# plot_roc: The plot_roc() function In 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

 `1` ```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

 ```1 2 3 4 5 6 7 8 9``` ```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') ```

sparsenetgls documentation built on Nov. 8, 2020, 7:37 p.m.