Hypothesis tests for multivariate linear models using the car package

John Fox; Michael Friendly; Sanford Weisberg

Hypothesis tests for multivariate linear models using the car package

John Fox, Michael Friendly, Sanford Weisberg

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-review

164 Scopus citations

Abstract

The multivariate linear model is Y_(n×m) = X _(n×p) B _(p×m) + E _(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.

Original language	English (US)
Pages (from-to)	39-52
Number of pages	14
Journal	R Journal
Volume	5
Issue number	1
State	Published - Sep 4 2013

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title = "Hypothesis tests for multivariate linear models using the car package",

abstract = "The multivariate linear model is Y(n×m) = X (n×p) B (p×m) + E (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.",

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AU - Weisberg, Sanford

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N2 - The multivariate linear model is Y(n×m) = X (n×p) B (p×m) + E (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.

AB - The multivariate linear model is Y(n×m) = X (n×p) B (p×m) + E (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.

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Hypothesis tests for multivariate linear models using the car package

Abstract

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