Implementation of StabilizedRegression, a framework for multi-environment regression. Analyzes the functional dependence of the response on a set of predictors and assess whether this functional relationship remains stable or unstable across different environments.
N. Pfister, E. Williams, R. Aebersold, P. Bühlmann: Stabilizing Variable Selection and Regression. Annals of Applied Statistics, 15(3):1220-1246. https://doi.org/10.1214/21-AOAS1487
The package is available on CRAN. To install and load the package use the following command:
install.packages(StabilizedRegression)
library(StabilizedRegression)The main functions are StabilizedRegression(), which performs a multi-environment regression.
help(StabilizedRegression)
example(StabilizedRegression)The diagnostic plots can be created by using the function SRanalysis(). This will generate an object of class 'SRanalysis' which can then be plotted with using the plot function.
help(SRanalysis)
example(SRanalysis)