similar to: Converting data frame into multidimensional array

Hello. I have a 4 dimensional array and I want to fill in the slots with values which are a function of the inputs. Through searching the forums here I found that the function "outer" is helpful for 2x2 matrices but cannot be applied to general multidimensional arrays. Is there anything which can achieve, more efficiently than the following code, the job I want? K <-

I'm looking for a function in R that extends kappa to multiple raters when there is more than one response per subject. For example, say a group of doctors have to assign diseases to patients. Each patient will be assigned one to many diseases, and the number of doctors assigning diseases to any one patient will be two to many. Here's an extremely simple example of the type of data I

Hi there I know, I'm sure you discussed this stuff 100 times, but I really have a basic understanding problem, if and how do I create a multidimensional array in R. I'm coming from MATLAB and there it's as easy as you ever could imagine. Ok, so, I want to have an array, where I can fill in data from a Excel spreadsheet. The array should be addressed like this:

I am moving this to r-devel. The problem and solution below posted on r-help could have been a bit slicker if %*% worked with multidimensional arrays multiplying them so that if the first arg is a multidimensional array it is mulitplied along the last dimension (and first dimension for the second arg). Then one could have written: Tbar <- tarray %*% t(wt) / rep(wti, each = 9) which is a bit

On Aug 2, 2019, at 8:57 AM, Michael Kruse <llvmdev at meinersbur.de> wrote: >> This is why I ask whether its makes sense to add this to LLVM IR: If you want HPC style loop transformations, I don’t think that LLVM IR itself will ever be great, even with this. This might make some narrow set of cases slightly better, but this is far from a solution, and isn’t contiguous with getting to

I get some results back from running an iterative analysis in the form of a list of matrices. What I would like to do with this list is combine it such that all the similar components get combined into a multidimensional array. If possible I'd like to put results[[1]]$resultmean and results[[2]]$resultmean into a 3x3x2 array, and also put results[[1]]$resultsd and results[[2]]$resultsd in a

Hi, I am looking for some generalization of colSums and rowSums for general vector valued functions, and for arrays of more than 2 dimensions. So as a concrete example, suppose I have a 3 dimensional array, given by x = array(1:100,c(3,4,5)). and I want to sum the 3rd index of x to obain a 3 by 4 matrix. Using rowSums would return a vector of length 3 because it treats the last two indices as

Linearized array addresses are an issue in loop nest transformation. To alleviate the same, a multidimensional array indexing intrinsics have been proposed recently in the llvmp-dev mailing list [1]. From the mailing thread, it looks like there is a consensus on using intrinsics for communicating dimensions [2]. While working with our own loop transformation framework, we did a similar work on

Hello Group, I am having trouble with the sqldf package on unix. The same code works fine on windows. Silly Example script: # Load the package library(sqldf) # Use the titanic data set data(women) colnames(women) head(women) sqldf('select height, count(*) from women where height is not null group by weight') Unix Output and error: bash-3.00$ R --vanilla <testR.R

A student of mine wants to use R to do some nonmetric multidimensional scaling. According to the R FAQ, there's a package called pcurve that computes multidimensional scaling solutions, but I was not able to locate it the contrib page (I am a Windows user with R version 1.4.1). Can anyone tell me whether it is possible to do nonmetric multidimensional scaling with R, and if so, how? John

It's also very common in Fortran. -David Michael Ferguson via llvm-dev <llvm-dev at lists.llvm.org> writes: >> It seems that the main advantage of your proposal is that it would >> allow for non-constant strides (i.e. variable length arrays) in >> dimensions other than the first one. Do these appear frequently >> enough in the programs

Hello list. Another 'puzzle' for which I don't have a clean solution. Say I have a multidimensional object, e.g.: Mm<-matrix(1:6, nrow=2, dimnames=list(c("a","b"), c("g","h","i"))) And on the other hand I have a list Ind<-list("b","g") This holds, for each dimension, an indexer for that dimension. Now I would

Hi, anyone knows about any functions in R can get multidimensional integration not over a multidimensional rectangle (not adapt). For example, I tried the following function f(x,n)=x^n/n! phi.fun<-function(x,n) { if (n==1) { x }else{ integrate(phi.fun, lower=0, upper=x, n=n-1)$value } } I could get f(4,2)=4^2/2!=8, but failed in f(4,3)=4^3/3! Thanks Best, Lynette

On Jul 29, 2019, at 1:30 PM, Michael Kruse <llvmdev at meinersbur.de> wrote: >> Have you been following what is happening on the MLIR side of the world? It directly supports multi-dimensional arrays with both value and buffer semantics (e.g. load, store, and DMA operations). It is specifically focused on solving these sorts of problems, and is the current proposed direction for the

After having spoken to Johannes, I think we had a classic misunderstanding on what "extending" means. 1. The most obvious why for me was changing GEP to allow variable-sized multi-dimensional arrays in the first argument, such as %1 = getelementptr double, double* %ptr, inrange i64 %i, inrange i64 %j (normally GEP would only allow a single index argument for a pointer-typed base

Hello, Your way is much better than to mess with the dim attribute, like I did. But, "If you can create a data.frame or matrix that has the indices" Actually, it must be a matrix, indices can't be of type list. A way to avoid loops/apply altogether, and much faster, is the one creating K3 (K is the result from the op.) n <- 20 t2 <- system.time({ K2 <-

> It seems that the main advantage of your proposal is that it would allow for non-constant strides (i.e. variable length arrays) in dimensions other than the first one. Do these appear frequently enough in the programs that you're interested in to be worth optimizing for? Yes - at least in Chapel (which is one of the motivating languages) these are very common. In other words, typical

Hi, I recently encountered a problem with array subsetting and came up with a fix. Given an array of arbitrary dimensions, in which the number of dimensions is only known at runtime, I wanted to extract a subarray. The main issue with doing this is that in order to extract a subarray from an array of (say) 4 dimensions you usually specify something like this a.subarray <- a[,c(4,2),1:5,]

Hi, I am inquiring as to what are the best practices with respect to storing and manipulating ordered multi-dimensional arrays. For example, suppose I have a sequence of time-varying covariance matrices of asset returns. The data is ordered, but the ordering is not necessarily regular (e.g. daily data omitting weekends and holidays, etc.). The data array is say, N x N x T. For example, the

We could also simply extend the existing inrange mechanism to non-constantexpr GEPs.  It would remove an inconsistency in the semantics, be relatively straight forward, and solve the motivating example. (I didn't read the proposal in full, so there may be other examples it doesn't solve.) Philip On 7/22/19 10:01 AM, Peter Collingbourne via llvm-dev wrote: > The restrictions of