similar to: Making 2 dimensional vector from the 3 dimensional one

Dear Everyone, I 've just begun to use the library ncdf and I would like to compare meteorological observational data with forecast data, so to make verification. The netcdf files I'm using contain data of many different parameters in many different stations. I could read easily that I needed, but naturally I do not need the data of all the stations. On the other hand, the order of

Dear Everyone, I wrote a Taylor diagram program in R, but it was not very general, so I was happy to find the Taylor.diagram in the plotrix package. On the other hand I can find many problems with the pos.cor=TRUE case, in other words, when we use only the first quarter of the space (positive correlations). (When we choose pos.cor=FALSE, the program seems to me perfect) 1.There is only one line

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/.

Let's start with a concrete case of n=3 dimensions. Along dimension x, I have a matrix of 5 vectors (each with ten rows) Along dimension y, I have a matrix of 3 vectors (each with ten rows) Along dimension z, I have a matrix of 2 vectors (each with ten rows) I am trying to write a: function(..., funcp) { } where funcp(...) is an operator which works on n vectors and returns a numeric.

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]

I find the one-dimensional "integrate" very helpful, but often enough I stumble into problems that require two (or more)-dimensional integrals. I suppose there are no R functions that can do this for me, "directly"? The ideal thing would be to be able to define say f <- function(x) { x1 <- x[1] x2 <- x[2] sin(x1*x2)*exp(x1-x2) } and then write say integrate(f,

I want to take slices of a multi-dimensional table (or array) without knowing the number of dimensions in advance. As a test I tried using (in this example a 3d table): do.call(`[`, list(tbl, x,NULL,NULL)] where I built the list on the fly. It works great as long as I only want the first dimension however when I try a different dimension, say with list(tbl,NULL,x,NULL), I get

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 Simon, thank you for the concise reply. Do you mean the reported behavior of drop() is not a bug? It looks like a borderline bug to me (see below), but I'm not the judge of that. If this is the intended behavior and serves an actual purpose, then that could be explicitly documented in a \note{} on the help page. Such a note would slightly reduce the surprise of users running into this

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

foo <- rnorm(30*34*12) dim(foo) <- c(30, 34, 12) I want to make a data.frame out of this three-dimensional array. Each dimension will be a variabel (column) in the data.frame. I know how this can be done in a very slow way using for loops, like this: x <- rep(seq(from = 1, to = 30), 34) y <- as.vector(sapply(1:34, function(x) {rep(x, 30)})) month <- as.vector(sapply(1:12,

On 09/11/2012 03:38 PM, Hal Finkel wrote: > This is a general problem that also affects, for example, Preston's > dependence-analysis work. How have you thought about dealing with this? Hi Hal, you are right. Preston's dependence-analysis and Polly are sharing the same problem. As far as I know, non of us implemented a solution yet. Preston, what is your status on multi-dim arrays?

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

Dear R user, I'm trying to find a solution for optimizing my code. I have to run a 50.000 iteration long simulation and it is absolutely necessary to have an optimized code. I have to do this operation *sum_t ( t(X_t) %*% A %*% X_t )* where X_t is a (d*k) matrix which changes in time and A is a constant in time (d*d) matrix. I have put all my X_t in a three dimensional array X of dimension

I am using numerical optimization to fit a 1 parameter model, in which the input parameter is bounded. I am currently using optimize(), however, the problem turns out to have local minima, and optimize does not always seem to find the global minimum. I could to write a wrapping function that tries multiple intervals or starting values, but I would prefer a package that has built-in methods to make

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 <-

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]]

Hi, I had a few thoughts about recovering multi-dimensional accesses in Polly and maybe it's time to share them. On 09/12/2012 10:38 AM, Tobias Grosser wrote: > [...] Even in the last example which contains parameters both for the > sizes and the offsets, the parameters for the sizes are the only ones that > appear on the right hand sides ('8 * %m * %o' and '8 *

On Wed, 12 Sep 2012 14:18:43 +0200 Armin Größlinger <armin.groesslinger at uni-passau.de> wrote: > Hi, > > I had a few thoughts about recovering multi-dimensional accesses in > Polly and maybe it's time to share them. > > On 09/12/2012 10:38 AM, Tobias Grosser wrote: > > [...] Even in the last example which contains parameters both for > > the sizes and

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