Siew Leng TENG <siewlengteng <at> yahoo.com> writes: : : Hi, : : I would like to generate a correlation matrix with a : particular structure. For example, a 3n x 3n matrix : : A_(nxn) aI_(nxn) bI_(nxn) : aI_(nxn) A_(nxn) cI_(nxn) : aI_(nxn) cI_(nxn) A_(nxn) : : where : - A_(nxn) is a *specified* symmetric, positive : definite nxn matrix. : - I_(nxn) is an identity matrix of order n : - a, b, c are (any) real numbers : : Many attempts have been unsuccessful because a : resulting matrix with any a, b, c may not be a : positive definite one, and hence cannot qualify as a : correlation matrix. Trying to first generate a : covariance matrix however, does not guarantee a : corresponding correlation matrix with the above : structure. : : My larger purpose is to use this correlation matrix to : generate multivariate normal observations from the : corresponding covariance matrix (derived via cholesky : decomposition of the cor matrix).
This can be formulated a semidefinite programming problem. I don't think R has any packages that do that but a google search for "semidefinite programming" will find more info and some free non-R software which you could consider interfacing to R. ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html