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rpartOrdinal: An R Package for Deriving a Classification Tree for Predicting an Ordinal Response

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  • Archer, Kellie J.

Abstract

This paper describes an R package, rpartOrdinal, that implements alternative splitting functions for fitting a classification tree when interest lies in predicting an ordinal response. This includes the generalized Gini impurity function, which was introduced as a method for predicting an ordinal response by including costs of misclassification into the impurity function, as well as an alternative ordinal impurity function due to Piccarreta (2008) that does not require the assignment of misclassification costs. The ordered twoing splitting method, which is not defined as a decrease in node impurity, is also included in the package. Since, in the ordinal response setting, misclassifying observations to adjacent categories is a less egregious error than misclassifying observations to distant categories, this package also includes a function for estimating an ordinal measure of association, the gamma statistic.

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  • Archer, Kellie J., 2010. "rpartOrdinal: An R Package for Deriving a Classification Tree for Predicting an Ordinal Response," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i07).
    • Handle: RePEc:jss:jstsof:v:034:i07
    DOI: http://hdl.handle.net/10.18637/jss.v034.i07
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    Cited by:

    1. 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.
    2. 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.
    3. Fellinghauer, Bernd & Bühlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.
    4. 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.
    5. Espitia-Escuer, Manuel A. & García-Cebrián, Lucía Isabel, 2012. "Diversificación en la configuración de los equipos de la primera división española de fútbol/Diversification in the Team Configuration of the Spanish Football First Division," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 30, pages 527-544, Agosto.

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