文章基本信息

标题：Nonparametric Inference for Multivariate Data: The R Package npmv
本地全文：下载
作者：Woodrow W. Burchett ; Amanda R. Ellis ; Solomon W. Harrar 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2017
卷号：76
期号：1
页码：1-18
语种：English
出版社：University of California, Los Angeles
摘要：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.
关键词：MANOVA;multiple testing;closed testing procedure;rank test;permutation test;randomization test;familywise error rate