Do not log‐transform count data
Senckenberg Biodiversity and Climate Research Centre · University of Helsinki
Abstract
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…
Citation impact
- FWCI
- 41.05
- Percentile
- 100%
- References
- 26
Authors
2Topics & keywords
- Count data
- Negative binomial distribution
- Poisson distribution
- Quasi-likelihood
- Statistics
- Mathematics
- Overdispersion
- Binomial proportion confidence interval
- Life in Land