Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses
Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses
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DOI：
10.1163/156853806777239922
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发表时间：
2006-06-01
影响因子：
1.6
通讯作者：
Mazerolle, Marc J.
中科院分区：
文献类型：
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作者：
Mazerolle, Marc J.
在生态学中，研究人员经常使用观测研究来解释给定的模式，如栖息地斑块中的个体数量，并使用大量的解释变量(即，独立变量)。为了阐明这种关系，生态学家长期以来一直依赖假设检验来在回归模型中包括或排除变量，尽管结论通常取决于所使用的方法(例如，向前、向后、逐步选择)。虽然在20世纪70年代中期出现了更好的工具，但在某些领域，特别是在爬行动物方面，它们仍然没有得到充分的利用。这就是阿凯克信息标准(AIC)的情况，它在模型选择(即变量选择)方面明显优于基于假设的方法。它计算简单，易于理解，但更重要的是，对于给定的数据集，它提供了一个衡量每个模型的证据强度的指标，这些模型代表了一个可信的生物学假设相对于所考虑的整个模型集。使用这种方法，然后可以计算所有考虑的模型中任何给定的感兴趣变量的估计和标准误差的加权平均值。这一过程被称为模型平均或多模型推理，产生精确和稳健的估计。在本文中，我用两个真实的爬行动物数据集说明了AIC在模型选择和推断中的应用，以及对在这个框架下分析的结果的解释。爬行动物学家应该常规地采用AIC和由此得出的措施。
In ecology, researchers frequently use observational studies to explain a given pattern, such as the number of individuals in a habitat patch, with a large number of explanatory (i.e., independent) variables. To elucidate such relationships, ecologists have long relied on hypothesis testing to include or exclude variables in regression models, although the conclusions often depend on the approach used (e.g., forward, backward, stepwise selection). Though better tools have surfaced in the mid 1970's, they are still underutilized in certain fields, particularly in herpetology. This is the case of the Akaike information criterion (AIC) which is remarkably superior in model selection (i.e., variable selection) than hypothesis-based approaches. It is simple to compute and easy to understand, but more importantly, for a given data set, it provides a measure of the strength of evidence for each model that represents a plausible biological hypothesis relative to the entire set of models considered. Using this approach, one can then compute a weighted average of the estimate and standard error for any given variable of interest across all the models considered. This procedure, termed model-averaging or multimodel inference, yields precise and robust estimates. In this paper, I illustrate the use of the AIC in model selection and inference, as well as the interpretation of results analysed in this framework with two real herpetological data sets. The AIC and measures derived from it is should be routinely adopted by herpetologists.