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Therefore, regardless of the number of phases selected by different approaches to model selection, we see that the models with PA are preferred over models without.
Model Selection We use the BIC to select the final model from the possible regression models for a gene i.
For model selection, we used the BIC, values of Entropy and Posterior Class Probability, the BLRT as well as theoretical implications.
For model selection we analysed the likelihood for varying numbered of topics in the range (T in {5, 10, 15,ldots, 100, 150, 200,ldots, 500}).
For model selection, we propose a procedure grounded on the calculation of distances defined in a metric space and a minimization procedure to determine the optimal set of representative models.
For the model selection, we employed a 2-fold inner cross-validation, again to ensure a test set size of at least 5. Figure 11 summarizes the results of the multi-task approaches compared to the baseline methods.
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Through a series of model selections, we have identified selection conditions where expression of a catalytically active antibody confers a growth advantage to Escherichia coli.
At minimum we suggest conducting a similar model selection as we did for cycads to select the best-fitting prior and thus the best timetree.
In the model selection phase, we applied 2-fold cross validation, and selected parameters that gave the highest auROC.
As an alternative model selection method, we performed manual forward selection using α = 0.05 and selected the same model as with the backward selection method.
Complementarity and subset allocation As described in the "Data description and model selection" section, we allocate firms to the three subsets by their mean energy cost share.
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