IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v047i10.html
   My bibliography  Save this article

Classification Trees for Ordinal Responses in R: The rpartScore Package

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v047i10/v47i10.pdf
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v047i10/rpartScore_1.0-0.tar.gz
    Download Restriction: no
    File URL: https://www.jstatsoft.org/index.php/jss/article/downloadSuppFile/v047i10/v47i10.R
    Download Restriction: no
    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v047.i10?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mussini Mauro, 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.
    2. Buczak, Philip, 2024. "fabOF: A Novel Tree Ensemble Method for Ordinal Prediction," OSF Preprints h8t4p, Center for Open Science.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roberta Siciliano & Antonio D’Ambrosio & Massimo Aria & Sonia Amodio, 2017. "Analysis of Web Visit Histories, Part II: Predicting Navigation by Nested STUMP Regression Trees," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 473-493, October.
    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. Mariangela Sciandra & Antonella Plaia & Vincenza Capursi, 2017. "Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 641-655, March.
    4. Mussini Mauro, 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.
    5. Massimo Aria & Antonio D’Ambrosio & Carmela Iorio & Roberta Siciliano & Valentina Cozza, 2020. "Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images," Statistical Papers, Springer, vol. 61(4), pages 1645-1661, August.
    6. Sam Asher & Denis Nekipelov & Paul Novosad & Stephen P. Ryan, 2016. "Classification Trees for Heterogeneous Moment-Based Models," NBER Working Papers 22976, National Bureau of Economic Research, Inc.
    7. Carmela Cappelli & Francesca Iorio & Angela Maddaloni & Pierpaolo D’Urso, 2021. "Atheoretical Regression Trees for classifying risky financial institutions," Annals of Operations Research, Springer, vol. 299(1), pages 1357-1377, April.
    8. George Petrakos & Claudio Conversano & Gregory Farmakis & Francesco Mola & Roberta Siciliano & Photis Stavropoulos, 2004. "New ways of specifying data edits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 249-274, May.
    9. 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.
    10. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.
    11. Antonio D’Ambrosio & Massimo Aria & Roberta Siciliano, 2012. "Accurate Tree-based Missing Data Imputation and Data Fusion within the Statistical Learning Paradigm," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 227-258, July.
    12. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:047:i10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.