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

Autor: Oliveira LA; Faculty of Exact Sciences and Technology (FACET), Federal University of Grande Dourados, Dourados, Mato Grosso do Sul, Brazil., Silva CPD; Department of Statistics (DES), Federal University of Lavras, Lavras, Minas Gerais, Brazil., Silva AQD; Faculty of Exact Sciences and Technology (FACET), Federal University of Grande Dourados, Dourados, Mato Grosso do Sul, Brazil., Mendes CTE; Department of Statistics (DES), Federal University of Lavras, Lavras, Minas Gerais, Brazil., Nuvunga JJ; Department of Agriculture, Chibuto College of Business and Entrepreneurship, Eduardo Mondlane University, Chibuto, Gaza, Mozambique., Muniz JA; Department of Statistics (DES), Federal University of Lavras, Lavras, Minas Gerais, Brazil., Bueno Filho JSS; Department of Statistics (DES), Federal University of Lavras, Lavras, Minas Gerais, Brazil., Balestre M; Department of Statistics (DES), Federal University of Lavras, Lavras, Minas Gerais, Brazil.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2021 Aug 30; Vol. 16 (8), pp. e0256882. Date of Electronic Publication: 2021 Aug 30 (Print Publication: 2021).
DOI: 10.1371/journal.pone.0256882
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE