The arcsine is asinine: the analysis of proportions in ecology
The arcsine is asinine: the analysis of proportions in ecology
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DOI：
10.1890/10-0340.1
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发表时间：
2011-01-01
期刊：
影响因子：
4.8
通讯作者：
Hui, Francis K. C.
中科院分区：
文献类型：
--
作者：
Warton, David I.;Hui, Francis K. C.
反正弦平方根变换长期以来一直是生态学中分析比例数据的标准程序，适用于包含二项式和非二项式响应变量的数据集。在这里，我们认为，反正弦变换不应该在任何情况下使用。对于二项分布数据，逻辑回归比转换数据的分析具有更大的可解释性和更高的功效。然而，重要的是检查数据是否存在其他无法解释的变化，即，对于非二项分布数据，反正弦变换不可取，因为它可能产生无意义的预测，而且考虑到可解释性，反正弦变换可能产生无意义的预测。Logit变换被提出作为解决这些问题的替代方法。在这两种情况下的例子来说明这些优点，比较各种方法的分析比例，包括未转换，反正弦和对数转换的线性模型和逻辑回归（有或没有随机效应）。仿真表明，逻辑回归通常提供了一个增益的权力比其他方法。
The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.