Figure 2.

Identification of variables using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The numbers above the graph represent the number of variables involved in the LASSO model. (A) LASSO coefficient profiles of the 122 variables. (B) Identification of the optimal penalization coefficient λ in the LASSO model. The partial likelihood deviance is plotted against log (λ), where λ is the tuning parameter. Red dots indicate average deviance values for each model with a given λ, and partial likelihood deviance values are shown, with error bars representing s.e. The dotted vertical lines are plotted at the value selected using the 10-fold cross-validation and 1 – s.e. criteria.