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Two RF models were created; one that evaluated all features, and one that contained only the top 10 features after ranking variable importance using a RF filter.
For unsupervised clustering, we performed hierarchical clustering, ranking variable genes by interquartile range (IQR) and using the average linkage algorithm and 1 minus Pearson correlation as the distance measure.
In 2012, Chile's centralized university admission system introduced a GPA ranking variable relative to the previous cohorts' average GPA.
Because the large number of observations bias the AIC towards ever decreasing values with increasing numbers of variables, acceptance of the best ranking variable was subsequently based on an F-test performed on the predicted values for the acceptance-dataset (Zar 1996).
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In addition, posterior odds statistics have proven to be a useful means of ranking variables in terms of evidence for differential expression [ 59, 62- 65].
Non-parametric test was conducted for a ranked variable such as the frequency of bronchodilator used during asthma attack.
The produced ordered training data with more relevant to less important variables was further processed by meta-node "Build Model by Adding Ranked Variables", which firstly splits data into training and test set and introduces each ranked variable into a new SVMs fitted models (Additional file 1: Figure S5).
Due to multicollinearity, coefficients of the mark-based regression are not reliable to rank variable importance.
Relative indices of inequality are then computed by regressing the mortality on this ranked variable.
For the ranked variable (leaf colour), differences between groups were confirmed in all cases using non-parametric testing (SAS npar1way).
Nested RF modeling was done using the ranked indices, by starting with the highest ranked variable and subsequent addition remaining after each step.
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