Estimating and comparing thermal performance curves
Estimating and comparing thermal performance curves
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DOI：
10.1016/j.jtherbio.2006.06.002
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发表时间：
2006-10-01
影响因子：
2.7
通讯作者：
Angilletta, Michael J., Jr.
中科院分区：
文献类型：
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作者：
Angilletta, Michael J., Jr.
我将展示如何使用信息论来估计热性能曲线的形状。这种方法根据赤池信息标准（Akaike information criterion， AIC）对合理的模型进行排序，这是一种衡量模型描述因模型复杂性而打折扣的数据的能力的标准。我分析了以前发表的数据，以演示如何应用这种方法来描述热性能曲线。这个典型的分析产生了两个有趣的结果。首先，具有非常高的r(2)（修正高斯函数）的模型似乎过拟合了数据。其次，信息理论所支持的模型（高斯函数）在热性能曲线的最优性研究中得到了广泛的应用。最后，我讨论了在比较热性能曲线时回归和方差分析之间的选择，并强调了一种称为模板变异模式的优越方法。抛弃传统方法，采用信息论与变异模板模式相结合的方法，可以取得很大进展。(c) 2006 Elsevier Ltd.版权所有。
I show how one can estimate the shape of a thermal performance curve using information theory. This approach ranks plausible models by their Akaike information criterion (AIC), which is a measure of a model's ability to describe the data discounted by the model's complexity. I analyze previously published data to demonstrate how one applies this approach to describe a thermal performance curve. This exemplary analysis produced two interesting results. First, a model with a very high r(2) (a modified Gaussian function) appeared to overfit the data. Second, the model favored by information theory (a Gaussian function) has been used widely in optimality studies of thermal performance curves. Finally, I discuss the choice between regression and ANOVA when comparing thermal performance curves and highlight a superior method called template mode of variation. Much progress can be made by abandoning traditional methods for a method that combines information theory with template mode of variation. (c) 2006 Elsevier Ltd. All rights reserved.