Generalised Linear Mixed Models

2006 ◽  
pp. 107-152 ◽  
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
Generalised Linear Mixed Models

Spatial generalised linear mixed models based on distances

2016 ◽  
Vol 25 (5) ◽  
pp. 2138-2160
Author(s):  
Oscar O Melo ◽  
Jorge Mateu ◽  
Carlos E Melo
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models ◽  
Generalised Linear Mixed Models

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

2015 ◽  
Vol 57 (1) ◽  
pp. 1-19
Author(s):  
Norbert Mielenz ◽  
Joachim Spilke ◽  
Eberhard von Borell
Keyword(s):  
Negative Binomial Distribution ◽  
Count Data ◽  
Binomial Distribution ◽  
Mixed Models ◽  
Aggressive Behaviour ◽  
Negative Binomial ◽  
Linear Mixed Models ◽  
Model Parameters ◽  
Generalised Linear Mixed Models ◽  
Animal Effects

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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