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Volume 57, issue 1
Arch. Anim. Breed., 57, 26, 2015
https://doi.org/10.7482/0003-9438-57-026
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Arch. Anim. Breed., 57, 26, 2015
https://doi.org/10.7482/0003-9438-57-026
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Jan 2015

29 Jan 2015

Analysis of correlated count data using generalised linear mixed models exemplified by field data on aggressive behaviour of boars

Norbert Mielenz1, Joachim Spilke1, and Eberhard von Borell2 Norbert Mielenz et al.
  • 1Working group Biometry and Agricultural Informatics
  • 2Department of Animal Husbandry and Livestock Ecology, Institute for Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Germany

Abstract. Population-averaged and subject-specific models are available to evaluate count data when repeated observations per subject are present. The latter are also known in the literature as generalised linear mixed models (GLMM). In GLMM repeated measures are taken into account explicitly through random animal effects in the linear predictor. In this paper the relevant GLMMs are presented based on conditional Poisson or negative binomial distribution of the response variable for given random animal effects. Equations for the repeatability of count data are derived assuming normal distribution and logarithmic gamma distribution for the random animal effects. Using count data on aggressive behaviour events of pigs (barrows, sows and boars) in mixed-sex housing, we demonstrate the use of the Poisson »log-gamma intercept«, the Poisson »normal intercept« and the »normal intercept« model with negative binomial distribution. Since not all count data can definitely be seen as Poisson or negative-binomially distributed, questions of model selection and model checking are examined. Emanating from the example, we also interpret the least squares means, estimated on the link as well as the response scale. Options provided by the SAS procedure NLMIXED for estimating model parameters and for estimating marginal expected values are presented.

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