plot_roc: The plot_roc() function

In superOmics/sparsenetgls: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression

Description

The plot_roc function is designed to produce the Receiver 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.

Usage

plot_roc(result_assessment, group = TRUE, ngroup = 0, est_names)

Arguments

result_assessment	It is the list result from function path_result_for_roc() which has five-dimensions recording the path number (i.e. the order of λ ), the sensitivity, the specificity, the Negative predicted value (NPV) and the Positive predicted value (PPV) respectively.
group	It is a logical parameter indicating if the result_assessment is for several GGM models. When it is TRUE, it produceS the ROC from several GGM models. when it is FALSE, it only produces a ROC for one model.
ngroup	It is an integer recording the number of models when group is TRUE.
est_names	it is used for labeling the GGM model in legend of ROC curve.

Value

Return the plot of Receiver Operational Curve

Examples

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(1,1,1,0.2,0.5,0.2,2,0.2,0.3),nrow=3,ncol=3)
Omega_est[,,2] <- matrix(c(0,1,1,1,0,0,0,0,1),nrow=3,ncol=3)
Omega_est[,,3] <- matrix(c(0,0,0,0,0,0,0,0,0),nrow=3,ncol=3)
roc_path_result <- path_result_for_roc(PREC_for_graph=prec1,
OMEGA_path=Omega_est,pathnumber=3)
plot_roc(result_assessment=roc_path_result,group=FALSE,ngroup=0,
est_names='test example')

superOmics/sparsenetgls documentation built on Sept. 11, 2020, 5:49 a.m.