code examples) this would be immediately obvious from the value of the vector elements themselves and programming errors would be far less easy to overlook. e.g. x <...

Hi, I noticed whether some one could explain why "&" and "&&" behave differently in data frame transformations. Consider the following : a<-data.frame(r=c(0,0,2,3),g=c(0,2,0,2.1)) Then: > transform(a,R=ifelse(r>0 && g> 0,log(r/g),NA)) r g R 1 0 0.0 NA 2 0 2.0 NA 3 2 0.0 NA 4 3 2.1 NA but > transform(a,R=ifelse(r>0 & g>

...ot; > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > > On Thu, Apr 30, 2020 at 9:56 PM suharto_anggono--- via R-devel < > r-devel at r-project.org> wrote: > >> Without 'collapse', 'paste' pastes (concatenates) its arguments >> elementwise (separated by 'sep', " " by default). New in R devel and R >> patched, specifying recycle0 = FALSE makes mixing zero-length and >> nonzero-length arguments results in length zero. The result of paste(n, >> "th", sep = "", recycle0 = FALSE) alw...

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 helpeRs, I seem to be a little bit confused on the result I am getting from the few codes below: > u=v=seq(0,1,length=30) > u [1] 0.00000000 0.03448276 0.06896552 0.10344828 0.13793103 0.17241379 [7] 0.20689655 0.24137931 0.27586207 0.31034483 0.34482759 0.37931034 [13] 0.41379310 0.44827586 0.48275862 0.51724138 0.55172414 0.58620690 [19] 0.62068966 0.65517241 0.68965517 0.72413793

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 straight division NN / lambda then lambda is repeated: 1 4 2 5 3 6 2 3 4 2 3 4 to get 0.5 1.3 0.5 2.5 1.0 1.5 but if you transpose first 1 4 2 5 3 6 then that co...