An alternative methodology for imputing missing data in trials with genotype-by-environment interaction

Abstract

A common problem in multi-environment trials arises when some genotype-by-environment combinations are missing. The aim of this paper is to propose a new deterministic imputation algorithm using a modification of the Gabriel cross-validation method. The method involves the singular value decomposition (SVD) of a matrix and was tested using three alternative component choices of the SVD in simulations based on two complete sets of real data, with values deleted randomly at different rates. The quality of the imputations was evaluated using the correlations and the mean square deviations between these estimates and the true observed values. The proposed methodology does not make any distributional or structural assumptions and does not have any restrictions regarding the pattern or mechanism of the missing data.