similar to: Correlation between 2 matrices but with subset of variables

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:

Hi everyone. Based on a dependent variable (y), I'm trying to generate some independent variables with a specified correlation. For this there's no problems. However, I would like that have all my "regressors" to be orthogonal (i.e. no correlation among them. For example, y = x1 + x2 + x3 where the correlation between y x1 = 0.7, x2 = 0.4 and x3 = 0.8. However, x1, x2 and x3

Hi there, I wonder if there is a way of efficiently generating a correlation matrix of two expression matrices. I want to correlate miRNA and mRNA expression and used the following code: ##dat.mi miRNA expression matrix, dat.m mRNA expression matrix nc <- nrow(dat.mi) cor.mat <- data.frame(rep(NA,nrow(dat.m))) pval.mat <- data.frame(rep(NA,nrow(dat.m))) for(i in 1:nc) { cr <- vector()

I am trying to generate a fourth vector,z, given three known and fixed vectors, x1,x2,x3 with corresponding known and fixed correlations with themeselves and with z. That is, all correlations are known and prespecified. How can I do this? Thank you, ben

Dear All, I have a matrix of size 10000 x 50. I would like to calculate all possible pair-wise correlation coefficient (5x10^7 combinations) using cor(). How can I efficiently calcualte and save the result in a matrix? Thanks in advance. Kind regards, Ezhil

Hello, If I have two correlation matrices (e.g. one for each of two treatments) and then perform cor() on those two correlation matrices is this third correlation matrix interpreted as the correlation between the two treatments? In my sample below I would interpret that the treatments are 0.28 correlated. Is this correct? > var1<- c(.000000000008, .09, .1234, .5670008, .00110011002200,

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

Hi All, I wonder if there should be one character for quote= in read.table, i.e., > args(read.table) function (file, header = FALSE, sep = "", quote = "\"'", dec = ".", ... I have a file containing the following lines, 08248-GOTERM 3'-phosphoadenosine 5'-phosphosulfate biosynthetic process 08279-GOTERM 3'-phosphoadenosine

suppose I have two sets of vectors: x1,x2,...,xN and y1,y2,...,yN. I want N correlations: cor(x1,y1), cor(x2,y2), ..., cor(xN,yN). my sets of vectors are arranged as data frames x & y (vector=column): x <- data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10)) y <- data.frame(d=rnorm(10),e=rnorm(10),f=rnorm(10)) cor(x,y) returns a _matrix_ of all pairwise correlations: cor(x,y)

Hi, After manipulate my data I have ended up with 5 different data frames with different number of observations but the same number of variables (columns) An example, if I write str(object1), I see this, data.frame': 47 obs. of 3 variables: $ ORF : Factor w/ 245 levels "YAL038W","YAL054C",..: 10 19 38 39 44 45 50 51 59 60 ... $ mRNA : num 0.891 1.148 1.202

Hi everyone, I'm trying to calculate correlation coefficients between corresponding columns in two matrices with identical dimensions but different data. The problem is that the matrices contain NAs in different locations. I am using the following code to try to calculate correlations between complete sets of data: #Code start maxcol<-ncol(mat1) for (i in 1:maxcol) {

Hello, I have a seemingly simple problem that a tab-delimited file can't be read in. > annoTranscripts <- read.table("matched.txt", sep = '\t', stringsAsFactors = FALSE) Error in scan(file, what, nmax, sep, dec, quote, skip, nlines, na.strings, : line 5933 did not have 12 elements However, all lines do have 12 columns. > lines <-

Hi there, When I make two correlations matrices of the same size from a <- cor(el[1:20]) b <- cor(fl[1:20]) how can I compare if this matrices are equal? Thanks, Martin -- Ihr Partner f?r Webdesign, Webapplikationen und Webspace. http://www.roomandspace.com/ Martin Kaffanke +43 650 4514224 -------------- next part -------------- A non-text attachment was scrubbed... Name: not available

Hi All, How do i calculate KMO for a dataset? *Dataset:---------------------* m1 m2 m3 m4 m5 m6 m7 m8 1 2 20 20 2 1 4 14 12 2 9 16 3 5 2 5 5 15 3 18 18 18 13 17 9 2 4 4 7 7 2 12 2 11 11 11 5 7 8 5 19 5 2 20 18 6 7 4 7 4 7 9 3 3 7 5 5 5 12 5 13 13 12 8 6 6 4 3 5 17 17 16 9 12 12 4 2 4 4 14 14 10 5 14

Hi all. I want to calculate partial correlations while controlling for one or more variables. That works already fine when I control for example just for x[,1] and x[,2] that gives me one single correlation matrix and i have to to it for x [,1]...x[,10]. That would give me out 10 matrices. Controlling for 2 Variables 100 matrices. how can I run a loop to get f.e the 10 or 100 matrices at once?

Dear all, I am new to plotting Taylor Diagram using plotrix package within R, hence this post. I have written a script which plots Taylor Diagram with one reference and 7 model values. However the font size, line width and line type are not clear when saving the diagram as a jpeg file. I tried the functions lty, lwd and font but no apparent change. I am attaching the script here. Any help would

I am trying to simulate a dataset using Parallel Latent CTT model and this is what i have done so far: (START) #Importing psych library for all the simulation related functions library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #Using the function to generate the data GenData <- congeneric.sim(N=500, loads =

Hi, For computing correlation among variables in a matrix, I use cor( ), but for computing the p-values I'm using cor.test in the following way: cor.p <- function(X) { res <- matrix(0, ncol(X), ncol(X)) for (i in 1:ncol(X)) for (j in 1:ncol(X)) res[i, j]<- cor.test(X[, i], X[, j])$p.value rownames(res) <- colnames(res) <- colnames(X) res } I'm just wondering if there is a

Dear List, I have some problem with my simulation code. Here is output from R: > sim.sp <- function(data,CM,n,N) + { + C <- matrix(rep(NA,N),ncol=1) + for(i in 1:N) + { + j <- n + xx <- which(colSums(CM[j,])==1) + V <- names(xx) + V <- paste(V, collapse="+") + V <- paste("SBA~", V) + rd <- round(nrow(data)*(2/3)) + d <-

Dear All, I have matrix (50000 X 60) of subjects and their responses to a set of questions. All responses are classified into categories (500). I would like to average all subject's responses for each category. I wrote a code using a for loop but is not working. Could please tell me what's wrong with the code? I guess, there is a elegant R way of doing the same thing. Thanks in advance.