Petr, This is the code I used for your suggestion: k<-6;kk<-(k*(k-1))/2 x<-matrix(0,5000,kk) for(i in 1:5000){ A.1<-matrix(0,k,k) rs<-runif(kk,min=-1,max=1) A.1[lower.tri(A.1)]<-rs A.1[upper.tri(A.1)]<-t(A.1)[upper.tri(A.1)] cors.i<-diag(k) t<-.001-min(Re(eigen(A.1)$values)) new.cor<-cov2cor(A.1+(t*cors.i)) x[i,]<-new.cor[lower.tri(new.cor)]} hist(c(x)); max(c(x)); median(c(x))
This, unfortunately, does not maintain the desired distribution of correlations. I did, however, learn some neat coding tricks (that were new for me) along the way. Ned -- On Thu, Jun 02, 2011 at 04:42:59PM -0700, Ned Dochtermann wrote: > 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. > ... > > Any recommendations on how to generate the desired covariance matrices would > be appreciated. Hello. Let me suggest the following procedure. 1. Generate a symmetric matrix A with the desired distribution of the non-diagonal elements and with zeros on the diagonal. 2. Compute the smallest eigenvalue lambda_1 of A. 3. Replace A by A + t I, where I is the identity matrix and t is a number such that t + lambda_1 > 0. The resulting matrix will have the same non-diagonal elements as A, but will be positive definite. Petr Savicky. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.