R-AMMI-LM: Linear-fit Robust-AMMI model to analyze genotype-by environment interactions

Outliers are a common phenomenon when genotypes are evaluated over locations and years under field conditions and such outliers makes studying genotype-environment Interactions difficult. Robust-AMMI models which use a combination of robust fit and robust SVD approaches, denoted as ‘R-AMMI-RLM’ have been proposed to study GEI in presence of such outliers. Instead of ‘R-AMMI-RLM’ we propose a model which uses a combination of linear fit and robust SVD to study GEI in presence of outliers and we denote this model as ‘R-AMMI-LM’. Here we prove that ‘RAMMI-LM’ was superior over ‘R-AMMI-RLM’ as it recorded very low residual sum of squares and low RMSE values. Thus proposed, ‘R-AMMI-LM’ model could explain the GEI more precisely even in presence of outliers.