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Do not log-transform count data
Article first published online: 24 MAR 2010
DOI: 10.1111/j.2041-210X.2010.00021.x
© 2010 The Authors. Journal compilation © 2010 British Ecological Society
Additional Information
How to Cite
O’Hara, R. B. and Kotze, D. J. (2010), Do not log-transform count data. Methods in Ecology and Evolution, 1: 118–122. doi: 10.1111/j.2041-210X.2010.00021.x
Publication History
- Issue published online: 4 MAY 2010
- Article first published online: 24 MAR 2010
- Received 13 December 2009; accepted 19 January 2010 Handling Editor: Robert P. Freckleton
Keywords:
- generalized linear models;
- linear models;
- overdispersion;
- Poisson;
- transformation
Summary
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.