The R Package trafo for Transforming Linear Regression Models

Authors

Lily Medina, Freie Universität Berlin
Ann-Kristin Kreutzmann, Freie Universität Berlin
Natalia Rojas-Perilla, Freie Universität Berlin
Piedad Castro, Freie Universität Berlin

Date of this Version

12-2019

Document Type

Article

Citation

The R Journal (December 2019) 11(2); Editor: Michael J. Kane

Comments

Copyright 2019, The R Foundation. Open access material. License: CC BY 4.0 International

Abstract

Researchers and data-analysts often use the linear regression model for descriptive, predictive, and inferential purposes. This model relies on a set of assumptions that, when not satisfied, yields biased results and noisy estimates. A common problem that can be solved in many ways – use of less restrictive methods (e.g. generalized linear regression models or non-parametric methods ), variance corrections or transformations of the response variable just to name a few. We focus on the latter option as it allows to keep using the simple and well-known linear regression model. The list of transformations proposed in the literature is long and varies according to the problem they aim to solve. Such diversity can leave analysts lost and confused. We provide a framework implemented as an R-package, trafo, to help select suitable transformations depending on the user requirements and data being analyzed. The package trafo contains a collection of selected transformations and estimation methods that complement and increase the breadth of methods that exist in R.