For testing the significance of regression coefficients, go ahead and log-transform count data

Methods in Ecology and Evolution

Volumn 6, Issue 7, 2015, Pages 828-835

  Ives, Anthony R ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Generalized linear mixed models; Generalized linear models; Least squares regression; Linear models; Transformation; Type i errors

Indexed keywords

EID: 84937073944     PISSN: None     EISSN: 2041210X     Source Type: Journal    
DOI: 10.1111/2041-210X.12386     Document Type: Article

References (25)

1. Bates, D., Maechler, M., Bolker, B. & Walker, S. (2014) Lme4: Linear Mixed-Effects Models Using Eigen and S4. http://CRAN.R-project.org/package=lme4 [version 1.1-7].
2. Bolker, B.M. (2008) Ecological Models and Data in R. Princeton University Press, Princeton and Oxford.
3. Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, M.H.H. & White, J.S.S. (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution, 24, 127-135.
4. Campbell, N.L., Young, L.J. & Capuano, G.A. (1999) Analyzing over-dispersed count data in two-way cross-classification problems using generalized linear models. Journal of Statistical Computation and Simulation, 63, 263-281.
5. Engle, R.F. (1984) Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. Handbook of Econometrics. Vol. 2 (eds Z. Griliches & M. Intriligator ), pp. 775-826. Elsevier, Amsterdam.
6. Gelman, A. & Hill, J. (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, New York, NY.
7. Hadfield, J.D. (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. Journal of Statistical Software, 33, 1-22.
8. Ives, A.R. & Garland, T. Jr (2014) Phylogenetic regression for binary dependent variables. Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. L.Z. Garamszegi ), pp. 231-261. Springer-Verlag, Berlin Heidelberg.
9. Ives, A.R. & Helmus, M.R. (2011) Generalized linear mixed models for phylogenetic analyses of community structure. Ecological Monographs, 81, 511-525.
10. Judge, G.G., Griffiths, W.E., Hill, R.C., Lutkepohl, H. & Lee, T.-C. (1985) The Theory and Practice of Econometrics, 2nd edn. John Wiley & Sons, New York.
11. Linden, A. & Mantyniemi, S. (2011) Using the negative binomial distribution to model overdispersion in ecological count data. Ecology, 92, 1414-1421.
12. Martin, T.G., Wintle, B.A., Rhodes, J.R., Kuhnert, P.M., Field, S.A., Low-Choy, S.J., Tyre, A.J. & Possingham, H.P. (2005) Zero tolerance ecology: improving ecological inference by modelling the source of zero observations. Ecology Letters, 8, 1235-1246.
13. McCullagh, P. & Nelder, J.A. (1989) Generalized Linear Models, 2nd edn. Chapman and Hall, London.
14. Murtaugh, P.A. (2007) Simplicity and complexity in ecological data analysis. Ecology, 88, 56-62.
15. O'Hara, R.B. (2005) Species richness estimators: how many species can dance on the head of a pin? Journal of Animal Ecology, 74, 375-386.
16. O'Hara, R.B. & Kotze, D.J. (2010) Do not log-transform count data. Methods in Ecology and Evolution, 1, 118-122.
17. Okamura, H., Punt, A.E. & Amano, T. (2012) A generalized model for overdispersed count data. Population Ecology, 54, 467-474.
18. R Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
19. Ross, B.E., Hooten, M.B. & Koons, D.N. (2012) An accessible method for implementing hierarchical models with spatio-temporal abundance data. PLoS ONE, 7, e49395. doi:10.1371/journal.pone.0049395.
20. Sokal, R.R. & Rohlf, F.J. (1981) Biometry, 2nd edn. W. M. Freeman & Company, New York.
21. Steel, E.A., Kennedy, M.C., Cunningham, P.G. & Stanovick, J.S. (2013) Applied statistics in ecology: common pitfalls and simple solutions. Ecosphere, 4, 115. doi: 10.1890/ES13-00160.1.
22. Venables, W.N. & Ripley, B.D. (2002) Modern Applied Statistics with S. Springer, New York.
23. Ver Hoef, J.M. & Boveng, P.L. (2007) Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology, 88, 2766-2772.
24. Warton, D.I. & Hui, F.K.C. (2011) The arcsine is asinine: the analysis of proportions in ecology. Ecology, 92, 3-10.
25. Young, L.J., Campbell, N.L. & Capuano, G.A. (1999) Analysis of overdispersed count data from single-factor experiments: a comparative study. Journal of Agricultural Biological and Environmental Statistics, 4, 258-275.