Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology
Data(s) |
2010
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Publicador |
Wiley-Blackwell |
Relação |
http://eprints.qut.edu.au/29532/1/c29532.pdf DOI:10.1111/j.2041-210X.2009.00009.x 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. |
Direitos |
Copyright 2010 The Authors. Journal compilation Copyright 2010 British Ecological Society |
Fonte |
Faculty of Health; Institute of Health and Biomedical Innovation; School of Public Health & Social Work |
Palavras-Chave | #010401 Applied Statistics #deviance information criteria #information criteria #quasi information criteria #longitudinal data #variance-covariance |
Tipo |
Journal Article |