Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data

Genes

Jie Ren¹  Weiqun Wang²  Cen Wu³  Fei Zhou³  Yuwen Liu³  Xiaoxi Li³ 

[1] Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA;Department of Food, Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS 66506, USA;Department of Statistics, Kansas State University, Manhattan, KS 66506, USA;

关键词: GEE;    interaction analysis;    longitudinal data;    penalized variable selection;   

DOI  :  10.3390/genes13030544

来源: DOAJ

【 摘 要 】

We introduce interep, 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 interep 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 interep is available at The Comprehensive R Archive Network (CRAN).

【 授权许可】

Unknown