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The final model (third column) is based on backward elimination of non-significant factors.
Both methods yielded the same result and therefore only results based on backward elimination are presented.
For this procedure we retained variables based on backward elimination, p < 0.1.
Further selection of variables was based on backward elimination procedure using a LR-test at 0.05 as cut-point.
Stepwise regression analyses based on backward elimination were performed to test the influence of various factors on the vaspin serum concentrations.
The variables significantly associated with the outcome on analysis (P ≤ 0.2) were included in the multivariable analysis by applying a multiple logistic regression based on backward elimination of data.
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Multivariate logistic regression was fitted based on backward hierarchical elimination approach and the minimal model was reported.
Wrapper methods based on backward feature elimination, such as SVM-RFE (Guyon et al., 2002), are limited in choosing a small set of highly discriminative features.
The elimination of non-significant variables in regression analysis based on backward variable selection is similar to the pruning of the tree that contains the maximum number of terminal nodes.
Finally, we ranked these distributions according to their profiling accuracy, based on the backward elimination method.
Further selection of variables in the final model was based on stepwise backward elimination procedure.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com