Power analysis for generalized linear mixed models in ecology and evolution

Methods in Ecology and Evolution

Volumn 6, Issue 2, 2015, Pages 133-142

  Johnson, Paul C D ^a,b   Barry, Sarah J E ^b   Ferguson, Heather M ^a   Müller, Pie ^c,d  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

^c SWISS TROPICAL AND PUBLIC HEALTH INSTITUTE   (Switzerland)

^d UNIVERSITY OF BASEL   (Switzerland)

Author keywords

Experimental design; Generalized linear mixed model; Long lasting insecticidal net; Overdispersion; Precision; Random effects; Sample size; Simulation

Indexed keywords

IXODIDA; TETRAONIDAE;

EID: 84922402421     PISSN: None     EISSN: 2041210X     Source Type: Journal    
DOI: 10.1111/2041-210X.12306     Document Type: Article

References (40)

1. Barr, D.J., Levy, R., Scheepers, C. & Tily, H.J. (2013) Random effects structure for confirmatory hypothesis testing: keep it maximal. Journal of Memory and Language, 68, 255-278.
2. Bates, D., Maechler, M., Bolker, B. & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-7. Retrieved July 19, 2014, from http://cran.r-project.org/package=lme4
3. Bliss, C.I. & Fisher, R.A. (1953) Fitting the negative binomial distribution to biological data. Biometrics, 9, 176-200.
4. Bolker, B.M. (2008) Ecological Models and Data in R. Princeton University Press, Princeton & Oxford.
5. 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 and Evolution, 24, 127-135.
6. Burnham, K. & Anderson, D. (2001) Kullback-Leibler information as a basis for strong inference in ecological studies. Wildlife Research, 28, 111-119.
7. Crowder, M.J. (1978) Beta-binomial anova for proportions. Journal of the Royal Statistical Society Series C (Applied Statistics), 27, 34-37.
8. Cumming, G. (2013) Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge, New York.
9. Di Stefano, J. (2003) How much power is enough? Against the development of an arbitrary convention for statistical power calculations. Functional Ecology, 17, 707-709.
10. Eberhardt, L. (1978) Appraising variability in population studies. The Journal of Wildlife Management, 42, 207-238.
11. Ecological Society of America Statistical Ecology Section. (2012) Guidelines for Statistical Analysis and Data Presentation. Retrieved August 13, 2013, from http://esapubs.org/esapubs/statistics.htm
12. Elston, D.A., Moss, R., Boulinier, T., Arrowsmith, C. & Lambin, X. (2001) Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks. Parasitology, 122, 563-569.
13. Faraway, J.J. (2005) Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC, Boca Raton.
14. Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191.
15. Hooper, R. (2013) Versatile sample-size calculation using simulation. The Stata Journal, 13, 21-38.
16. Hurlbert, S.H. (1984) Pseudoreplication and the design of ecological field experiments. Ecological Monographs, 54, 187-211.
17. Ioannidis, J.P.A. (2005) Why most published research findings are false. PLoS Medicine, 2, e124.
18. Jennions, M. & Møller, A. (2003) A survey of the statistical power of research in behavioral ecology and animal behavior. Behavioral Ecology, 14, 438-445.
19. Lengeler, C. (2004) Insecticide-treated bed nets and curtains for preventing malaria. The Cochrane Database of Systematic Reviews, CD000363.
20. Maas, C.J.M. & Hox, J.J. (2005) Sufficient sample sizes for multilevel modeling. Methodology, 1, 86-92.
21. Malima, R.C., Magesa, S.M., Tungu, P.K., Mwingira, V., Magogo, F.S., Sudi, W. et al. (2008) An experimental hut evaluation of Olyset nets against anopheline mosquitoes after seven years use in Tanzanian villages. Malaria Journal, 7, 38.
22. Martin, J.G.A., Nussey, D.H., Wilson, A.J. & Réale, D. (2011) Measuring individual differences in reaction norms in field and experimental studies: a power analysis of random regression models. Methods in Ecology and Evolution, 2, 362-374.
23. Ngufor, C., Tchicaya, E., Koudou, B., N'Fale, S., Dabire, R., Johnson, P., Ranson, H. & Rowland, M. (2014) Combining organophosphate treated wall linings and long-lasting insecticidal nets for improved control of pyrethroid resistant Anopheles gambiae. PLoS ONE, 9, e83897.
24. Nussey, D.H., Wilson, A.J. & Brommer, J.E. (2007) The evolutionary ecology of individual phenotypic plasticity in wild populations. Journal of Evolutionary Biology, 20, 831-844.
25. O'Hara, R.B. & Kotze, D.J. (2010) Do not log-transform count data. Methods in Ecology and Evolution, 1, 118-122.
26. R Core Team. (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved April 10, 2014, from http://www.r-project.org/
27. Roll Back Malaria Partnership. (2008) The Global Malaria Action Plan: For a malaria-free world. Geneva, Switzerland.
28. Schielzeth, H. & Forstmeier, W. (2009) Conclusions beyond support: overconfident estimates in mixed models. Behavioral Ecology, 20, 416-420.
29. Smith, D.R., Hardy, I.C.W. & Gammell, M.P. (2011) Power rangers: no improvement in the statistical power of analyses published in Animal Behaviour. Animal Behaviour, 81, 347-352.
30. Snijders, T.A.B. (2005) Power and sample size in multilevel linear models. Encyclopedia of Statistics in Behavioral Science Volume 3 (eds B.S. Everitt & D.C. Howell ), pp. 1570-1573. Wiley, Chichester.
31. Snijders, T. & Bosker, R. (1993) Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237-259.
32. Taborsky, M. (2010) Sample size in the study of behaviour. Ethology, 116, 185-202.
33. Thomas, L. & Juanes, F. (1996) The importance of statistical power analysis: an example from Animal Behaviour. Animal Behaviour, 52, 856-859.
34. Warton, D.I. & Hui, F.K.C. (2011) The arcsine is asinine: the analysis of proportions in ecology. Ecology, 92, 3-10.
35. Williams, E.J. (1949) Experimental designs balanced for the estimation of residual effects of treatments. Australian Journal of Chemistry, 2, 149-168.
36. ® Maxx in the laboratory and against insecticide-resistant Anopheles gambiae in central Côte d'Ivoire. Malaria Journal, 11, 167.
37. World Health Organization. (2013a) Guidelines for laboratory and field testing of long-lasting insecticidal nets. WHO/HTM/NTD/WHOPES/2013.1.
38. World Health Organization. (2013b). World Malaria Report 2013. World Health Organization, Geneva, Switzerland.
39. Zuur, A.F., Hilbe, J.M. & Leno, E.N. (2013) A Beginner's Guide to GLM and GLMM with R: A Frequentist and Bayesian Perspective for Ecologists. Highland Statistics Ltd, Newburgh, UK.
40. Zuur, A.F., Saveliev, A.A. & Ieno, E.N. (2012) Zero Inflated Models and Generalized Linear Mixed Models with R. Highland Statistics Ltd, Newburgh, UK.