# path_result_for_roc: The path_result_for_roc() function In sparsenetgls: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression

## Description

The path_result_for_roc function is designed to evaluate the the prediction accuracy of a series Gaussian Graphical models (GGM) comparing 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 path_result_for_roc(PREC_for_graph, OMEGA_path, pathnumber) 

## Arguments

 PREC_for_graph It is the known precision matrix which is used to assess the estimated precision matrix from GGM. OMEGA_path It is a matrix comprising of a series estimated precision matrices from a GGM model using a penalized path based on a range of structure parameters (i.e. λ,\in [0,1]). pathnumber It represents the number of graph models (i.e. λ) for the evaluation.The value of pathnumber can be the same number used in a penalized path.

## Value

Return the list of assessment results for a series of precision matrices. The results include sensitivity/specificity/NPV/PPV

## Examples

 1 2 3 4 5 6 7 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) 

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