Correspondence site: http://www.respond2articles.com/MEE/
Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology
Article first published online: 18 JAN 2010
© 2010 The Authors. Journal compilation © 2010 British Ecological Society
Methods in Ecology and Evolution
Volume 1, Issue 1, pages 15–24, March 2010
How to Cite
Barnett, A. G., Koper, N., Dobson, A. J., Schmiegelow, F. and Manseau, M. (2010), Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology. Methods in Ecology and Evolution, 1: 15–24. doi: 10.1111/j.2041-210X.2009.00009.x
- Issue published online: 23 FEB 2010
- Article first published online: 18 JAN 2010
- Received 1 September 2009; accepted 20 December 2009 Handling Editor: Robert P. Freckleton
- Bayesian methods;
- correlated data;
- covariance structure;
- information criteria;
- generalized estimating equation;
- longitudinal data
1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations.
2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC) and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years.
3. The overall success was 80·6% for the AIC, 29·4% for the QIC and 81·6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct.
4. We recommend using DIC for selecting the correct covariance structure.