Generalized linear mixed models for strawberry inflorescence data

D. Cole, B. Morgan, M. Ridout
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引用次数: 5

Abstract

Strawberry inflorescences have a variable branching structure. This paper demonstrates how the inflorescence structure can be modelled concisely using binomial logistic generalized linear mixed models. Many different procedures exist for estimating the parameters of generalized linear mixed models, including penalized likelihood, EM, Bayesian techniques, and simulated maximum likelihood. The main methods are reviewed and compared for fitting binomial logistic generalized linear mixed models to strawberry inflorescence data. Simulations matched to the original data are used to show that a modified EM method due to Steele (1996) is clearly the best, in terms of speed and mean-squared-error performance, for data of this kind.
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草莓花序数据的广义线性混合模型
草莓的花序具有可变的分枝结构。本文论证了如何用二项逻辑广义线性混合模型简明地模拟花序结构。估计广义线性混合模型的参数存在许多不同的程序,包括惩罚似然、EM、贝叶斯技术和模拟最大似然。综述和比较了草莓花序数据的二项logistic广义线性混合模型拟合的主要方法。与原始数据相匹配的模拟表明,由于Steele(1996)的改进的EM方法在速度和均方误差性能方面,对于这类数据显然是最好的。
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