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Nonparametric Inference for Multivariate Data: The R Package npmv

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

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 highdimensional data with small or with large sample sizes and many or few factor levels.

Suggested Citation

  • Burchett, Woodrow W. & Ellis, Amanda R. & Harrar, Solomon W. & Bathke, Arne C., 2017. "Nonparametric Inference for Multivariate Data: The R Package npmv," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i04).
    • Handle: RePEc:jss:jstsof:v:076:i04
    DOI: http://hdl.handle.net/10.18637/jss.v076.i04
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    References listed on IDEAS

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    1. Noguchi, Kimihiro & Gel, Yulia R. & Brunner, Edgar & Konietschke, Frank, 2012. "nparLD: An R Software Package for the Nonparametric Analysis of Longitudinal Data in Factorial Experiments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i12).
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    2. Gunawardana, Asanka & Konietschke, Frank, 2019. "Nonparametric multiple contrast tests for general multivariate factorial designs," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 165-180.
    3. Annalisa Paolino & Elizabeth H. Haines & Evan J. Bailey & Dylan A. Black & Ching Moey & Fernando García-Moreno & Linda J. Richards & Rodrigo Suárez & Laura R. Fenlon, 2023. "Non-uniform temporal scaling of developmental processes in the mammalian cortex," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Justin W. Bonny & Lisa M. Castaneda, 2022. "To Triumph or to Socialize? The Role of Gaming Motivations in Multiplayer Online Battle Arena Gameplay Preferences," Simulation & Gaming, , vol. 53(2), pages 157-174, April.
    5. Patrick B. Langthaler & Riccardo Ceccato & Luigi Salmaso & Rosa Arboretti & Arne C. Bathke, 2023. "Permutation testing for thick data when the number of variables is much greater than the sample size: recent developments and some recommendations," Computational Statistics, Springer, vol. 38(1), pages 101-132, March.
    6. Aguilera, Ana M. & Acal, Christian & Aguilera-Morillo, M. Carmen & Jiménez-Molinos, Francisco & Roldán, Juan B., 2021. "Homogeneity problem for basis expansion of functional data with applications to resistive memories," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 41-51.
    7. Dennis Dobler & Sarah Friedrich & Markus Pauly, 2020. "Nonparametric MANOVA in meaningful effects," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 997-1022, August.
    8. Harrar, Solomon W. & Kong, Xiaoli, 2022. "Recent developments in high-dimensional inference for multivariate data: Parametric, semiparametric and nonparametric approaches," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

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