IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v82y2015icp152-163.html
   My bibliography  Save this article

Ordinal Logic Regression: A classifier for discovering combinations of binary markers for ordinal outcomes

Author

Listed:
  • Wolf, Bethany J.
  • Slate, Elizabeth H.
  • Hill, Elizabeth G.

Abstract

In medicine, it is often useful to stratify patients according to disease risk, severity, or response to therapy. Since many diseases arise from complex gene–gene and gene–environment interactions, patient strata may be defined by combinations of genetic and environmental factors. Traditional statistical methods require specifying interactions a priori making it difficult to identify high order interactions. Alternatively, machine learning methods can model complex interactions, however these models are often difficult to interpret in a clinical setting. Logic regression (LR) enables modeling a binary outcome using logical combinations of binary predictors yielding easily interpretable models. However LR, as currently available, cannot model ordinal responses. This paper extends LR to model an ordinal response and the resulting method is called Ordinal Logic Regression (OLR). Several simulations comparing OLR and Classification and Regression Trees (CART) demonstrate that OLR is superior to CART for identifying variable interactions associated with an ordinal response. OLR is applied to data from a study to determine associations between genetic and health factors with severity of adult periodontitis. Ordinal Logic Regression is publicly available on CRAN in the OrdLogReg package, http://cran.r-project.org/.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:82:y:2015:i:c:p:152-163
    DOI: 10.1016/j.csda.2014.08.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016794731400245X
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2014.08.013?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Raffaella Piccarreta, 2008. "Classification trees for ordinal variables," Computational Statistics, Springer, vol. 23(3), pages 407-427, July.
    2. 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).
    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. Yulia Shichkina & Mikhail Petrov & Fatkieva Roza, 2022. "Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic," Mathematics, MDPI, vol. 10(7), pages 1-13, April.

    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. Mussini Mauro, 2018. "On Measuring Polarization For Ordinal Data: An Approach Based On The Decomposition Of The Leti Index," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 277-296, June.
    2. 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.
    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. 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.
    5. 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.
    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. 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.
    8. 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.
    9. Buczak, Philip, 2024. "fabOF: A Novel Tree Ensemble Method for Ordinal Prediction," OSF Preprints h8t4p, Center for Open Science.
    10. 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.
    11. 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).

    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:eee:csdana:v:82:y:2015:i:c:p:152-163. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.