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The importance of each predictor was estimated for the ensemble model prediction using the "variables_importance" function available in biomod2 (Thuiller et al. 2015).
The predictor was estimated at 0.0874 (standard error 0.0053) and was highly significant (p < 0.001).
For Passes All Courses (a binary outcome variable), the odds ratio (OR) associated with the change in a linear predictor was estimated with 95%% confidence interval.
For GPA (a continuous outcome variable), its mean change associated with the change in a linear predictor was estimated with 95%% confidence interval.
For GPA (a continuous outcome variable), its mean change associated with the change in alinear predictor was estimated with 95%% confidence interval.
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In this case, how can the reliability of the performance of a consensus predictor be estimated?
The false positive error rate, that is, the prediction of disorder when a region is known to be ordered, of the VL-XT predictor is estimated at 22% on a per residue basis.
In the BRT model, the relative contribution of a predictor is estimated from the number of times a variable was selected for splitting regression trees weighted by the improvement of the model produced by that split [ 42].
The relative importance of predictors was estimated by standardized regression coefficients of sparse learning.
The PAF for the predictors was estimated in Stata version 11.2 for Windows using the punaf command.
The variance explained by the regression model was indicated by Nagelkerke's R. The significance of predictors was estimated using the Wald coefficient.
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