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

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 a...

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Bibliographic Details
Published inArchiv für Tierzucht Vol. 57; no. 1; pp. 1 - 19
Main Authors Mielenz, Norbert, Spilke, Joachim, von Borell, Eberhard
Format Journal Article
LanguageEnglish
Published Gottingen Copernicus GmbH 29.01.2015
Copernicus Publications
Subjects
Aggressive behavior
Animal husbandry
Animal models
Binomial distribution
Correlation analysis
Estimation
Evaluation
Generalized linear models
Livestock housing
Mathematical models
Methods
Normal distribution
Parameter estimation
Pigs
Probability distribution functions
Statistical analysis
Swine
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Summary: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.
ISSN:2363-9822
0003-9438
2363-9822
DOI:10.5194/aab-57-26-2015