In R you first need to import datafile "BodyMeasures.txt", define n to represent the sample size, and group your response variables into a set using cbind.
> bodyMeasures < read.table("/BodyMeasures.txt", header=TRUE, sep=",", na.strings="NA", dec=".")
>
n<length(bodyMeasures$Sex)
> bodyMeasures$Ys < with(bodyMeasures,cbind(Mass,Fore,Bicep,Chest,Neck,Shoulder,Waist,Height,Calf,Thigh,Head))
Perform a MANOVA, report Pillai's trace, and include univariate tests on each measure
> fit_manova < manova(Ys ~ Sex, data = bodyMeasures)
> summary(fit_manova)
> summary.aov(fit_manova)
Obtain the Matrix of error sums of squares and crossproducts (E) and the Matrix of model sums of squares and crossproducts (H) and report them
> E < (n1)*cov(fit_manova$residuals)
> E
> H < (n1)*cov(fit_manova$fitted.values)
> H
You can obtain for the Eigenvalues after solving H for E and the Eigenvectors via lda (which requires library MASS).
> roots < eigen(H%*%solve(E))
> roots$values
> library(MASS)
> fit_lda < lda(bodyMeasures$Ys,bodyMeasures$Sex)
> fit_lda
You can get a canonical centroid plot of this ananlysis using procedure cca (which requires library vegan).
> install.packages("vegan")
> library(vegan)
> cca.1 < cca(bodyMeasures[,1] ~ bodyMeasures$Sex)
> cca1.plot < plot(cca.1)
