On Mar 20, 2011, at 21:05 , Ranjan Maitra wrote: > 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?
Check out anova.mlm(), it does most of this sort of testing. Not quite the "C %*% ..." bit because the linear model code is not really built to handle linear constraints, but rather compare nested models, each specified using a set of betas. (So you usually test whether a subset of betas is zero). Also check out the "car" package. Its Anova() function does some similar stuff. If noone has done so already, I wouldn't think it to be very hard to implement the general case. Most of the bits are there already. -- Peter Dalgaard Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ 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.