Dear John, Peter and others, So, I now have a query at an even more elementary level and that is regarding my results from anova.mlm() not matching the car package's Manova(). Specifically, I have been trying the following out with regard to a simple one-way MANOVA setup. So, I try out the following using R:
******* R code ******* morel <- read.table(file = "http://www.public.iastate.edu/~maitra/stat501/datasets/morel.dat", col.names = c("studentgroup", "aptitude", "mathematics", "language", "generalknowledge")) morel[,1] <- as.factor(morel[,1]) fit <- anova.mlm(as.matrix(morel[,-1]) ~ morel[,1]) summary(fit, test="Wilks") *** providing the output *** Df Wilks approx F num Df den Df Pr(>F) morel[, 1] 2 0.54345 6.7736 8 152 1.384e-07 *** Residuals 79 *** end of output The above is correct, also by doing the calculations "by hand". Then, I use the car package, following the help function on Anova() and do the following: ******* R code ******** morel <- read.table(file = "http://www.public.iastate.edu/~maitra/stat501/datasets/morel.dat", col.names=c("studentgroup", "aptitude", "mathematics", "language", "generalknowledge")) library(car) fit1 <- Manova( lm( cbind(aptitude, mathematics, language, generalknowledge) ~ studentgroup , data = morel)) summary(fit1, test = "Wilks") ****** providing the output ***** Type II MANOVA Tests: Sum of squares and products for error: aptitude mathematics language generalknowledge aptitude 78506.68 13976.5677 11041.9434 4330.1304 mathematics 13976.57 16040.3996 3979.9528 -416.4845 language 11041.94 3979.9528 6035.6132 -372.8491 generalknowledge 4330.13 -416.4845 -372.8491 7097.9562 ------------------------------------------ Term: studentgroup Sum of squares and products for the hypothesis: aptitude mathematics language generalknowledge aptitude 1129.7271 996.0542 237.54441 -880.4353 mathematics 996.0542 878.1980 209.43741 -776.2594 language 237.5444 209.4374 49.94777 -185.1266 generalknowledge -880.4353 -776.2594 -185.12655 686.1536 Multivariate Test: studentgroup Df test stat approx F num Df den Df Pr(>F) studentgroup 1 0.8620544 3.080378 4 77 0.020805 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 ***** end of output. Which is very different from the previous results. So what am I doing wrong here? Same issues arise with the other tests also (Pillai, Roy, Hotelling-Lawley, etc). Many thanks and best wishes, Ranjan On Sun, 20 Mar 2011 19:29:41 -0500 John Fox <j...@mcmaster.ca> wrote: > Dear Peter and Ranjan, > > In addition to Anova(), linearHypothesis() in the car package handles > multivariate linear models, including those for repeated measures. > > Best, > John > > -------------------------------- > John Fox > Senator William McMaster > Professor of Social Statistics > Department of Sociology > McMaster University > Hamilton, Ontario, Canada > http://socserv.mcmaster.ca/jfox > > > > > > -----Original Message----- > > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > > On Behalf Of peter dalgaard > > Sent: March-20-11 6:50 PM > > To: Ranjan Maitra > > Cc: R-help > > Subject: Re: [R] manova question > > > > > > 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. > ______________________________________________ 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.