assess_direct: The assess_direct() function

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

Description

The assess_direct function is designed to evaluate the prediction accuracy of a Gaussian Graphical model(GGM) comparing with the true graph structure with a known precision matrix.

Usage

assess_direct(PREC_for_graph, OMEGA_for_graph, p)

Arguments

PREC_for_graph	It is the known precision matrix which is used to assess the estimated precision matrix from GGM.
OMEGA_for_graph	It is the estimated precision matrix from a GGM.
p	It is an integer representing the number of dimension of both the known and estimated precision matrix.

Value

Return the list of assessment results including sensitivity, specificity, NPV(test negative), PPV(test positive), true positive and true negative.

Examples

prec1 <- matrix(c(0,2,3,1,0,0.5,0,0,0.4),nrow=3,ncol=3)
prec0 <- matrix(c(0,1,2,1,0.5,0.2,0,1,1),nrow=3,ncol=3)

assessresult <- assess_direct(prec1,prec0,p=3)

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