Journal article

Collinearity: a review of methods to deal with it and a simulation study evaluating their performance

Carsten F Dormann, Jane Elith, Sven Bacher, Carsten Buchmann, Gudrun Carl, Gabriel Carre, Jaime R Garcia Marquez, Bernd Gruber, Bruno Lafourcade, Pedro J Leitao, Tamara Muenkemueller, Colin McClean, Patrick E Osborne, Bjoern Reineking, Boris Schroeder, Andrew K Skidmore, Damaris Zurell, Sven Lautenbach

Ecography | WILEY | Published : 2013

Abstract

Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial ..

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University of Melbourne Researchers

Grants

Awarded by Helmholtz Association


Awarded by German Science Foundation


Awarded by Australian Research Council


Awarded by Portuguese Science and Technology Foundation FCT


Funding Acknowledgements

CFD acknowledges funding by the Helmholtz Association (VH-NG-247) and the German Science Foundation (4851/220/07) for funding the workshop 'Extracting the truth: methods to deal with collinearity in ecological data' from which this work emerged. JE acknowledges the Australian Centre of Excellence for Risk Analysis and Australian Research Council (grant DP0772671). JRGM was financially supported by the research funding programme 'LOEWE-Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz' of Hesse's Ministry of Higher Education, Research, and the Arts. PJL acknowledges funding from the Portuguese Science and Technology Foundation FCT (SFRH/BD/12569/2003). BR acknowledges support by the 'Bavarian Climate Programme 2020' within the joint research centre FORKAST. BS acknowledges funding by the German Science Foundation (SCHR 1000/3-1, 14-2). We thank Thomas Schnicke and Ben Langenberg for supporting us to run our analysis at the UFZ high performance cluster system. CFD designed the review and wrote the first draft. CFD and SL created the data sets and ran all simulations. SL, CFD and DZ analysed the case studies. GuC, CFD, SL, JE, GaC, BG, BL, TM, BR and DZ wrote much of the code for implementing and operationalising the methods. PEO, CMC, PJL and AKS analysed the spatial scaling pattern of collinearity, SL that of biome patterns and CFD the temporal patterns. All authors contributed to the design of the simulations, helped write the manuscript and contributed code corrections. We should like to thank Christoph Scherber for contributing the much-used stepAICc-function.