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Each model was selected by Modeltest 3.7 [ 45] based on likelihood ratio tests.
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The network architecture that performs best for each model is selected using a k-fold cross-validation method.
The suitable lag value for each model is selected on the basis of Akaike information criterion.
Covariates for each model were selected using Bayesian model averaging; predictors with posterior probabilities >35% were retained in the model (13).
The selection process introduced earlier was repeated in each of the 1,000 bootstrap samples and the frequency of each model being selected in these bootstrap samples was recorded.
Six sites, two in each model, were selected across four provinces, with 293 newly treated uncomplicated TB patients being randomly selected.
Only two sites in each model were selected in the study, great cautions should be taken in extrapolation the results of this study.
In each run, the model was selected in a stepwise selection with all variables entered.
The best model was selected using model selection procedure (AICc).
All models were fitted separately to each data set, and the best fitting model was selected for each chemical according to a statistical goodness-of-fit criterion (Akaike information).
These plots (not included in this manuscript) were examined and a preferred model was selected for each BOPH and each study population.
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