Overcoming collinearity in path analysis of soybean [Glycine max (L.) Merr.] grain oil content.

ABSTRACT: Path analysis allows understanding the direct and indirect effects among traits. Multicollinearity in correlation matrices may cause a bias in path analysis estimates. This study aimed to: a) understand the correlation among soybean traits and estimate their direct and indirect effects on gain oil content; b) verify the efficiency of ridge path analysis and trait culling to overcome colinearity. Three different matrices with different levels of collinearity were obtained by trait culling. Ridge path analysis was performed on matrices with strong collinearity; otherwise, a traditional path analysis was performed. The same analyses were run on a simulated dataset. Trait culling was applied to matrix R originating the matrices R1 and R2. Path analysis for matrices R1 and R2 presented a high d

SUBMITTER: Viotto Del Conte M 

PROVIDER: S-EPMC7244132 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature