Computing optimal allocation of trials to subregions in crop‐variety testing in case of correlated genotype effects

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-07-25 DOI:10.1111/stan.12353

Maryna Prus

引用次数: 0

Abstract

The subject of this work is the allocation of trials to subregions in crop variety testing in the case of correlated genotype effects. A solution for computation of optimal allocations using the OptimalDesign package in R is proposed. The obtained optimal designs minimize linear criteria based on the mean squared error matrix of the best linear unbiased prediction of the genotype effects. The proposed computational approach allows for any kind of additional linear constraint on the designs. The results are illustrated by a real data example.

查看原文本刊更多论文

在基因型效应相关的情况下，计算作物品种测试中试验在次区域的最佳分配方案

这项工作的主题是在基因型效应相关的情况下，如何在作物品种测试中将试验分配到子区域。本文提出了一种使用 R 软件包 OptimalDesign 计算最优分配的解决方案。根据基因型效应的最佳线性无偏预测的均方误差矩阵，所获得的最优设计能最大限度地降低线性标准。所提出的计算方法允许对设计进行任何额外的线性约束。一个真实数据实例对结果进行了说明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.