MODEL SELECTION AND VALIDATION FOR YIELD TRIALS WITH INTERACTION
MODEL SELECTION AND VALIDATION FOR YIELD TRIALS WITH INTERACTION
复制标题
DOI：
10.2307/2531585
复制
发表时间：
1988-09-01
期刊：
影响因子：
1.9
通讯作者：
GAUCH, HG
中科院分区：
文献类型：
--
作者：
GAUCH, HG
加性主效应和乘性交互作用（AMMI）模型首先将加性方差分析（ANOVA）模型应用于双向数据，然后将乘性主成分分析（PCA）模型应用于加性模型的残差，即交互作用。产量试验数据的AMMI分析是更熟悉的ANOVA、PCA和线性回归程序的有用扩展，特别是在大的基因型与环境相互作用的情况下。模型的选择和验证被认为是从预测和事后的角度来看，分别使用数据分裂和F-检验。纽约大豆产量就是一个例子。
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 serves as an example.