文章基本信息

标题：HRM: An R Package for Analysing High-dimensional Multi-factor Repeated Measures
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作者：Martin Happ ; Solomon W. Harrar ; Arne C. Bathke 等
期刊名称：R News
印刷版ISSN：1609-3631
出版年度：2018
卷号：10
期号：1
页码：534-548
语种：English
出版社：The R Foundation for Statistical Computing
摘要：High-dimensional longitudinal data pose a serious challenge for statistical inference as many test statistics cannot be computed for high-dimensional data, or they do not maintain the nominal type-I error rate, or have very low power. Therefore, it is necessary to derive new inference methods capable of dealing with high dimensionality, and to make them available to statistics practitioners. One such method is implemented in the package HRM described in this article. This new method uses a similar approach as the Welch-Satterthwaite t-test approximation and works very well for high-dimensional data as long as the data distribution is not too skewed or heavy-tailed. The package also provides a GUI to offer an easy way to apply the methods.