1989
DOI: 10.1080/00401706.1989.10488538
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Akaike Information Criterion Statistics

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Cited by 15 publications
(9 citation statements)
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“…The data of the response variable MTL was the square root transformed to Gaussian distribution. Model selection was performed by ranking the candidate models based on their Akaike information criterion (AIC) and selecting the model that minimizes AIC as the best supported model [40,41]. All the statistical analyses were performed in R for Windows (R Foundation for Statistical Computing, Vienna, Austria) [38].…”
Section: Discussionmentioning
confidence: 99%
“…The data of the response variable MTL was the square root transformed to Gaussian distribution. Model selection was performed by ranking the candidate models based on their Akaike information criterion (AIC) and selecting the model that minimizes AIC as the best supported model [40,41]. All the statistical analyses were performed in R for Windows (R Foundation for Statistical Computing, Vienna, Austria) [38].…”
Section: Discussionmentioning
confidence: 99%
“…Automated model selection analysis was performed using the glmulti function from the R package [20]. The optimal model was the one with minimum Akaike's Information Criterion (AIC) [21]. We performed power analysis using simulations [22].…”
Section: Discussionmentioning
confidence: 99%
“…In particular, the use of "Two Step Cluster Analysis" was preferred, in which the measure of distance adopted to identify the groups is the Log-likelihood [34]. Moreover, the Akaike Information Criterion (AIC) [35], an automatic algorithm, was used for the extraction of the clusters. The AIC measures the deviation of our model by the probability distribution f compared to the "true" distribution g. The mathematical formulation of the criterion is the following:…”
Section: Methodsmentioning
confidence: 99%