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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-054.zip
trafo, car, rcompanio, bestNormalize, caret, Johnson, jtrans, MASS, AID, Ecdat
Econometrics, Multivariate, SocialSciences, TeachingStatistics, Distributions, Environmetrics, Finance, HighPerformanceComputing, MachineLearning, NumericalMathematics, Psychometrics, Robust, TimeSeries
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Medina, et al., "The R Package trafo for Transforming Linear Regression Models", The R Journal, 2020
BibTeX citation
@article{RJ-2019-054,
author = {Medina, Lily and Kreutzmann, Ann-Kristin and Rojas-Perilla, Natalia and Castro, Piedad},
title = {The R Package trafo for Transforming Linear Regression Models},
journal = {The R Journal},
year = {2020},
note = {https://doi.org/10.32614/RJ-2019-054},
doi = {10.32614/RJ-2019-054},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {99-123}
}