path_result_for_roc {sparsenetgls} | R Documentation |
The path_result_for_roc function is designed to produce the Reciever Operative Characteristics (ROC) Curve for visualizing the prediction accuracy of a Gaussian Graphical model (GGM) to the true graph structure. The GGM must use a l-p norm regularizations (p=1,2) with the series of solutions conditional on the regularization parameter.
path_result_for_roc(PREC_for_graph, OMEGA_path, pathnumber)
PREC_for_graph |
It is the known precision matrix which is used to assess the estimated precision matrix from GGM. |
OMEGA_path |
It is an estimated precision matrix from a GGM model using the penalized path with a range of values (i.e. λ,\in [0,1]). |
pathnumber |
It represents the number of values (i.e. λ) used in the penalized GGM. This will result in the number of co-ordinates used to form the ROC curve. |
Return the list of assessment results for a serie of precision matrices. The results include sensitiviy/specificity/NPV/PPV
prec1 <- matrix(c(0,2,3,1,0,0.5,0,0,0.4),nrow=3,ncol=3) Omega_est <- array(dim=c(3,3,3)) Omega_est[,,1] <- matrix(c(0,1,2,1,0.5,0.2,0,1,1),nrow=3,ncol=3) Omega_est[,,2] <- matrix(c(0,1,0,1,0.5,0.2,0,1,1),nrow=3,ncol=3) Omega_est[,,3] <- matrix(c(0,1,0,1,0,0.2,0,1,1),nrow=3,ncol=3) rocpath <- path_result_for_roc(PREC_for_graph=prec1,OMEGA_path=Omega_est, pathnumber=3)