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    A Baseline Category Logit Model for Assessing Competing Strains of Rhizobium Bacteria


    Brophy, Caroline and Connolly, John and Fagerli, I.L. and Duodu, Samuel and Svenning, Mette M. (2011) A Baseline Category Logit Model for Assessing Competing Strains of Rhizobium Bacteria. Journal of Agricultural, Biological, and Environmental Statistics, 16 (3). pp. 409-421. ISSN 1085-7117

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    Abstract

    In this paper we describe novel methodology for evaluating competition among strains of Rhizobium bacteria which can be found naturally occurring in or can be introduced into soil. Rhizobia can occupy nodules on the roots of legume plants allowing the plant to ‘fix’ atmospheric nitrogen. Our model defines competitive outcomes for a community (the multinomial count of nodules occupied by each strain at the end of a time period) relative to the past state of the community (the proportion of each strain present at the beginning of the time period) and incorporates this prior information in the analysis. Our approach for assessing competition provides an analogy to multivariate methods for continuous responses in competition studies and an alternative to univariate methods for discrete responses that respects the multivariate nature of the data. It can also handle zero values in the multinomial response providing an alternative to compositional data analysis methods, which traditionally have not been able to facilitate zero values. The proposed experimental design is based on the simplex design and the model is an extension of multinomial baseline category logit models that includes multiple offsets and random terms to allow for correlation among clustered responses. Supplemental materials for this article are available from the journal website.

    Item Type: Article
    Additional Information: Cite as: Brophy, C., Connolly, J., Fagerli, I.L. et al. JABES (2011) 16: 409. https://doi.org/10.1007/s13253-011-0058-6
    Keywords: Key Words Competition with discrete response; Compositional data analysis; Discrete multivariate analysis; Random effects; Simplex design; Zero values;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 10143
    Identification Number: https://doi.org/10.1007/s13253-011-0058-6
    Depositing User: Dr. Caroline Brophy
    Date Deposited: 23 Oct 2018 15:20
    Journal or Publication Title: Journal of Agricultural, Biological, and Environmental Statistics
    Publisher: Springer Verlag
    Refereed: Yes
    URI:

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