Sentence examples for best fitting for each from inspiring English sources
Following the suggestion of Dowton et al. [ 6] we used the Bayesian approach using nucleotide sequences and implemented the GTR+I+Γ model of sequence evolution across genes and codon positions since jModeltest usually indentified this model as the best fitting for each data partition.
Both the Likelihood Ratio Test (LRT) and Akaike Information Criterion (AIC) found the GTR+I+Γ model [ 30, 31] as best fitting for all partitions except 2nd codon positions, for which LRT found TN+I+Γ [ 32] as best fitting.
The best fitting for coatings was obtained using three fitting components with varied intensities but fixed positions and line widths.
Thus, we conclude that the proposed model is the best fitting for Phase II test data among all other models in Table 1.
The best fitting for MB experimental equilibrium data was achieved with the Langmuir isotherm, giving a maximum MB adsorption capacity of 381.22 mg/g.
In contrast, the model with both baseline and longitudinal differences among groups was the best fitting for companions (wAICc=0.95).
The GTR model of nucleotide substitution was identified by jModeltest [ 41] as the best fitting for nd5, cox1-B, LSU-A, LSU-B, ITS2 and all concatenated datasets.
The Bayesian skyline demographic model was best-fitting for each of the datasets, though it was not significantly better than the constant or exponentially growing population models.
Determining the best fit for the ILD functions was particularly useful, as we used the parameters of the best fitting function for each ILD function for quantifying the characteristics of that ILD function, i.e. response strength, ILD50 or peak ILD, and full width at half maximum or 80% dynamic range (these characteristics are described later in detail).
In order to determine the best fitting model for each country, we fitted regression models with stepwise in-/exclusion of the variables according to specific model parameters (probability of F for entry = 0.05 and of 0.1 for removal).
