Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology
Barnett, Adrian G., Koper, Nicola, Dobson, Annette J., Schmiegelow, Fiona, & Manseau, Micheline (2010) Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology. Methods in Ecology & Evolution.
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
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
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|Item Type:||Journal Article|
|Keywords:||deviance information criteria, information criteria, quasi information criteria, longitudinal data, variance-covariance|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health|
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Public Health & Social Work
|Copyright Owner:||Copyright 2010 The Authors. Journal compilation Copyright 2010 British Ecological Society|
|Deposited On:||20 Jan 2010 13:29|
|Last Modified:||01 Mar 2012 00:24|
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