IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v466y2022ics0304380022000023.html
   My bibliography  Save this article

Coefficients of association between nominal and fully ranked ordinal variables with applications to ecological network analysis

Author

Listed:
  • Podani, János
  • Patonai, Katalin
  • Szabó, Péter
  • Szilágyi, András

Abstract

A central issue of ecological data analysis is the pairwise comparison of variables describing biological entities and the environment. Difficulties arise with calculations if the measurement scales of the variables differ. In particular, no method is available for measuring the association between a nominal and a fully ranked ordinal variable. Here two coefficients are suggested by reducing this problem to the evaluation of pattern in string representations. The first one is a topological measure that counts the number of other types of elements occurring between pairs of elements of a given state along the entire length of the string, thus providing a global coefficient of aggregation/segregation. The second coefficient is based on counting the number of different elements within substrings generated from the complete string with the moving window technique. Thus, it is a local measure. There is no compact and general formula for calculating these measures, and heuristics are involved for finding the possible minimum and maximum values by algorithmic approximation and Markov Chain Monte Carlo simulation. An R function is provided for computations. The methods are applied to the comparison of nominal variables (biological traits) categorizing marine food web nodes with fully ranked variables describing major graph theory properties of the same nodes in the network. The most descriptive traits (mobility, major functional group) significantly associated with network metrics (weighted indices) were identified from a variety of combinations across three marine ecosystems. These coefficients thus provide an objective, statistically-sound method for identifying ecologically meaningful traits.

Suggested Citation

  • Podani, János & Patonai, Katalin & Szabó, Péter & Szilágyi, András, 2022. "Coefficients of association between nominal and fully ranked ordinal variables with applications to ecological network analysis," Ecological Modelling, Elsevier, vol. 466(C).
  • Handle: RePEc:eee:ecomod:v:466:y:2022:i:c:s0304380022000023
    DOI: 10.1016/j.ecolmodel.2022.109873
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022000023
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.109873?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, 2001. "A new measure of nominal-ordinal association," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(1), pages 107-120.
    2. Baak, M. & Koopman, R. & Snoek, H. & Klous, S., 2020. "A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    Full references (including those not matched with items on IDEAS)

    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. Cosimo Russo & Alberto Castro & Andrea Gioia & Vito Iacobellis & Angela Gorgoglione, 2023. "A Stormwater Management Framework for Predicting First Flush Intensity and Quantifying its Influential Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1437-1459, February.
    2. M. Perakis & P. Maravelakis & S. Psarakis & E. Xekalaki & J. Panaretos, 2005. "On Certain Indices for Ordinal Data with Unequally Weighted Classes," Quality & Quantity: International Journal of Methodology, Springer, vol. 39(5), pages 515-536, October.
    3. Tianqi Zhang & Yue Zhou & Ming Li & Haoran Zhang & Tong Wang & Yu Tian, 2022. "Impacts of Urbanization on Drainage System Health and Sustainable Drainage Recommendations for Future Scenarios—A Small City Case in China," Sustainability, MDPI, vol. 14(24), pages 1-24, December.
    4. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).
    5. Raffaella Piccarreta, 2008. "Classification trees for ordinal variables," Computational Statistics, Springer, vol. 23(3), pages 407-427, July.
    6. Cesar de Lima Nogueira, Silvio & Och, Stephan Hennings & Moura, Luis Mauro & Domingues, Eric & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2023. "Prediction of the NOx and CO2 emissions from an experimental dual fuel engine using optimized random forest combined with feature engineering," Energy, Elsevier, vol. 280(C).
    7. dos Santos Ferreira, Greicili & Martins dos Santos, Deilson & Luciano Avila, Sérgio & Viana Luiz Albani, Vinicius & Cardoso Orsi, Gustavo & Cesar Cordeiro Vieira, Pedro & Nilson Rodrigues, Rafael, 2023. "Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios," Applied Energy, Elsevier, vol. 339(C).
    8. Jialiang Cui & Vanessa Hoi Mei Cheung & Wenjie Huang & Wan Sang Kan, 2022. "Mental Distress during the COVID-19 Pandemic: A Cross-Sectional Study of Women Receiving the Comprehensive Social Security Allowance in Hong Kong," IJERPH, MDPI, vol. 19(16), pages 1-13, August.
    9. Cimpoeru Smaranda & Roman Monica & Kobeissi Amira & Mohammad Heba, 2020. "How are European Migrants from the MENA Countries Affected by COVID-19? Insights from an Online Survey," Journal of Social and Economic Statistics, Sciendo, vol. 9(1), pages 128-143, August.
    10. Zhou, Yu & Chen, Ben & Meng, Kai & Zhou, Haoran & Chen, Wenshang & Zhang, Ning & Deng, Qihao & Yang, Guanghua & Tu, Zhengkai, 2023. "Optimal design of a cathode flow field for performance enhancement of PEM fuel cell," Applied Energy, Elsevier, vol. 343(C).
    11. Alla Yu. Vladova, 2022. "Remote Geotechnical Monitoring of a Buried Oil Pipeline," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
    12. Rosaria Lombardo & Eric Beh & Antonello D'Ambra, 2011. "Studying the dependence between ordinal-nominal categorical variables via orthogonal polynomials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2119-2132.
    13. Choi, Insu & Lee, Myounggu & Kim, Hyejin & Kim, Woo Chang, 2023. "Elucidating Directed Statistical Dependencies: Investigating Global Financial Market Indices' Influence on Korean Short Selling Activities," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    14. Giuseppe Bove & Pier Luigi Conti & Daniela Marella, 2021. "A measure of interrater absolute agreement for ordinal categorical data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 927-945, September.
    15. Yuan Liu & Chuyao Liao & Li Zhuo & Haiyan Tao, 2022. "Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China," IJERPH, MDPI, vol. 19(16), pages 1-17, August.
    16. Janitza, Silke & Tutz, Gerhard & Boulesteix, Anne-Laure, 2016. "Random forest for ordinal responses: Prediction and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 57-73.

    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:ecomod:v:466:y:2022:i:c:s0304380022000023. 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.journals.elsevier.com/ecological-modelling .

    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.