Dear list, I have a matrix Y of multiple response variables and a matrix X of predictor variables and I would like to fit a multivariate multiple regression model and compute the R2-value to determine the overall proportion of variance of the response matrix Y that is explained by the predictor matrix X.
I have been using manova(Y ~ X) to assess the significance of the linear model. I am also using lm(Y ~ X) or lm(cbind(y1, y2, ...) ~ x1 + x2 + x3 +....) but these seem to fit separate multiple linear models to each response variable, i.e., summary(lm_object) would return a list of regression summaries for each response variable. I would actually like to fit a model on the two matrices with one as the response and the other as the predictor, and compute an R2 value of the correlation between the two matrices. Is there a built-in function in R that does this? If not, how can I compute an R2 value of a correlation between two matrices? best, Manabu -- Manabu Sakamoto, PhD School of Earth Sciences University of Bristol manabu.sakam...@googlemail.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.