Nonparametric inference for multivariate data: The R package npmv

Amanda R. Ellis, Woodrow W. Burchett, Solomon W. Harrar, Arne C. Bathke

Research output: Contribution to journalArticlepeer-review

141 Scopus citations

Abstract

We introduce the R package npmv that performs nonparametric inference for the comparison of multivariate data samples and provides the results in easy-to-understand, but statistically correct, language. Unlike in classical multivariate analysis of variance, multivariate normality is not required for the data. In fact, the different response variables may even be measured on different scales (binary, ordinal, quantitative). p values are calculated for overall tests (permutation tests and F approximations), and, using multiple testing algorithms which control the familywise error rate, significant subsets of response variables and factor levels are identified. The package may be used for low- or high-dimensional data with small or with large sample sizes and many or few factor levels.

Original languageEnglish
JournalJournal of Statistical Software
Volume76
Issue number1
DOIs
StatePublished - 2017

Bibliographical note

Publisher Copyright:
© 2017 American Statistical Association. All rights reserved.

Keywords

  • Closed testing procedure
  • Familywise error rate
  • MANOVA
  • Multiple testing
  • Permutation test
  • Randomization test
  • Rank test

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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