Nonparametric Inference for Multivariate Data: The R Package npmv

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

Journal of Statistical Software, 2017, vol. 076, issue i04

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.

Date: 2017-01-11
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:076:i04

DOI: 10.18637/jss.v076.i04

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