Do not log-transform count data
Do not log-transform count data
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DOI：
10.1111/j.2041-210x.2010.00021.x
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发表时间：
2010-06-01
影响因子：
6.6
通讯作者：
Kotze, D. Johan
中科院分区：
文献类型：
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作者：
O'Hara, Robert B.;Kotze, D. Johan
1.生态计数数据（E。G.个体数或物种数）通常进行对数转换以满足参数检验要求.除了广义线性模型更适合处理计数数据之外，计数的对数变换在如何处理零观测值方面还有额外的困境。如果只有一个零观测值（如果该观测值代表一个采样单位），则整个数据集需要通过在转换之前添加一个值（通常为1）来进行模糊处理。3.模拟来自负二项分布的数据，我们比较了以各种方式（对数，平方根）转换的拟合模型的结果与使用拟泊松和负二项模型拟合模型的结果，以未转换的计数数据.我们发现，转换表现不佳，除了当分散小，平均计数大。准泊松模型和负二项模型表现良好，偏差很小。我们建议计数数据不应通过对数转换进行分析，而应使用基于泊松分布和负二项分布的模型。
1. Ecological count data (e. g. number of individuals or species) are often log-transformed to satisfy parametric test assumptions.2. Apart from the fact that generalized linear models are better suited in dealing with count data, a log-transformation of counts has the additional quandary in how to deal with zero observations. With just one zero observation (if this observation represents a sampling unit), the whole data set needs to be fudged by adding a value (usually 1) before transformation.3. Simulating data from a negative binomial distribution, we compared the outcome of fitting models that were transformed in various ways (log, square root) with results from fitting models using quasi-Poisson and negative binomial models to untransformed count data.4. We found that the transformations performed poorly, except when the dispersion was small and the mean counts were large. The quasi-Poisson and negative binomial models consistently performed well, with little bias.5. We recommend that count data should not be analysed by log-transforming it, but instead models based on Poisson and negative binomial distributions should be used.