Multiple comparison procedures in linear models

Frank Bretz, Torsten Hothorn, Peter Westfall

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations


Multiplicity is a difficult and ubiquitous problem. The problem of evaluating multiple experimental questions occurs in many areas of applications, such as, for example, in clinical trials assessing more than one outcome variable, or in agricultural field experiments comparing several irrigation systems. If multiple null hypotheses are tested simultaneously, the probability of declaring effects when none exists increases beyond the nominal type I error level used for the individual comparisons. In this paper we review multiple comparison procedures in the linear model framework. We use the multcomp package from R to illustrate the methods with a linear regression example.

Original languageEnglish
Title of host publicationCOMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium
PublisherSpringer Berlin Heidelberg
Number of pages9
ISBN (Print)9783790820836
StatePublished - 2008
Event18th Symposium on Computational Statistics, COMPSTAT 2008 - Porto, Portugal
Duration: Aug 24 2008Aug 29 2008

Publication series

NameCOMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium


Conference18th Symposium on Computational Statistics, COMPSTAT 2008


  • Multcomp
  • Multiple testing
  • Multiplicity
  • Multivariate t
  • R


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