Q-Q plots allow us to assess univariate distributional assumptions by comparing a set of quantiles from the empirical and the theoretical distributions in the form of a scatterplot. To aid in the interpretation of Q-Q plots, reference lines and confidence bands are often added. We can also detrend the Q-Q plot so the vertical comparisons of interest come into focus. Various implementations of Q-Q plots exist in R, but none implements all of these features. qqplotr extends ggplot2 to provide a complete implementation of Q-Q plots. This paper introduces the plotting framework provided by qqplotr and provides multiple examples of how it can be used.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-051.zip
base, lattice, car, ggplot2, qqplotr, stats, robustbase, boot
Multivariate, Econometrics, Graphics, Robust, SocialSciences, Finance, Optimization, Phylogenetics, Survival, TimeSeries
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For attribution, please cite this work as
Almeida, et al., "ggplot2 Compatible Quantile-Quantile Plots in R", The R Journal, 2018
BibTeX citation
@article{RJ-2018-051,
author = {Almeida, Alexandre and Loy, Adam and Hofmann, Heike},
title = {ggplot2 Compatible Quantile-Quantile Plots in R},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-051},
doi = {10.32614/RJ-2018-051},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {248-261}
}