Visualizing Hypothesis Tests in Multivariate Linear Models

John Fox (McMaster University), Michael Friendly (York University), and
Georges Monette (York University)

Hypothesis-error (or "HE") plots, introduced by Friendly (2006, 2007),
permit the visualization of hypothesis tests in multivariate linear models
by representing hypothesis and error matrices of sums of squares and
cross-products as ellipses. This paper describes the implementation of these
methods in R, as well as their extension, for example from two to three
dimensions and by scaling hypothesis ellipses and ellipsoids in a natural
manner relative to error. The methods, incorporated in the heplots package
for R, exploit new facilities in the car package for testing linear
hypotheses in multivariate linear models and for constructing MANOVA tables
for these models, including models for repeated measures.