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Among the 30 different physicochemical, topological and structural descriptors that were considered as inputs to the model, only eight variables (four variables for the polymer and four descriptors for the solvent) were selected using the elimination selection stepwise regression method (ES-SWR).
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Predictive modelling for some need was developed using the backwards elimination selection procedure to identify suitable logistic regression models for each of the five domains based on the pooled model results.
Pre-selected factors were included in a multiple logistic regression using backward elimination (selection level 5%) in order to identify important risk factors.
Cofactors were initially specified according to peak positions in interval mapping and selected by backwards elimination using the "Automatic cofactor selection" tool of MapQTL® 6. Maps were visualized using MapChart [ 46].
Using a backwards elimination selection process (see statistical methods), 4 independently informative predictors were identified: creatinine level, phosphate level, urinary albumin-creatinine ratio, and female sex (Table 4).
Using the forward-selection backward-elimination algorithm, the AIC model selection criteria, and a multiple-hypothesis correction we reduce the number of features to a set that remains statistically significant.
Genotype selection was then performed using the forward-selection and backward-elimination scheme.
The main effect model is built by using the stepwise forward selection and backward elimination technique.
Select the rectangle using the Selection tool.
Then the ϵ value obtained by BS is used for serving the boundary elimination selection (BES) to guarantee the stability of the elite population.
The final model was determined using backward elimination variable selection, with a significance level of 0.10 of the Wald chisquared statistic to specify the co-variates that would remain in each step.
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