QuantifQuantile: An R Package for Performing Quantile Regression Through Optimal Quantization

Authors

• Isabelle Charlier, Université Libre de Bruxelles, Université de Bordeaux, Inria Bordeaux Sud-Ouest
• Davy Paindaveine, Université Libre de Bruxelles
• Jérôme Saracco, Université de Bordeaux, Inria Bordeaux Sud-Ouest

Date of this Version

12-2015

Document Type

Article

Citation

The R Journal (December 2015) 7(2); Editor: Bettina Grün

Comments

Copyright 2015, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

In quantile regression, various quantiles of a response variable Y are modelled as functions of covariates (rather than its mean). An important application is the construction of reference curves/surfaces and conditional prediction intervals for Y. Recently, a nonparametric quantile regression method based on the concept of optimal quantization was proposed. This method competes very well with k-nearest neighbor, kernel, and spline methods. In this paper, we describe an R package, called QuantifQuantile, that allows to perform quantization-based quantile regression. We describe the various functions of the package and provide examples.