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Classification Trees for Ordinal Responses in R: The rpartScore Package

Author

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  • Galimberti, Giuliano
  • Soffritti, Gabriele
  • Maso, Matteo Di

Abstract

This paper introduces rpartScore (Galimberti, Soffritti, and Di Maso 2012), a new R package for building classification trees for ordinal responses, that can be employed whenever a set of scores is assigned to the ordered categories of the response. This package has been created to overcome some problems that produced unexpected results from the package rpartOrdinal (Archer 2010). Explanations for the causes of these unexpected results are provided. The main functionalities of rpartScore are described, and its use is illustrated through some examples.

Suggested Citation

  • Galimberti, Giuliano & Soffritti, Gabriele & Maso, Matteo Di, 2012. "Classification Trees for Ordinal Responses in R: The rpartScore Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i10).
    • Handle: RePEc:jss:jstsof:v:047:i10
    DOI: http://hdl.handle.net/10.18637/jss.v047.i10
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    References listed on IDEAS

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    1. Cappelli, Carmela & Mola, Francesco & Siciliano, Roberta, 2002. "A statistical approach to growing a reliable honest tree," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 285-299, January.
    2. Raffaella Piccarreta, 2008. "Classification trees for ordinal variables," Computational Statistics, Springer, vol. 23(3), pages 407-427, July.
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    Cited by:

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    2. Gerhard Tutz, 2022. "Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 241-263, July.
    3. Adolfo Morrone & Alfonso Piscitelli & Antonio D’Ambrosio, 2019. "How Disadvantages Shape Life Satisfaction: An Alternative Methodological Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 477-502, January.
    4. Wolf, Bethany J. & Slate, Elizabeth H. & Hill, Elizabeth G., 2015. "Ordinal Logic Regression: A classifier for discovering combinations of binary markers for ordinal outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 152-163.
    5. Elena Ballante & Silvia Figini & Pierpaolo Uberti, 2022. "A new approach in model selection for ordinal target variables," Computational Statistics, Springer, vol. 37(1), pages 43-56, March.
    6. Mauro Mussini, 2018. "On Measuring Polarization For Ordinal Data: An Approach Based On The Decomposition Of The Leti Index," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 277-296, June.
    7. Yuanyuan Shen & Katherine P. Liao & Tianxi Cai, 2015. "Sparse kernel machine regression for ordinal outcomes," Biometrics, The International Biometric Society, vol. 71(1), pages 63-70, March.
    8. Angela Maria D’Uggento & Alfonso Piscitelli & Nunziata Ribecco & Germana Scepi, 2023. "Perceived climate change risk and global green activism among young people," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1167-1195, October.

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