Dear friends, Sorry for this somewhat generically titled posting but I had a question with using contrasts in a manova context. So here is my question:
Suppose I am interested in doing inference on \beta in the case of the model given by: Y = X %*% \beta + e where Y is a n x p matrix of observations, X is a n x m design matrix, \beta is m x p matrix of parameters, and e is a normally-distributed random matrix with mean zero and independent rows, each having dispersion matrix given by \Sigma. Then, I know (I think) how to perform MANOVA. Specifically, I use: fit <- manova(Y ~ X) and summary(fit) will allow me to perform appropriate inference on beta. Now, suppose I am interested in doing inference on C %*% \beta %*% M (say testing whether this is equal to zero) with C and M being q x m and p x r matrices, respectively (with q, r both being no more than p), then can this be done using the manova object from the above? How? If not, is there an efficient way to do this? Many thanks in advance for all your help, and best wishes, Ranjan ______________________________________________ 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.
