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Getting Things in Order: An Introduction to the R Package seriation

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  • Hahsler, Michael
  • Hornik, Kurt
  • Buchta, Christian

Abstract

Seriation, i.e., finding a suitable linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides an infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.

Suggested Citation

  • Hahsler, Michael & Hornik, Kurt & Buchta, Christian, 2008. "Getting Things in Order: An Introduction to the R Package seriation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i03).
    • Handle: RePEc:jss:jstsof:v:025:i03
    DOI: http://hdl.handle.net/10.18637/jss.v025.i03
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    3. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
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    8. Telcs, András & Kosztyán, Zsolt Tibor & Banász, Zsuzsanna & Csányi, Vivien Valéria, 2019. "Felsőoktatási ligák, parciális rangsorok képzése biklaszterezési eljárásokkal [How to rate higher education systems partial rankings using bi-clustering methods]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 905-931.
    9. Crespo Cuaresma, Jesus & Grün, Bettina & Hofmarcher, Paul & Humer, Stefan & Moser, Mathias, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Paper Series 193, WU Vienna University of Economics and Business.
    10. Piccarreta, Raffaella & Bonetti, Marco, 2019. "Assessing and comparing models for sequence data by microsimulation (with Supplementary Material)," SocArXiv 3mcfp, Center for Open Science.
    11. Hahsler, Michael, 2017. "An experimental comparison of seriation methods for one-mode two-way data," European Journal of Operational Research, Elsevier, vol. 257(1), pages 133-143.
    12. Eric C. Chi & Genevera I. Allen & Richard G. Baraniuk, 2017. "Convex biclustering," Biometrics, The International Biometric Society, vol. 73(1), pages 10-19, March.
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    15. Aliyev, Denis A. & Zirbel, Craig L., 2023. "Seriation using tree-penalized path length," European Journal of Operational Research, Elsevier, vol. 305(2), pages 617-629.
    16. Tseng, George C., 2010. "Quantile map: Simultaneous visualization of patterns in many distributions with application to tandem mass spectrometry," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1124-1137, April.
    17. Martin Junge & Rainer Reisenzein, 2015. "Maximum Likelihood Difference Scaling versus Ordinal Difference Scaling of emotion intensity: a comparison," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2169-2185, September.
    18. Piccarreta, Raffaella & Struffolino, Emanuela, 2019. "An Integrated Heuristic for Validation in Sequence Analysis," SocArXiv v7mj8, Center for Open Science.
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    20. Keefe Murphy & T. Brendan Murphy & Raffaella Piccarreta & I. Claire Gormley, 2021. "Clustering longitudinal life‐course sequences using mixtures of exponential‐distance models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1414-1451, October.
    21. Amon, Julian & Hornik, Kurt, 2022. "Is it all bafflegab? – Linguistic and meta characteristics of research articles in prestigious economics journals," Journal of Informetrics, Elsevier, vol. 16(2).
    22. Mirko Armillotta & Konstantinos Fokianos, 2024. "Count network autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 584-612, July.

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