Testing multiplicative terms in AMMI and GGE models for multienvironment trials with replicates
Testing multiplicative terms in AMMI and GGE models for multienvironment trials with replicates
复制标题
使用重复项测试 AMMI 和 GGE 模型中的乘法项，以进行多环境试验
DOI：
10.1007/s00122-019-03339-8
复制
发表时间：
2019
影响因子：
5.4
通讯作者：
Piepho
中科院分区：
文献类型：
--
作者：
Forkman;Piepho
为了分析具有重复的多环境试验的关键信息，提出了一种基于重抽样的AMMI和GGE模型中乘性交互作用项的显著性检验方法，该方法在对方差异质性的稳健性方面优于竞争方法。摘要多环境试验数据分析常用的方法有加性主效应和乘性互作模型、基因主效应和基因-环境互作模型。农学家和植物育种者经常使用这些模型在不同的环境和/或年份重复进行品种试验。在这些模型中，决定保留多少重要的乘性相互作用项是至关重要的。当复制数据可用时，已经为此目的提出了几个检验；然而，所有检验都假定误差服从具有齐次方差的正态分布。在这里，我们提出了基于重采样的方法，用于多环境试验数据的重复，而不是这些分布假设。将这些方法与竞争参数检验进行了比较。在基于两个多环境试验的广泛的仿真研究中，发现所提出的方法在I类错误率方面表现良好，而不考虑错误的分布。当方差的正态和齐性假设被违反时，所提出的方法甚至优于稳健性检验。
Key messageFor analysing multienvironment trials with replicates, a resampling-based method is proposed for testing significance of multiplicative interaction terms in AMMI and GGE models, which is superior compared to contending methods in robustness to heterogeneity of variance.AbstractThe additive main effects and multiplicative interaction model and genotype main effects and genotype-by-environment interaction model are commonly used for the analysis of multienvironment trial data. Agronomists and plant breeders are frequently using these models for cultivar trials repeated across different environments and/or years. In these models, it is crucial to decide how many significant multiplicative interaction terms to retain. Several tests have been proposed for this purpose when replicate data are available; however, all of them assume that errors are normally distributed with a homogeneous variance. Here, we propose resampling-based methods for multienvironment trial data with replicates, which are free from these distributional assumptions. The methods are compared with competing parametric tests. In an extensive simulation study based on two multienvironment trials, it was found that the proposed methods performed well in terms of Type-I error rates regardless of the distribution of errors. The proposed method even outperforms the robusttest when the assumptions of normality and homogeneity of variance are violated.
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DOI：
10.1007/s13253-011-0063-9
发表时间：
2012-04-01
影响因子：
1.4
作者：
Perez-Elizalde, Sergio;Jarquin, Diego;Crossa, Jose
通讯作者：
Crossa, Jose
基因型 × 环境相互作用的双图分析：谨慎进行
影响因子：
2.3
作者：
Rong‐Cai Yang;J. Crossa;P. Cornelius;J. Burgueño
通讯作者：
J. Burgueño
影响因子：
2.3
作者：
Hadasch, Steffen;Forkman, Johannes;Piepho, Hans-Peter
通讯作者：
Piepho, Hans-Peter
AMMI 和 GGE 模型的加权估计
DOI：
10.1007/s13253-018-0323-z
发表时间：
2018
期刊：
Journal of Agricultural, Biological and Environmental Statistics
影响因子：
--
作者：
Hadasch;Forkman;Piepho
通讯作者：
Piepho
用于测试多环境试验 AMMI 和 GGE 模型中乘法项的非参数重采样方法
影响因子：
2.3
作者：
Hadasch;Forkman;Piepho
通讯作者：
Piepho