2010
DOI: 10.1007/s00265-010-1037-6
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A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion

Abstract: O que, como e porque escolher [What, how and why to choose]. Univerciência, 2 (3/4), 33-38.

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Cited by 1,982 publications
(1,602 citation statements)
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References 33 publications
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“…We assessed the utility of models of animal activity or population spawning based on a normal or binomial distribution, and an identity or logit link function, respectively. We corrected values of Akaike's Information Criterion (AIC) that were generated in Statistica, for over-dispersion of the data in all models (QAIC), and for small sample size in the models of diurnal or nocturnal animal activity (QAIC c ; Symonds & Moussalli 2010). For model averaging we used models with a corrected AIC value that differed by < 2 from that of the "best" model of population spawning, or diurnal or nocturnal animal activity (Symonds & Moussalli 2010).…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…We assessed the utility of models of animal activity or population spawning based on a normal or binomial distribution, and an identity or logit link function, respectively. We corrected values of Akaike's Information Criterion (AIC) that were generated in Statistica, for over-dispersion of the data in all models (QAIC), and for small sample size in the models of diurnal or nocturnal animal activity (QAIC c ; Symonds & Moussalli 2010). For model averaging we used models with a corrected AIC value that differed by < 2 from that of the "best" model of population spawning, or diurnal or nocturnal animal activity (Symonds & Moussalli 2010).…”
Section: Methodsmentioning
confidence: 99%
“…We corrected values of Akaike's Information Criterion (AIC) that were generated in Statistica, for over-dispersion of the data in all models (QAIC), and for small sample size in the models of diurnal or nocturnal animal activity (QAIC c ; Symonds & Moussalli 2010). For model averaging we used models with a corrected AIC value that differed by < 2 from that of the "best" model of population spawning, or diurnal or nocturnal animal activity (Symonds & Moussalli 2010). The importance of a specific predictor was estimated as the sum of the Akaike weights (Σw i ) of all models of diurnal or nocturnal animal activity, or population spawning, which included that specific predictor (Symonds & Moussalli 2010).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…This avoids problems associated with trying to produce a single best-fit model where the order of parameter deletion or addition can result in different combinations of fixed effects (Burnham and Anderson 1998;Symonds and Moussalli 2011). The approach used Akaike's information criterion (AIC) to compare model fit (Burnham and Anderson 1998).…”
Section: Resultsmentioning
confidence: 99%
“…w provides a measure of the strength of evidence for each model and represents the probability that the model is the best among the whole set of candidate models (Franklin et al, 2001;Johnson & Omland, 2004). If w did not provide strong support for any particular model, we used model averaging to stimate the parameters of the variables included in the best model (Symonds & Moussalli, 2011).…”
Section: Methodsmentioning
confidence: 99%