In quantile regression, various quantiles of a response variable Y are modelled as func tions 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 regres sion 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.
quantreg, quantregGrowth, QuantifQuantile, rgl, quantregGrowth
Environmetrics, Econometrics, Graphics, Multivariate, Optimization, ReproducibleResearch, Robust, SocialSciences, SpatioTemporal, Survival
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For attribution, please cite this work as
Charlier, et al., "QuantifQuantile: An R Package for Performing Quantile Regression Through Optimal Quantization", The R Journal, 2015
BibTeX citation
@article{RJ-2015-021,
author = {Charlier, Isabelle and Paindaveine, Davy and Saracco, Jérôme},
title = {QuantifQuantile: An R Package for Performing Quantile Regression Through Optimal Quantization},
journal = {The R Journal},
year = {2015},
note = {https://doi.org/10.32614/RJ-2015-021},
doi = {10.32614/RJ-2015-021},
volume = {7},
issue = {2},
issn = {2073-4859},
pages = {65-80}
}