Generalized Linear Mixed Model versus Transformation on Bayesian Approach
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Tarih
2018
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Linear mixed effects models (LMM) have been widely used for nearly all analysis of animal breedingdata. They are very powerful tools for the estimation of variance components and genetic parameters, and forthe prediction of genetic merit of animals. These models can only be used when some specific assumptions areprovided such as normality and constant of variances. However, if these assumptions are not provided, the datashould be transformed and the statistical analysis is carried out with the transformed data. Generalized linearmixed-effect models (GLMM) provide a solution for this problem by satisfying normality assumptions withouttransformation. This allows differences among animals to be assessed properly using the data most appropriateto the researcher's theoretical context. The aim of this study is to estimate variance components, geneticparameter of birth weight (BW), weaning weight (WW) which have economic values in animal breeding withLMM_t (with transformed data), LMM_ut (with untransformed data) and GLMM based on Bayesian approach.The present study also intended to compare the both models and the estimated parameters values obtained withLMM_t and GLMM. The data is obtained from BW and WW of 4972 Awassi lambs were born between theyears of 2012-2016. As a result, GLMM is the most suitable model for BW and WW according to DIC values.Although estimation of BW heritabilities does not change in all models, there are significant differences inestimation of WW heritabilities.
Açıklama
Anahtar Kelimeler
Ziraat Mühendisliği
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Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi
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Cilt
28
Sayı
5
