Springer: An R package for bi-level variable selection of high-dimensional longitudinal data

Fei Zhou; Yuwen Liu; Jie Ren; Weiqun Wang; Cen Wu

doi:10.3389/fgene.2023.1088223

Springer: An R package for bi-level variable selection of high-dimensional longitudinal data

Front Genet. 2023 Apr 6:14:1088223. doi: 10.3389/fgene.2023.1088223. eCollection 2023.

Authors

Fei Zhou¹, Yuwen Liu¹, Jie Ren², Weiqun Wang³, Cen Wu¹

Affiliations

¹ Department of Statistics, Kansas State University, Manhattan, KS, United States.
² Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN, United States.
³ Department of Food, Nutrition, Dietetics and Health, Kansas State University, Manhattan, KS, United States.

Abstract

In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene-environment interaction studies. This study introduces "springer," an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package "springer" has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the "springer" package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package "springer" is available at https://cran.r-project.org/package=springer.

Keywords: bi-level variable selection; generalized estimating equation; gene–environment interaction; quadratic inference function; repeated measurements.