Missing value imputation in multi-environment trials: Reconsidering the Krzanowski method
Arciniegas-Alarcón, Sergio, García-Peña, Marisol, Krzanowski, Wojtek
Crop Breed. Appl. Biotechnol.
DATA DE PUBLICAÇÃO
Abstract We propose a new methodology for multiple imputation when faced with missing data in multi-environmental trials with genotype-by-environment interaction, based on the imputation system developed by Krzanowski that uses the singular value decomposition (SVD) of a matrix. Several different iterative variants are described; differential weights can also be included in each variant to represent the influence of different components of SVD in the imputation process. The methods are compared through a simulation study based on three real data matrices that have values deleted randomly at different percentages, using as measure of overall accuracy a combination of the variance between imputations and their mean square deviations relative to the deleted values. The best results are shown by two of the iterative schemes that use weights belonging to the interval [0.75, 1]. These schemes provide imputations that have higher quality when compared with other multiple imputation methods based on the Krzanowski method.
- Genotype x environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models
- Factor analysis applied to the G+GE matrix via REML/BLUP for multi-environment data
- A surplus of positive trials: weighing biases and reconsidering equipoise
- Análise multiambientes visando a recomendação regionalizada de clones de cana-de-açúcar
- Mapeamento de QTLs em múltiplos caracteres e ambientes em cruzamento comercial de cana-de-açucar usando modelos mistos