Zhou Fei, Ren Jie, Liu Yuwen, Li Xiaoxi, Wang Weiqun, Wu Cen
Department of Statistics, Kansas State University, Manhattan, KS 66506, USA.
Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Genes (Basel). 2022 Mar 19;13(3):544. doi: 10.3390/genes13030544.
We introduce , an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties. The R package implements generalized estimating equation (GEE)-based penalization methods with this sparsity assumption. Moreover, alternative methods have also been implemented in the package. These alternative methods merely select effects on an individual level and ignore the group-level interaction structure. In this software article, we first introduce the statistical methodology corresponding to the penalized GEE methods implemented in the package. Next, we present the usage of the core and supporting functions, which is followed by a simulation example with R codes and annotations. The R package is available at The Comprehensive R Archive Network (CRAN).
我们介绍了一个用于对具有高维主效应和交互效应的重复测量数据进行交互分析的R包。在基因与环境（G×E）交互研究中，环境因素的形式在确定如何在高维场景中施加结构化稀疏性以识别重要效应方面起着关键作用。周等人（2019年）（ PMID：31816972）提出了一种纵向惩罚方法，分别选择与个体和组结构相对应的主效应和交互效应，这需要个体和组水平惩罚的混合。该R包实现了基于广义估计方程（GEE）的惩罚方法，并采用了这种稀疏性假设。此外，该包中还实现了替代方法。这些替代方法仅在个体水平上选择效应，而忽略了组水平的交互结构。在这篇软件文章中，我们首先介绍与该包中实现的惩罚GEE方法相对应的统计方法。接下来，我们展示核心函数和支持函数的用法，随后是一个带有R代码和注释的模拟示例。该R包可在综合R存档网络（CRAN）上获取。
