The Bagplot: A Bivariate Boxplot
Abstract
We propose the bagplot, a bivariate generalization of the univariate boxplot. The key notion is the half space location depth of a point relative to a bivariate dataset, which extends the univariate concept of rank. The “depth median” is the deepest location, and it is surrounded by a “bag” containing the n/2 observations with largest depth. Magnifying the bag by a factor 3 yields the “fence” (which is not plotted). Observations between the bag and the fence are marked by a light gray loop, whereas observations outside the fence are flagged as outliers. The bagplot visualizes the location, spread, correlation, skewness, and tails of the data. It is equivariant for linear transformations, and not limited to elliptical distributions. Software for drawing the bagplot is made available for the S-Plus and MATLAB environments. The bagplot is illustrated on several datasets—for example, in a scatterplot matrix of multivariate data.
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These statistics are often depicted graphically as error bars and boxplots for univariate data, and two dimensional error bars and bagplots for bivariate data.
“These statistics are often depicted graphically as error bars and boxplots for univariate data, and two dimensional error bars and bagplots for bivariate data [109].”
From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [109]
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Nik-Zainal, S., Wedge, D. C., Alexandrov, L. B., Petljak, M., Butler, A. P., Bolli, N., Davies, H. R., Knappskog, S., Martin, S., Papaemmanuil, E., et al. (2014) · Nature Genetics · doi:10.1038/ng.2955 · Cited by 277 · see citing works
Parise, C. V., & Spence, C. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005664 · Cited by 272 · see citing works
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G., Krishnan, R., Stanley, J., Tickoo, O., et al. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462571 · Cited by 225 · see citing works
- Authors
- Rousseeuw, P. J., Ruts, I., & Tukey, J. W.
- Affiliations
- University of Antwerp (BE)
- Princeton University (US)
- ORCID
- Rousseeuw, Peter J.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1999-11
- Volume
- 53
- Issue
- 4
- Pages
- 382-387
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2043872985
- Cited by
- 568
- References
- 0
- Citations by year
- 2026: 14 · 2025: 29 · 2024: 22 · 2023: 23 · 2022: 37 · 2021: 38 · 2020: 33 · 2019: 30 · 2018: 30 · 2017: 35 · 2016: 28 · 2015: 38 · 2014: 33 · 2013: 20 · 2012: 22
- FWCI
- 6.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The American Statistician (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- outlier detection, scatterplot matrix, multivariate data visualization
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