首页    期刊浏览 2024年12月01日 星期日
登录注册

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
  • 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
国家哲学社会科学文献中心版权所有