Figure 3.

Performance of different methods on simulated data with different ρ and ρ₁ when p = 100, n = 300. (A) AUC under ROC curves. Advantage of LDGM on ROC becomes more visible when differential networks are more dense with an increased network density ρ. (B) AUC under precision-recall curves. LDGM consistently has a much larger AUC under a precision-recall curve than Glasso, JGL and CNJGL. Bar height represents an AUC under an averaged curve over 30 runs. Error bar represents one standard deviation of AUC under 30 replicated curves.