Comparing covariance structures using different optimization techniques in GLMM on some sexual behaviors of male lambs

dc.contributor.authorSer, Gazel
dc.contributor.authorYeşilova, Abdullah
dc.contributor.authorYılmaz, Ayhan
dc.date.accessioned2019-11-10T14:45:38Z
dc.date.available2019-11-10T14:45:38Z
dc.date.issued2013
dc.departmentBelirleneceken_US
dc.description.abstractThis study is concerned with use of generalized linear mixed models (GLMM) to analyse the repeated measurements based on count data obtained from the sexual behaviors of male lambs. A combination of different optimization techniques and covariance structures were applied to four constructed models. These models were defined in terms of random effect specifications. Therefore, residuals was assumed to be random (Model A), intercept assumed to be random effect (Model B), time (slope) assumed to be random effect (Model C) and both intercept and time assumed to be random effects (Model D). Five different techniques quasi-newton (QUANEW), newton-raphson NEWRAP), trust region (TRUREG), newton-raphson ridge (NRRIDG) and double-dogleg (DBLDOG) optimization techniques were used for analyzing these models. Three different covariance structures compound symmetry (CS), unstructured (UN) and firstorder autoregressive (AR(1)) were used. In conclusion, based on likelihood criteria, the Model A with CS structure outperformed other models for the repeated measurement data of sexual behavior characteristics.en_US
dc.identifier.issn1018-7081
dc.identifier.urihttps://hdl.handle.net/20.500.12604/1335
dc.language.isoenen_US
dc.publisherThe Journal of Animal & Plant Sciencesen_US
dc.relation.ispartofseries23(6):1583-1587;
dc.relation.publicationcategoryUluslararası Hakemli Dergi Makalesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz#KayıtKontrol#
dc.subjectNorduz male lambs, optimization techniques, subject specific modelen_US
dc.titleComparing covariance structures using different optimization techniques in GLMM on some sexual behaviors of male lambsen_US
dc.typeArticleen_US

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