similar to: Flattening and unflattening symmetric matrices

Greetings, I have a seemingly simple task which I have not been able to solve today. I want to construct a symmetric matrix of arbtriray size w/o using loops. The following I thought would do it: p <- 6 Rmat <- diag(p) dat.cor <- rnorm(p*(p-1)/2) Rmat[outer(1:p, 1:p, "<")] <- Rmat[outer(1:p, 1:p, ">")] <- dat.cor However, the problem is that the matrix

Hello R-users, I have a symmetric matrix of numerical values and I want to obtain those values in the upper or lower triangle of the matrix in a vector. I tried to do the job by using two for-loops but it doens't seem to be a clever way, and I'd like to know a more efficient code for a large matrix of thousands of rows and columns. Below is my code for your reference. Thanks a lot.

Hi, I am trying to work with the output of the MINE analysis routine found at http://www.exploredata.net Specifically, I am trying to read the results into a matrix (ideally an n x n x 6 matrix, but I'll settle right now for getting one column into a matrix.) The problem I have is not knowing how to take what amounts to being one half of a symmetric matrix - excluding the diagonal -

I have following matrix : a = matrix(rnorm(36), 6) Now I want to replace the lower-triangular elements with it's upper-triangular elements. That is I want to make a symmetric matrix from a. I have tried with lower.tri() and upper.tri() function, but got desired result. Can anyone please tell me how to do that?

List members, Via searches I've seen similar discussion of this topic but have not seen resolution of the particular issue I am experiencing. If my search on this topic failed, I apologize for the redundancy. I am attempting to generate random covariance matrices but would like the corresponding correlations to be uniformly distributed between -1 and 1. The approach I have been using is:

Dear all, I would like to compute power of a square non symmetric matrix. This is a part of a simulation study. Matrices are quite large (e.g., 900 by 900), and contains many 0 (more than 99 %). I have try the function mtx.exp of the Biodem package: library(Biodem) m <- matrix(0, 900, 900) i <- sample(1:900, 3000, replace = T) j <- sample(1:900, 3000, replace = T) for(x in 1:3000)

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I'm generating a symmetric correlation matrix using a data matrix as input: mat <- cor(data.mat) My question is: Is there a more memory efficient way to store this data? For instance, since: all(mat == t(mat)) every value is duplicated, and I should be able to almost half the memory usage for large matrices. Any thoughts/comments? Cheers,

So many matrices are square symmetrical (i.e. variance-covariance matrices), is there any way to get R to split the matrix on its diagonal and just return one diagonal? So if I have mat<-matrix(c(1,4,3,4,1,2,3,2,1), nrow = 3, ncol=3, byrow=TRUE) is there anyway to get the lower right diagonal instead of the entire symmetric matrix? -------------------------------------------

Hi Suppose I have a matrix (cohort are rows and years are columns) [2000] [2001] [2002] [2003] [C1] 0.01 0.03 0.02 0.09 [C2] 0.06 0.05 0.07 0.11 [C3] 0.1 0.5 0.4 0.98 [C4] 0.7 0.6 0.2 0.77 I want to extracts the diagonals to get a matrix which looks like this (C1 becomes C2 in 2002, C2 becomes C3 in 2003

Hi, Can anyone please point to how to decompose BCCB (Block-Circulant-Circulant-Block) matrices? I am interested in the derivations: I do know that this can be numerically done using 2-dimensional FFTs. Many thanks and best wishes!

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

R-3.0.1 rev 62743, binary downloaded from CRAN just now; macosx 10.8.3 Hello, eigen(symmetric=TRUE) behaves strangely when given complex matrices. The following two lines define 'A', a 100x100 (real) symmetric matrix which theoretical considerations [Bochner's theorem] show to be positive definite: jj <- matrix(0,100,100) A <- exp(-0.1*(row(jj)-col(jj))^2) A's being

Hi all, Quick question: What function can I use to draw a line in the margin of a plot? segments() and lines() both stop at the margin. In case the answer depends on exactly what I'm trying to do, see below. I'm using R v. 2.8.1 on Windows XP. Cheers, Alan I'm trying to make a horizontal barplot with a column of numbers on the right side. I'd like to put a line between the

Hi, I need to raise a correlation matrix; R; to the negative one half power. i.e. I need to find R^(-1/2) eg: if R=[{1,.4},{.4,1}], then R^(-1/2)=[{1.0681,-.2229}, {-.2229,1.0681}]where matrix=[{row1},{row2}] And are there any built in functions to do this? mtx.exp doesn't work because it's not raised to a positive integer Thank you so much. -- View this message in context:

Hi, I'm still not quite there with my H-F (G-G) correction code. I have it working for the main effects, but I just can't figure out how to do it for the effect interactions. The thing I really don't know (and can't find anything about) is how to calculate the covariance matrix for the interaction between the two (or even n) main factors. I've looked through some books

Hi, I'm still not quite there with my H-F (G-G) correction code. I have it working for the main effects, but I just can't figure out how to do it for the effect interactions. The thing I really don't know (and can't find anything about) is how to calculate the covariance matrix for the interaction between the two (or even n) main factors. I've looked through some books

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

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-

Dear R users, even if this question is not related to an issue about R, probably some of you will be able to help me. I have a square matrix of dimension k by k with alpha on the diagonal and beta everywhee else. This symmetric matrix is called symmetric compound matrix and has the form a( I + cJ), where I is the k by k identity matrix J is the k by k matrix of all ones a = alpha - beta c =

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