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A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research

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  • N. R. Parsons
  • R. N. Edmondson
  • S. G. Gilmour

Abstract

Summary. Generalized estimating equations for correlated repeated ordinal score data are developed assuming a proportional odds model and a working correlation structure based on a first‐order autoregressive process. Repeated ordinal scores on the same experimental units, not necessarily with equally spaced time intervals, are assumed and a new algorithm for the joint estimation of the model regression parameters and the correlation coefficient is developed. Approximate standard errors for the estimated correlation coefficient are developed and a simulation study is used to compare the new methodology with existing methodology. The work was part of a project on post‐harvest quality of pot‐plants and the generalized estimating equation model is used to analyse data on poinsettia and begonia pot‐plant quality deterioration over time. The relationship between the key attributes of plant quality and the quality and longevity of ornamental pot‐plants during shelf and after‐sales life is explored.

Suggested Citation

  • N. R. Parsons & R. N. Edmondson & S. G. Gilmour, 2006. "A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 507-524, August.
    • Handle: RePEc:bla:jorssc:v:55:y:2006:i:4:p:507-524
    DOI: 10.1111/j.1467-9876.2006.00550.x
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    Cited by:

    1. Yuqi Tian & Bryan E. Shepherd & Chun Li & Donglin Zeng & Jonathan S. Schildcrout, 2023. "Analyzing clustered continuous response variables with ordinal regression models," Biometrics, The International Biometric Society, vol. 79(4), pages 3764-3777, December.
    2. Parsons, Nick R. & Costa, Matthew L. & Achten, Juul & Stallard, Nigel, 2009. "Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 632-641, January.
    3. Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.
    4. Touloumis, Anestis, 2015. "R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i08).
    5. Dale Bowman & E. Olusegun George, 2017. "Weighted least squares estimation for exchangeable binary data," Computational Statistics, Springer, vol. 32(4), pages 1747-1765, December.
    6. Anestis Touloumis & Alan Agresti & Maria Kateri, 2013. "GEE for Multinomial Responses Using a Local Odds Ratios Parameterization," Biometrics, The International Biometric Society, vol. 69(3), pages 633-640, September.

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