The R Journal: article published in 2013, volume 5:1

Hypothesis Tests for Multivariate Linear Models Using the car Package
John Fox, Michael Friendly and Sanford Weisberg , The R Journal (2013) 5:1, pages 39-52.

Abstract The multivariate linear model is Y = X B + E (n×m) (n× p)( p×m) (n×m) The multivariate linear model can be fit with the lm function in R, where the left-hand side of the model comprises a matrix of response variables, and the right-hand side is specified exactly as for a univariate linear model (i.e., with a single response variable). This paper explains how to use the Anova and linearHypothesis functions in the car package to perform convenient hypothesis tests for parameters in multivariate linear models, including models for repeated-measures data.

Received: 2012-01-13; online 2013-06-03
CRAN packages: car, lme4, nlme, survival, nnet, MASS, survey, heplots , CRAN Task Views implied by cited CRAN packages: SocialSciences, Econometrics, Environmetrics, OfficialStatistics, Psychometrics, Finance, Multivariate, Pharmacokinetics, SpatioTemporal, Survival, Bayesian, ChemPhys, ClinicalTrials, Distributions, MachineLearning, NumericalMathematics, Robust, Spatial


CC BY 4.0
This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2013-004,
  author = {John Fox and Michael Friendly and Sanford Weisberg},
  title = {{Hypothesis Tests for Multivariate Linear Models Using the
          car Package}},
  year = {2013},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2013/RJ-2013-004/index.html},
  pages = {39--52},
  volume = {5},
  number = {1}
}