Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.

The genotype main effects plus the genotype × environment interaction effects model has been widely used to analyze multi-environmental trials data, especially using a graphical biplot considering the first two principal components of the singular value decomposition of the interaction matrix. Many...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Luciano Antonio de Oliveira, Carlos Pereira da Silva, Alessandra Querino da Silva, Cristian Tiago Erazo Mendes, Joel Jorge Nuvunga, Joel Augusto Muniz, Júlio Sílvio de Sousa Bueno Filho, Marcio Balestre
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/98fb0b499e3d43cc825f7ee23a41ac55
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:98fb0b499e3d43cc825f7ee23a41ac55
record_format	dspace
spelling	oai:doaj.org-article:98fb0b499e3d43cc825f7ee23a41ac552021-12-02T20:19:21ZShrinkage in the Bayesian analysis of the GGE model: A case study with simulation.1932-620310.1371/journal.pone.0256882https://doaj.org/article/98fb0b499e3d43cc825f7ee23a41ac552021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256882https://doaj.org/toc/1932-6203The genotype main effects plus the genotype × environment interaction effects model has been widely used to analyze multi-environmental trials data, especially using a graphical biplot considering the first two principal components of the singular value decomposition of the interaction matrix. Many authors have noted the advantages of applying Bayesian inference in these classes of models to replace the frequentist approach. This results in parsimonious models, and eliminates parameters that would be present in a traditional analysis of bilinear components (frequentist form). This work aims to extend shrinkage methods to estimators of those parameters that composes the multiplicative part of the model, using the maximum entropy principle for prior justification. A Bayesian version (non-shrinkage prior, using conjugacy and large variance) was also used for comparison. The simulated data set had 20 genotypes evaluated across seven environments, in a complete randomized block design with three replications. Cross-validation procedures were conducted to assess the predictive ability of the model and information criteria were used for model selection. A better predictive capacity was found for the model with a shrinkage effect, especially for unorthogonal scenarios in which more genotypes were removed at random. In these cases, however, the best fitted models, as measured by information criteria, were the conjugate flat prior. In addition, the flexibility of the Bayesian method was found, in general, to attribute inference to the parameters of the models which related to the biplot representation. Maximum entropy prior was the more parsimonious, and estimates singular values with a greater contribution to the sum of squares of the genotype + genotype × environmental interaction. Hence, this method enabled the best discrimination of parameters responsible for the existing patterns and the best discarding of the noise than the model assuming non-informative priors for multiplicative parameters.Luciano Antonio de OliveiraCarlos Pereira da SilvaAlessandra Querino da SilvaCristian Tiago Erazo MendesJoel Jorge NuvungaJoel Augusto MunizJúlio Sílvio de Sousa Bueno FilhoMarcio BalestrePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256882 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luciano Antonio de Oliveira Carlos Pereira da Silva Alessandra Querino da Silva Cristian Tiago Erazo Mendes Joel Jorge Nuvunga Joel Augusto Muniz Júlio Sílvio de Sousa Bueno Filho Marcio Balestre Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.
description	The genotype main effects plus the genotype × environment interaction effects model has been widely used to analyze multi-environmental trials data, especially using a graphical biplot considering the first two principal components of the singular value decomposition of the interaction matrix. Many authors have noted the advantages of applying Bayesian inference in these classes of models to replace the frequentist approach. This results in parsimonious models, and eliminates parameters that would be present in a traditional analysis of bilinear components (frequentist form). This work aims to extend shrinkage methods to estimators of those parameters that composes the multiplicative part of the model, using the maximum entropy principle for prior justification. A Bayesian version (non-shrinkage prior, using conjugacy and large variance) was also used for comparison. The simulated data set had 20 genotypes evaluated across seven environments, in a complete randomized block design with three replications. Cross-validation procedures were conducted to assess the predictive ability of the model and information criteria were used for model selection. A better predictive capacity was found for the model with a shrinkage effect, especially for unorthogonal scenarios in which more genotypes were removed at random. In these cases, however, the best fitted models, as measured by information criteria, were the conjugate flat prior. In addition, the flexibility of the Bayesian method was found, in general, to attribute inference to the parameters of the models which related to the biplot representation. Maximum entropy prior was the more parsimonious, and estimates singular values with a greater contribution to the sum of squares of the genotype + genotype × environmental interaction. Hence, this method enabled the best discrimination of parameters responsible for the existing patterns and the best discarding of the noise than the model assuming non-informative priors for multiplicative parameters.
format	article
author	Luciano Antonio de Oliveira Carlos Pereira da Silva Alessandra Querino da Silva Cristian Tiago Erazo Mendes Joel Jorge Nuvunga Joel Augusto Muniz Júlio Sílvio de Sousa Bueno Filho Marcio Balestre
author_facet	Luciano Antonio de Oliveira Carlos Pereira da Silva Alessandra Querino da Silva Cristian Tiago Erazo Mendes Joel Jorge Nuvunga Joel Augusto Muniz Júlio Sílvio de Sousa Bueno Filho Marcio Balestre
author_sort	Luciano Antonio de Oliveira
title	Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.
title_short	Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.
title_full	Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.
title_fullStr	Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.
title_full_unstemmed	Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.
title_sort	shrinkage in the bayesian analysis of the gge model: a case study with simulation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/98fb0b499e3d43cc825f7ee23a41ac55
work_keys_str_mv	AT lucianoantoniodeoliveira shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation AT carlospereiradasilva shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation AT alessandraquerinodasilva shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation AT cristiantiagoerazomendes shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation AT joeljorgenuvunga shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation AT joelaugustomuniz shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation AT juliosilviodesousabuenofilho shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation AT marciobalestre shrinkageinthebayesiananalysisoftheggemodelacasestudywithsimulation
_version_	1718374242712551424

Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.

Ejemplares similares