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Nonparametric inference for multivariate data : the R package npmv
VerfasserBurchett, Woodrow W. ; Ellis, Amanda R. ; Harrar, Solomon W. ; Bathke, Arne C.
Erschienen in
Journal of Statistical Software, 2017, Jg. 76, H. 4, S. 1-18
ErschienenFoundation for Open Access Statistics, 2017
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)MANOVA / multiple testing / closed testing procedure / rank test / permutation test / randomization test / familywise error rate
URNurn:nbn:at:at-ubs:3-3538 Persistent Identifier (URN)
 Das Werk ist frei verfügbar
Nonparametric inference for multivariate data [0.48 mb]
Nonparametric Inference for Multivariate Data Supplements [13.79 kb]
Zusammenfassung (Englisch)

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.