Model selection and its consequences for different split-plot designs with spatial covariance and trend.
Model selection and its consequences for different split-plot designs with spatial covariance and trend.
复制标题
具有空间协方差和趋势的不同裂区设计的模型选择及其后果。
DOI：
10.1111/j.1439-0523.2010.01795.x
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发表时间：
2010
期刊：
影响因子：
2
通讯作者：
H. Piepho
中科院分区：
文献类型：
--
作者：
J. Spilke;C. Richter;H. Piepho
有1张图和10张表
摘要
裂区设计在农业田间试验中发挥着重要作用，特别是在植物育种中。裂区试验的分析可采用经典的基于随机化的ANOVA模型。此外，可以对空间协方差进行建模以提高精度。本研究针对植物育种和品种测试中常见的设计场景，采用蒙特卡罗模拟方法研究了交替方差-协方差结构(随机区组、空间协方差)的性质。我们考察了用信息标准(AICC，BIC)选择模型的效率。我们的结果表明，当模型之间在经验错误率(I类误差和功率)以及效应估计(MSE)的精度方面存在显着差异时，可以预期有效的模型选择。对分母自由度的近似比较表明，对于空间模型，使用Kenward-Roger方法可以合理地控制标称I类错误率。
With 1 figure and 10 tables
Abstract
Split-plot designs play an important role in agricultural field trials, particularly in plant breeding. Analysis of split-plot experiments may be performed by classical randomization-based anova-type models. In addition, spatial covariance can be modelled to improve precision. This study investigates the properties of alternative variance–covariance structures (random blocks, spatial covariance) using Monte-Carlo simulation for design scenarios commonly found in plant breeding and variety testing. We investigate the efficiency of model selection by information criteria (AICC, BIC). Our results indicate that efficient model selection can be expected when there are marked differences among models with respect to the empirical error rates (type I error and power) as well as the precision of effect estimates (MSE). A comparison of approximations to the denominator degrees of freedom shows that for spatial models use of the Kenward–Roger method gives reasonable control of the nominal type I error rate.