Why do we still use stepwise modelling in ecology and behaviour?
Why do we still use stepwise modelling in ecology and behaviour?
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DOI：
10.1111/j.1365-2656.2006.01141.x
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发表时间：
2006-09-01
影响因子：
4.8
通讯作者：
Freckleton, Robert P.
中科院分区：
文献类型：
--
作者：
Whittingham, Mark J.;Stephens, Philip A.;Freckleton, Robert P.
1. 逐步多元回归的偏差和缺点在统计文献中得到了很好的证实。然而，对2004年发表在三家主要生态学和行为学期刊上的论文的研究表明，这种技术的使用仍然很普遍：在65篇使用多元回归方法的论文中，57%的研究使用了逐步方法。逐步多元回归的主要缺点包括参数估计的偏差，模型选择算法之间的不一致性，多重假设检验的固有（但经常被忽视）问题，以及对单一最佳模型的不适当关注或依赖。我们用例子来讨论每一个问题。我们使用4年收集的yellow - whammer分布数据的工作示例来突出逐步回归的缺陷。我们表明逐步回归允许从每年的数据中获得包含显著预测因子的模型。尽管所选择的模型具有重要意义，但它们在不同年份之间差异很大，并且所显示的模式与分析完整的4年数据集所确定的模式不一致。对黄锤数据集的信息理论（IT）分析说明了逐步分析产生不同结果的原因。特别是，IT方法确定了大量可以很好地描述数据的相互竞争的模型，表明不应该依赖任何一个模型来进行推理。
1. The biases and shortcomings of stepwise multiple regression are well established within the statistical literature. However, an examination of papers published in 2004 by three leading ecological and behavioural journals suggested that the use of this technique remains widespread: of 65 papers in which a multiple regression approach was used, 57% of studies used a stepwise procedure.2. The principal drawbacks of stepwise multiple regression include bias in parameter estimation, inconsistencies among model selection algorithms, an inherent (but often overlooked) problem of multiple hypothesis testing, and an inappropriate focus or reliance on a single best model. We discuss each of these issues with examples.3. We use a worked example of data on yellowhammer distribution collected over 4 years to highlight the pitfalls of stepwise regression. We show that stepwise regression allows models containing significant predictors to be obtained from each year's data. In spite of the significance of the selected models, they vary substantially between years and suggest patterns that are at odds with those determined by analysing the full, 4-year data set.4. An information theoretic (IT) analysis of the yellowhammer data set illustrates why the varying outcomes of stepwise analyses arise. In particular, the IT approach identifies large numbers of competing models that could describe the data equally well, showing that no one model should be relied upon for inference.