search for: brynedal

...xe), "-help > /dev/null")) if (rc != 0) stop("sorry, 'gs' cannot be found") } I cant figure out how to fix this. I am not an experienced programmer. Any help or tips would be greatly appreciated. Thank you, Boel --~*~**~***~*~***~**~*~-- Boel Brynedal, MSc, PhD student Karolinska Institutet Department of Clinical neuroscience

Hi, I want to simulate a data set with similar covariance structure as my observed data, and have calculated a covariance matrix (dimensions 8368*8368). So far I've tried two approaches to simulating data: rmvnorm from the mvtnorm package, and by using the Cholesky decomposition (http://www.cerebralmastication.com/2010/09/cholesk-post-on-correlated-random-normal-generation/). The problem is

Hi, I have a 900,000,000*9,000 matrix where I need to calculate the correlation between all entries along the smaller dimension, thus creating a 9k*9k correlation matrix. This matrix is too big to be uploaded in R, and is saved as a binary file. To access the data in the file I use mmap and some api-functions (to get all values in one row, one column, or one particular value). I'm looking for

Hi All, I want to select rows at random from a large data.frame while achieving a particular distribution defined my a given subset of this data.frame. How can I do this? More details and what I've done so far is given below. I have gene expression data and gene sets of interest. In order to look at enrichment of differential expression I'm doing a simple permutation approach: Selecting

Hi All, I'm working on analyzing a large data set, lets asume that dim(Data)=c(1000,8700). I want to calculate the canberra distance between the columns of this matrix, and using a toy example ('test' is a matrix filled with random numbers 0-1): > system.time(d<-as.matrix(dist(t(test), method = "canberra", diag = FALSE, upper = FALSE, p = 2))) user system