Rob_data

<p>A matrix containing the simulated genetic (G) effects (the first 20 columns), environmental (E)
effects (column 21 to column 24), continuous response (column 25), logarithm of survival time
(column 26), and censoring indicator (column 27).</p>

datasets

Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).

Xing Qin

GEInter

Robust Gene-Environment Interaction Analysis

Mengyun Wu

Shuangge Ma

Rob_data function

<p>A matrix with 100 rows and 27 variables.</p>

Rob_data: A matrix containing the simulated data for `RobSBoosting` and `Miss.boosting` methods

Description

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Examples