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Ordinal models and generalized estimating equations to evaluate disease severity

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  • JosE Eduardo Corrente
  • Maria Del Pilar DIAz

Abstract

Many assays have been carried out in Capsicum spp. in order to evaluate its resistance to Phytophthora capsici , which causes blight and considerable yield loss. An assay aiming at the selection of resistant pepper and bell pepper genotypes to P. capsici was jointly performed in the laboratory of the Phytopathological Clinic of Entomology, Phytopathology and Agricultural Zoology and in the experimental area of the Plant Production Department, both located at ESALQ, University of Sao Paulo, Brazil. The data set for this assay comes from ordinal categorized random variables, whose analysis does not generally take into account the ordinal nature of the responses, but instead, builds indexes, among other measures, in order to evaluate the resistance of the studied genotypes. This work presents ordinal generalized linear fits in order to evaluate blight severity as well as to identify and select new resources to the pathogen. It also analyses the estimating equations proposed by Liang & Zeger (1986a, b) in order to obtain an infection pattern for the disease. From the fit of the cumulative logit models, valuable genotypes are identified for genetic breeding programs.

Suggested Citation

  • JosE Eduardo Corrente & Maria Del Pilar DIAz, 2003. "Ordinal models and generalized estimating equations to evaluate disease severity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(4), pages 425-439.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:4:p:425-439
    DOI: 10.1080/0266476032000035458
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    References listed on IDEAS

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    1. Alicia Y. Toledano & Constantine Gatsonis, 1999. "Generalized Estimating Equations for Ordinal Categorical Data: Arbitrary Patterns of Missing Responses and Missingness in a Key Covariate," Biometrics, The International Biometric Society, vol. 55(2), pages 488-496, June.
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    Cited by:

    1. Kathryn Bartimote-Aufflick & Peter C. Thomson, 2011. "The analysis of ordinal time-series data via a transition (Markov) model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1883-1897, September.

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