Hi Folks, I want to do multivariate regression in R, i.e. basically (but with a complication -- see below): given an Nxp matrix Y of p-variate responses, and an Nxk matrix X of covariates, to fit the model Y = X*B + e with estimation of the kxp matrix of coefficients B and estimation of the pxp matrix of covariances between the p variates in Y. I haven't managed to find a function/package in R which seems to address this problem directly (maybe I'm overlooking a way of using a standard one). One way, of course, could be to stack the columns of Y on top of each other, replicate X vertically accordingly, and try to introduce a suitably structured covariance matrix; but I would like to think that there's an easier way ... ! The complication: for each row of Y, each of the p variates is associated with one level of a p-level factor W (on a permuted basis, so that y1,...,yp are associated with levels i1,...,ip of W where (i1,...,ip) is a permutation of levels (1,...,p) of W). I'm wondering, too, how to represent this in R. In the univariate case, for instance, the matrix representation for a factor when regressing y on r levels of a factor F represents the factor as an Nxr matrix with zeros except for 1s in col j for rows where y has the level j of F. In the multivariate case, each row of Y would by analogy be associated with a matrix of factor levels, one row for each variate in Y, so as to pick out the factor levels by columns as in the univariate case for that variate. Any help/advice would be much appreciated! With thanks, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk> Fax-to-email: +44 (0)870 167 1972 Date: 16-Jan-03 Time: 15:16:12 ------------------------------ XFMail ------------------------------