similar to: Creating a correlation matrix from a vector

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

I've a vector of pairwise correlations in the order low-index element precedes the high-index element, say: corr(1,2)=0.1, corr(1,3)=0.2, corr(2,3)=0.3, corr(3,4)=0.4 How can I construct the corresponding correlation matrix? I tried using the "combn"-function in "combinat" package: library(combinat) combn(c(0.1,0.2,0.3,0.4),2) , but to no avail... Thank you for your

> I am trying to produce a heatmap of pairwise correlations, but since the matrix is > symmetric, I only need either the upper or the lower triangle. I have scoured the > web and R documentation, but I have not been able to find a way to produce such a > figure. Is there a simple way to produce a heat map with only the part above or > below the diagonal? You might want to check

Can anyone explain this? I have a matrix with double components. It's taking up a lot of memory, so I want to multiply then turn it to integers. I'm pretty certain that there are only 2 decimal places, but I wanted to check by using modulo. E.g. mat = matrix(11:50/100, ncol=4,nrow=10) #Matrix with values out to the hundredths any((mat * 100)%%1!=0) But oddly enough it doesn't work.

Dear list members, Where can I find code for computing the p*p variance-covariance matrix given a vector of p variances (ordered varA, varB, ..., varp) and a vector of all possible correlations (ordered corAB, corAC, ..., corp-1,p)? I know that the covariance between 2 variables is equal to the product of their correlation and their standard deviations: corAB * varA^.5 * varB^.5 and so:

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:

Two questions about matrix indexing: Is is correct that V <- V[lower.tri(V, diag=TRUE)] returns the lower triangular of matrix V, that is: all elements above diagonal are set to zero? I understand that the triangle of matrix elements of V for which lower.tri is TRUE are returned while the others (above diagonal) are set to zero (or NA ???). If D and B are vectors of logicals, what

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 all, Suppose we have the following matrix m <- matrix(c(1,2,3,2,1,3,3,1,2), ncol = 3, byrow=T) where in each row each number occurs only once. I'd like to define a permutation, e.g. 1 -> 2, 2 -> 1, 3 -> 3 and apply it to the matrix. Thus, the following matrix should result: m.perm <- matrix(c(2,1,3,1,2,3,3,2,1), ncol = 3, byrow=T) i.e. each 1 should map to 2 and vice

Hi there, I tried this code from homepage: http://myowelt.blogspot.de/2008/04/beautiful-correlation-tables-in-r.html <http://myowelt.blogspot.de/2008/04/beautiful-correlation-tables-in-r.html> corstarsl <- function(x){ require(Hmisc) x <- as.matrix(x) R <- rcorr(x)$r p <- rcorr(x)$P ## define notions for significance levels; spacing is important. mystars <- ifelse(p <

Hello, I'm using a very large data set (n > 100,000 for 7 columns), for which I'm pretty happy dealing with pairwise-deleted correlations to populate my correlation table. E.g., a <- cor(cbind(col1, col2, col3),use="pairwise.complete.obs") ...however, I am interested in the number of cases used to compute each cell of the correlation table. I am unable to find such a

Hi there, Am I correct to believe that tetrachoric correlation is a special case of polychoric correlation when there are only two levels to the ordered factor? Thus it should be okay to use hetcor from the polycor package to build a matrix of correlations for binary variables? If this is true, how can one estimate 95% confidence intervals for the correlations? My guess would be mat =

I am a beginner and this is a naive question. I have the following data set. row column height 1 2 96 3 7 67 9 25 77 ...... I have a matrix of 50*100 data points and about 60% of them are zeros. I want to put the height data into the matrix according to their row and column numbers. does anybody have experience in setting up such matrix? Your help is highly appreciated. Jack LSU

Hi all, Here's an interesting (for me, at least!) problem I came across: I have a correlation matrix, let's say with 6 variables, A to F, as column headings and the same 6 as row headings. The matrix is filled with correlation coefficients. Therefore, the diagonal is all 1's, and each of the two triangles formed by the diagonal has the same 15 correlation coefficients. I need to

HI, I am really messing up to make a symmetrical matrix using upper.tri() & lower.tri() function. Here is my code:   > set.seed(1) > mat = matrix(rnorm(25), 5, 5) > mat            [,1]       [,2]       [,3]        [,4]        [,5] [1,] -0.6264538 -0.8204684  1.5117812 -0.04493361  0.91897737 [2,]  0.1836433  0.4874291  0.3898432 -0.01619026  0.78213630 [3,] -0.8356286  0.7383247

Hi, I have a correlation matrix of about 3000 items, i.e., a 3000*3000 matrix. For each of the 3000 items, I want to get the top 50 items that have the highest correlation with it (excluding itself) and generate a data frame with 3 columns like ("ID", "ID2", "cor"), where ID is those 3000 items each repeat 50 times, and ID2 is the top 50 correlated items with ID,

Duncan, Is this expected for x86_64-apple-darwin12 under Xcode 5? Using current llvm/compiler-rt/clang/polly/testsuite with dragonegg 3.4 branch, I see the warnings... % /sw/lib/gcc4.8/bin/gcc-4 -fplugin=/sw/lib/gcc4.8/lib/dragonegg.so -specs=/sw/lib/gcc4.8/lib/integrated-as.specs -fplugin-arg-dragonegg-enable-gcc-optzns -Ofast himenoBMTxpa.c himenoBMTxpa.c: In function ‘main’:

Is there a simple way to decompose the upper triangle of a correlation matrix to a linear list; For example: X Y Z X 1 2 3 Y 2 1 4 Z 3 4 1 so you get a list like: xy 2 XZ 3 YZ 4 I suspect you can do it with a matrix transformation, but that beyond me at present. Many thanks Mark _________________________ Department of Molecular and Human Genetics, Baylor College of Medicine,

Dear all, mydata<-data.frame(x1=c(1,4,6),x2=c(3,1,2),x3=c(2,1,3)) cor(mydata) x1 x2 x3 x1 1.0000000 -0.5960396 0.3973597 x2 -0.5960396 1.0000000 0.5000000 x3 0.3973597 0.5000000 1.0000000 I wonder if it is possible to fill only lower triangle of this correlation matrix? Using 'dist' doesn't seem to be useful as it doesnt allow to convert this table

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