An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach.
An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach.
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DOI：
10.1186/1471-2210-10-6
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发表时间：
2010-06-07
期刊：
影响因子：
--
通讯作者：
Neumeyer N
中科院分区：
文献类型：
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作者：
Spiess AN;Neumeyer N
在数学文献中早就知道，决定系数R2不足以衡量非线性模型的拟合优度。尽管如此，它仍然经常用于药理学和生物化学文献中，用于分析和解释数据的非线性拟合。密集的模拟方法破坏了以前的观察结果，并强调了R2作为模型有效性和性能的基础时，应用于药理学/生化非线性数据的极低性能。事实上，基于赤池权重的“真实”模型具有高达500倍的证据强度，这仅反映在R2的第三到第五位小数中。此外，即使是偏差校正的R2 adj也表现出对更高参数化模型的极端偏差。偏差校正的AICc和BIC在这方面表现得更好。研究人员和评审人员应该意识到，R2在用于证明某个非线性模型的性能或有效性时是不合适的。理想情况下，它应该从处理非线性模型拟合的科学文献中删除，或者至少用其他方法（如AIC或BIC）进行补充，或者在其他模型的背景下使用。
It is long known within the mathematical literature that the coefficient of determination R2 is an inadequate measure for the goodness of fit in nonlinear models. Nevertheless, it is still frequently used within pharmacological and biochemical literature for the analysis and interpretation of nonlinear fitting to data. The intensive simulation approach undermines previous observations and emphasizes the extremely low performance of R2 as a basis for model validity and performance when applied to pharmacological/biochemical nonlinear data. In fact, with the 'true' model having up to 500 times more strength of evidence based on Akaike weights, this was only reflected in the third to fifth decimal place of R2. In addition, even the bias-corrected R2adj exhibited an extreme bias to higher parametrized models. The bias-corrected AICc and also BIC performed significantly better in this respect. Researchers and reviewers should be aware that R2 is inappropriate when used for demonstrating the performance or validity of a certain nonlinear model. It should ideally be removed from scientific literature dealing with nonlinear model fitting or at least be supplemented with other methods such as AIC or BIC or used in context to other models in question.