Modeling of different covariance structures with the Bayesian method in repeated measurements

Fatma Yardibi; Mehmet Fırat

Makale detayları

Modeling of different covariance structures with the Bayesian method in repeated measurements

Yazarlar:

Fatma Yardibi | Mehmet Fırat

Yayıncı:

Ege University

Ana Başlıklar:

Ziraat Mühendisliği (Diğer) | Küçükbaş Hayvan Yetiştirme ve Islahı | Hayvansal Üretim (Diğer)

Dergi:

Ege Üniversitesi Ziraat Fakültesi Dergisi Vol.60 Issue 4, pp.611-626

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001307218

pISSN:

1018-8851

Anahtar kelimeler:

Özet:

 Objective: The objective of this study was to obtain solutions by modeling different covariance structures with Bayesian analysis methods in repeated measurement and to show its applicability to data in animal science. Materials and Methods: This article focused on the analysis of the body weight data of 4154 weaned 8-month-old lambs. Repeated measurement analyses based on the mixed effect model were evaluated with Bayesian methods. Models were created for 12 different covariance structures. As the model comparison criterion, Deviation Information Criteria based on the relationship between the fit of the data to the model and the complexity of the model were used. Result: Among 12 different covariance structures, the unstructured covariance structure was determined as a suitable structure for the data of this study. Conclusions: It was concluded that various variance-covariance structures, such as body weight, can be easily modeled in repeated measurement data. Instead of PROC MCMC methods that require complex and computational difficulties and profound coding knowledge, it was presented a relatively user-friendly and fast procedure with its theoretical structure and demonstrated its feasibility. As a result of the literature review, this is the first study in which Bayesian methods solved a wide variety of variance-covariance structure models.

 Objective: The objective of this study was to obtain solutions by modeling different covariance structures with Bayesian analysis methods in repeated measurement and to show its applicability to data in animal science. Materials and Methods: This article focused on the analysis of the body weight data of 4154 weaned 8-month-old lambs. Repeated measurement analyses based on the mixed effect model were evaluated with Bayesian methods. Models were created for 12 different covariance structures. As the model comparison criterion, Deviation Information Criteria based on the relationship between the fit of the data to the model and the complexity of the model were used. Result: Among 12 different covariance structures, the unstructured covariance structure was determined as a suitable structure for the data of this study. Conclusions: It was concluded that various variance-covariance structures, such as body weight, can be easily modeled in repeated measurement data. Instead of PROC MCMC methods that require complex and computational difficulties and profound coding knowledge, it was presented a relatively user-friendly and fast procedure with its theoretical structure and demonstrated its feasibility. As a result of the literature review, this is the first study in which Bayesian methods solved a wide variety of variance-covariance structure models.

 Objective: The objective of this study was to obtain solutions by modeling different covariance structures with Bayesian analysis methods in repeated measurement and to show its applicability to data in animal science. Materials and Methods: This article focused on the analysis of the body weight data of 4154 weaned 8-month-old lambs. Repeated measurement analyses based on the mixed effect model were evaluated with Bayesian methods. Models were created for 12 different covariance structures. As the model comparison criterion, Deviation Information Criteria based on the relationship between the fit of the data to the model and the complexity of the model were used. Result: Among 12 different covariance structures, the unstructured covariance structure was determined as a suitable structure for the data of this study. Conclusions: It was concluded that various variance-covariance structures, such as body weight, can be easily modeled in repeated measurement data. Instead of PROC MCMC methods that require complex and computational difficulties and profound coding knowledge, it was presented a relatively user-friendly and fast procedure with its theoretical structure and demonstrated its feasibility. As a result of the literature review, this is the first study in which Bayesian methods solved a wide variety of variance-covariance structure models.

 Amaç: Bu çalışma, tekrarlı ölçümlerde farklı kovaryans yapılarını Bayes analiz yöntemleriyle modelleyerek çözümler elde etmeyi ve bunun hayvan bilimindeki verilere uygulanabilirliğini göstermeyi amaçlamaktadır. Materyal ve Yöntem: Bu makalede sütten kesilmiş 8 aylık 4154 kuzunun canlı ağırlık verileri analiz edilmiştir. Karma etki modeline dayalı tekrarlı ölçüm analizleri Bayes yöntemleri ile değerlendirilmiştir. 12 farklı kovaryans yapısı için modeller oluşturulmuştur. Model karşılaştırma kriteri olarak, verilerin modele uyumu ile modelin karmaşıklığı arasındaki ilişkiye dayalı Sapma Bilgi Kriterleri kullanılmıştır. Araştırma Bulguları: 12 farklı kovaryans yapısı arasından yapılandırılmamış kovaryans yapısının bu çalışmanın verilerine uygun yapı olduğu belirlendi. Sonuç: Tekrarlanan ölçüm verilerinde vücut ağırlığı gibi çeşitli varyans-kovaryans yapılarının kolaylıkla modellenebileceği gösterilmiştir. Karmaşık ve hesaplama zorlukları ve derin kodlama bilgisi gerektiren PROC MCMC yöntemleri yerine, nispeten kullanıcı dostu ve hızlı bir prosedür, teorik yapısıyla birlikte sunuldu ve uygulanabilirliği gösterildi. Literatür taraması sonucunda bu, Bayes yöntemlerin çok çeşitli varyans-kovaryans yapı modellerini çözdüğü ilk çalışmadır.

Makale detayları

Modeling of different covariance structures with the Bayesian method in repeated measurements

Yazarlar:

Fatma Yardibi | Mehmet Fırat

Yayıncı:

Ege University

Ana Başlıklar:

Ziraat Mühendisliği (Diğer) | Küçükbaş Hayvan Yetiştirme ve Islahı | Hayvansal Üretim (Diğer)

Dergi:

Ege Üniversitesi Ziraat Fakültesi Dergisi Vol.60 Issue 4, pp.611-626

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001307218

pISSN:

1018-8851

Mixed model

Monte Carlo methods

Prior distributions

Proc BGLIMM

MCMCMCMC

Karma model

Monte Carlo yöntemleri

Önsel dağılım

Proc BGLIMM

Sayı

Makale detayları

Yazarlar: Fatma Yardibi | Mehmet Fırat

Yayıncı: Ege University

Ana Başlıklar: Ziraat Mühendisliği (Diğer) | Küçükbaş Hayvan Yetiştirme ve Islahı | Hayvansal Üretim (Diğer)

Dergi: Ege Üniversitesi Ziraat Fakültesi Dergisi Vol.60 Issue 4, pp.611-626

Belge Tipi: Makale

Yayın Tarihi: 5/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001307218

pISSN: 1018-8851

Sayı

Yazarlar:

Fatma Yardibi | Mehmet Fırat

Yayıncı:

Ege University

Ana Başlıklar:

Ziraat Mühendisliği (Diğer) | Küçükbaş Hayvan Yetiştirme ve Islahı | Hayvansal Üretim (Diğer)

Dergi:

Ege Üniversitesi Ziraat Fakültesi Dergisi Vol.60 Issue 4, pp.611-626

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001307218

pISSN:

1018-8851