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Model Selection and Validation for Yield Trials with Interaction

Hugh G. Gauch, Jr.
Biometrics
Vol. 44, No. 3 (Sep., 1988), pp. 705-715
DOI: 10.2307/2531585
Stable URL: http://www.jstor.org/stable/2531585
Page Count: 11
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Abstract

The additive main effects and multiplicative interaction (AMMI) model first applies the additive analysis of variance (ANOVA) model to two-way data, and then applies the multiplicative principal components analysis (PCA) model to the residual from the additive model, that is, to the interaction. AMMI analysis of yield trial data is a useful extension of the more familiar ANOVA, PCA, and linear regression procedures, particularly given a large genotype-by-environment interaction. Model selection and validation are considered from both predictive and postdictive perspectives, using data splitting and F-tests, respectively. A New York soybean yield trial serves as an example.

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