similar to: maximum over one dimension of a 3-dimensional array

Why does the assignment of a 3178x93 object to another 3178x93 object remove the dimension attribute? > GT <- array(dim = c(6,nrow(InData),ncol(InSNPs))) > dim(GT) [1] 6 3178 93 > SNP1 <- InSNPs[InData[,"C1"],] > dim(SNP1) [1] 3178 93 > SNP2 <- InSNPs[InData[,"C2"],] > dim(SNP2) [1] 3178 93 > dim(pmin(SNP1,SNP2)) [1] 3178 93

I'm attempting to sort a 3 dimensional array that looks like this > x , , 1 [,1] [,2] [1,] 9 9 [2,] 7 9 , , 2 [,1] [,2] [1,] 6 5 [2,] 4 6 , , 3 [,1] [,2] [1,] 2 1 [2,] 3 2 Such that it ends up like this .... > y , , 1 [,1] [,2] [1,] 2 1 [2,] 3 2 , , 2 [,1] [,2] [1,] 6 5 [2,] 4 6 , , 3 [,1] [,2]

It looks like OP uses a data.frame, so in order to use matrixStats (I'm the author) one would have to pay the price to coerce to a matrix before using matrixStats::rowMaxs(). However, if it is that the original data could equally well live in a matrix, then matrixStats should be computational efficient for this task. (I've seen cases where an original matrix was turned into a data.frame

On Tue, Feb 20, 2018 at 11:58 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > Ista, et. al: efficiency? > (Note: I needed to correct my previous post: do.call() is required for > pmax() over the data frame) > > > x <- data.frame(matrix(runif(12e6), ncol=12)) > > > system.time(r1 <- do.call(pmax,x)) > user system elapsed > 0.049 0.000

Ista, et. al: efficiency? (Note: I needed to correct my previous post: do.call() is required for pmax() over the data frame) > x <- data.frame(matrix(runif(12e6), ncol=12)) > system.time(r1 <- do.call(pmax,x)) user system elapsed 0.049 0.000 0.049 > identical(r1,r2) [1] FALSE > system.time(r2 <- apply(x,1,max)) user system elapsed 2.162 0.045 2.207 ##

I just want to integrate a 3D data set along one dimension to obtain a 2D data set. Something like: (given array "d" with dim nx,ny,nz ...) data_int<-array(dim=c(nx,ny)) for (n in 1:ny) { for (m in 1:nx) { data_int[m,n]<-sum(d[m,n,]) } } The thing is, given R's facility with integers, it seems that I should be able to obtain data_int without the

The documentation says, "pmax and pmin take several vectors as arguments and return a single vector giving the parallel maxima (or minima) of the vectors." I discovered that, if you use a matrix or array instead of a vector, pmax returns a matrix or array, respectively. This makes pmax and pmin much more useful, and should not be left to people to discover on their own! For example:

Thank you for your kind replies. Maybe I was not clear with my question (I apologize) or I did not understand... I would like to take the max for X0...X11 and X12...X24 in my dataset. When I use pmax with the function byapply as in byapply(df, 12, pmax) I get back a list which I cannot convert to a dataframe. Am I missing something? Thanks again! Sincerely, Milu

Just as as.vector() takes a vector, matrix or array and returns a vector in row-major order, I'd like to write a function to take such an object and return the dimension names, pasted with some separator, as a similar vector. Here is something ugly cobbled together to demonstrate what I want: a function to work for any number of dimensions. vecnames <- function(x, sep=':') {

If I have 2 n-dimensional arrays, how do I compose them into a n+1-dimension array? Is there a standard R function that's something like the following, but that gives clean errors, handles all the edge cases, etc. abind <- function(a,b) structure( c(a,b), dim = c(dim(a), 2) ) m1 <- array(1:6,c(2,3)) m2 <- m1 + 10 abind(m1,m2) ==> , , 1 [,1] [,2] [,3] [1,] 1 3 5

how to implement multi dimensional array in ruby in ruby multi dimension array look like tree structure plz help me & explain with code have a pleasant day thx narayana -- Posted via http://www.ruby-forum.com/.

Hello list members, I am working with simulated data for landscape pattern analysis. I have 1000 replicates of binary (2 colour) gridded landscapes at each combination of 9 levels of class proportion and 11 levels of spatial autocorrelation. The results are stored in an array as follows: > dim(surfaces) [1] 38 9 11 1000 The dimensions are defined as follows: [x,,,] 1:38, integers

Hi everyone, I created a four dimensional vector (dim (128,128,1,8)). This third dimension is necessary for another function somewhere. Now I'd like to perform a t-test on every vector of length 8 in my array on the fourth dimension. I'd like to obtain a new array of three dimensions with dimensions 128x128x1 with all these test statistics. I tried this with a double loop: A <-

maybe we've already diskussed this before, but Kurt and I can't remember ... is.vector() of an one-dimensional array returns FALSE. this is also the behavior of Splus, but totally counter-intuitive for me ... IMO an array of dimension 1 is exactly the definition of a vector ... it also breaks our current plot.factor, which is simply a barplot(table(x)) table() returns an

Hi suppose I have two arrays x1,x2 of dimensions a1,b1,c1 and a2,b2,c2 respectively. I want x = x1 "+" x2 with dimensions c(max(a1,a2), max(b1,b2),max (c1,c2)) with x[a,b,c] = x1[a1,b1,c1] + x2[a2,b2,c2] if a <=min(a1,a2) , b<=min (b1,b2), c<=min(c1,c2) and the other bits either x1 or x2 or zero according to whether the coordinates are "in range" for

If I have a two column data frame like: > dat <- cbind("x"=c(1:100),"y"=c(100:1)) How can I create an array that splits every ten rows of that data frame into a third dimension of an array so that: > newarray[,,1] ,,1 x y 1 100 2 99 3 98 ... ... 10 91 ,,2 x y 11 90 12 89 ... ... ... Thanks. [[alternative HTML version deleted]]

I am using R for my bioinformatics research. I am dealing with a graph in which I need to find all possible path. I was looking for some package that solve my purpose but all in vain. There are available algorithms but most of them find shortest path that ignore other paths So I decided to write my own from scratch. I need to create a two dimensional matrix of size nXn. The element of each entry

I would like to store and manipulate large sets of marker genotypes compactly using "raw" data arrays. This works fine for vectors or matrices, but I run into the error shown in the example below as soon as I try to use 3 dimensional arrays (eg. animal x marker x allele). > a <- array(as.raw(1:6),c(2,3)) > a [,1] [,2] [,3] [1,] 01 03 05 [2,] 02 04 06 >

>>>>> Anupam Tyagi >>>>> on Tue, 9 Jul 2024 16:16:43 +0530 writes: > How can I do automatic knot selection while fitting piecewise linear > splines to two variables x and y? Which package to use to do it simply? I > also want to visualize the splines (and the scatter plot) with a graph. > Anupam NB: linear splines, i.e. piecewise

Hello, A simple question, although I can't find an answer via my google/forum search: I have a 4-dimensional array; call it A[1:M,1:N,1:P,1:Q]. I have a vector x that is N by 1. I would like to "quickly" add x to the 2nd dimension of A; in other words, I want a quicker way of doing the following: for (m in 1:M) { for (p in 1:P) { for (q in 1:Q) { A[m,,p,q] =