Figure 2

Use several methods for variable screening. (A1) The abscissa represents the numbers of variables inclusion, and the ordinate represents the value of BIC; When the number of variables is 4 or 5, the minimum BIC is − 48.4. (A2) Specific variables included in the model with minimum BIC. (B1) The abscissa represents the numbers of variables inclusion, and the ordinate represents the value of adjusted R-square; when the number of variables is 8, the maximum adjusted R-square is 0.143. (B2) Specific variables included in the model with maximum adjusted R-square. (C1) LASSO coefficient profiles of the features. A coefficient profile plot was produced against the log (lambda) sequence. (C2) Optimal parameter (lambda) selection in the LASSO model used fivefold cross-validation via minimum criteria⁴⁰. The partial likelihood deviance (binomial deviance) curve was plotted versus log (lambda). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1 SE of the minimum criteria (the 1-SE criteria). BIC Bayesian Information Criterion, LASSO least absolute shrinkage and selection operator, SE standard error.