similar to: Matrix indexes

I have output from a program which produces a distance matrix I want to read into a clustering program in R. The output is a .txt file and is 'almost' lower triangular in the sense that it is just the triangle below the diagonal. So for example a 4-by-4 distance matrix appears as, 1 2 3 4 5 6 i.e. it looks like a lower triangular of a 3-by3. I thought I might be able

Your message doesn't help us very much. You haven't said what kind of calculation it is you want to do, and that certainly matters. For example, for some kinds of computations the solution you started below would work fine: > M <- matrix(1:16, 4, 4) > is.na(diag(M)) <- TRUE > M [,1] [,2] [,3] [,4] [1,] NA 5 9 13 [2,] 2 NA 10 14 [3,] 3 7 NA

Hello and sorry that I still haven?t found a solution for my problem. I need to extract the lower and upper triangle from a square matrix including the diagonal. This diagonal is not zero in that special case. I tried with as.dist w<-as.dist(w, diag = TRUE) > w 1 2 3 4 5 1 0 2 2 0 3 3 8 0 4 4 9 14 0 5 5 10 15 20 0 but found no way to keep the diagonal that is in the

Hello, I am in need of help in putting histograms on the diagonal of a plot produced with splom(). The plot matrix I am trying to produce is to have standard scatterplots in the upper-left triangle, contour plots in the lower-right triangle, and histograms on the diagonal. I have a function that does the first two, but the histograms on the diagonal has been beyond my ability. Here is my

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Hi, Sorry if this is a repost. I searched but found no results. I am wondering if it is an easy way to construct the following matrix: r 1 0 0 0 r^2 r 1 0 0 r^3 r^2 r 1 0 r^4 r^3 r^2 r 1 where r could be any number. Thanks. Wen [[alternative HTML version deleted]]

r-devel list, I have recently written an R package that solves a linear least squares problem, and computes the multivariate normal density function. The bulk of the code is written in C, with interfacing code to the BLAS and Lapack libraries. The motivation here is speed. I ran into a problem computing the determinant of a symmetric matrix in packed storage. Apparently, there are no explicit

from ?eigen symmetric: if 'TRUE', the matrix is assumed to be symmetric (or Hermitian if complex) and only its lower triangle is used. If 'symmetric' is not specified, the matrix is inspected for symmetry. I think that could mislead a naive reader as it suggests that, with symmetric=TRUE, the result of eigen() (vectors and values) depends only on

Dear List: I am working to construct a matrix of a particular form. For the most part, developing the matrix is simple and is built as follows: vl.mat<-matrix(c(0,0,0,0,0,64,0,0,0,0,64,0,0,0,0,64),nc=4) Now to expand this matrix to be block-diagonal, I do the following: sample.size <- 100 # number of individual students I<- diag(sample.size) bd.mat<-kronecker(I,vl.mat) This

Hi, I want to compute the lower triangle of a square matrix (optionally, sans diagonal). With for() loops I can do something like this: ## 5 by 5 matrix rtn for (j in 1:5) { for (k in 1:j) { if (j != k) { ## optional rtn[j, k] <- my.func(j, k) } } } I'd like to do this with apply(). Is there some way I can do this kind of 'short-circuit'? Thanks, Mark Wilkinson

Hello! I'm trying to build a lower triangular matrix (with zeros in the diagonal) from a particular dataframe. The matrix I have to construct has 203 rows and 203 columns and that makes 20503 values to be included within (that's why I can't do it manually). To illustrate the dataframe I have, I'll give you an example of a dataframe and matrix with dimensions 6x6 (to make it

This is not at all something new(*). As maintainer of the Matrix package, I don't like this inconsistency of base R's diag(). We have had the following -- forever, almost surely inherited from S and S+ : diag(x) preserves the storage mode of x for 'complex' and 'double' precision, but converts integer and logicals to double : > storage.mode(x <- 1i + 1:7);

Hi, suppose that we have a triangular upper matrix A test <- matrix(ncol = 4, nrow = 4) test[1, ] <- c(NA,1,1,1) test[2, ] <- c(NA,NA,1,1) test[3, ] <- c(NA,NA,NA,1) test[4, ] <- c(NA,NA,NA,NA) I know how quickly set diagonal value diag(test) <- 1. But how quickly set down value i.e. matrix is symmetrical? Is there in r project any quickly function? Thanks, Best Marcin

Hello, I am using Mac OS X on a power book and R 2.5.0 I try to extract a diagonal from a dissimilarity matrix made with dsvdis, with this code: diag(DiTestRR) But I get this error message: Fehler in array(0, c(n, p)) : 'dim' spezifiziert ein zu großes Array english: Error in array(0, c(n, p)) : 'dim' specifies a too big array. Is there a limit to extract diagonals? I

I'm looking to create a correlation matrix, but I have already obtained the correlations, which are stored in a vector. (Basically, I'm running a simulation which requires a correlation matrix, but I am simulating the various correlations.) My aim is to create a function that can take the vector, and fit the values into their respective locations in a correlation matrix. (The correlations

Hi, I'd like to store large covariance matrices using Matrix classes. dsyMatrix seems like the right one, but I want to specify just the upper/lower triangle and diagonal and not have to instantiate a huge n^2 vector just for the sake of having half of it ignored: Dumb example: M <- new("dsyMatrix", uplo="U", x=rnorm(1e4), Dim=as.integer(c(100, 100))) diag(M) <- 1

Here's an easy question: I'd like to convert a symmetric matrix to a vector composed of the upper.tri() part of it plus the diagonal, and convert it back again. What's the best way to achieve this? I'm wondering if there are some built in functions to do this easily. I can encode fine: v <- c(diag(A),A[upper.tri(A)]) but I don't see an easy way to recover A from v

Is there an easy way to turn a vector of length n into an n by n matrix, in which the diagonal equals the vector, the first off diagonal equals the first order differences, the second... etc. I.e. to do this more efficiently: diffmatrix <- function(x){ n <- length(x); M <- diag(x); for(i in 1:(n-1)){ differences <- diff(x, dif=i); for(j in 1:length(differences)){ M[j,i+j]

Hello, everybody, I hope somebody could help me with a dist() function. I have a data frame of size 2*4087 (col*row), where col corresponds to the treatment and rows are species, values are Hellinger distances, I should reconstruct a distance matrix with a dist() function. I know that "euclidean" method should be used. When I type: dist(dframe,"euclidean") it gives me a

Hi R user I need to read in some values from a computer program output. I can't change the output format because the developer of the program doesn't allow to change the format of output. There are two formats. First one looks like this if I have 10 variables, ------------------------------------------------------------------------------------------------------ [ 1]