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The best models are selected by maximizing the sensitivity and external predictive ability.
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Stepwise selection of variables was performed in each case, and the best models were selected by using the Akaike index criterion, following Venables and Ripley (14 ) and Rigby and Stasinopoulos (15 ).
Each dataset was cross-validated ten-fold using 10 stratified subsets, and the best models were selected based on maximum precision and recall.
The best models were selected among four different types of permanent magnet bearings for upper bearing and two types of superconductor bearings for lower bearing, respectively.
From the resulting set of models, a nonredundant representative set of best models was selected (Table S6).
Generalizable model performances were assessed, and the "best" models were selected, using a 10-fold cross-validation (CV) method (Hastie et al. 2001) Each data set (season) being modeled was divided into 10 approximately equal-size groups.
The best models were selected based on Akaike's Information Criterion corrected for small sample size (AICc, Burnham and Anderson 2007) using AICcmodavg (Mazerolle 2011) in R. Models with a difference in AICc of <2 were considered equally appropriate.
The best models were selected using Akaike's Information Criterion for small sample sizes (AICc) (Burnham and Anderson 2002) and the independent effect of each variable to the model estimated using hierarchical partitioning (Walsh and MacNally 2008).
The best model was selected using model selection procedure (AICc).
The model of Shikimate Kinase is generated using a comparative molecular modeling approach and the best model is selected based on stereochemical properties.
Out of the models generated, the best model was selected based on Modeller's scoring function and external validation via Whatcheck (Hooft et al., 1996) and PROSA (Wiederstein and Sippl, 2007).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com