calcSensSpec {ebdbNet}R Documentation

Calculate Sensitivity and Specificity of a Network

Description

Function to calculate the true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) of an estimated network, given the structure of the true network.

Usage

calcSensSpec(trueMatrix, estMatrix)

Arguments

trueMatrix

Posterior mean or adjacency matrix of the true network

estMatrix

Posterior mean or adjacency matrix of the estimated network

Details

The matrices trueMatrix and estMatrix must be of the same dimension.

Value

TP

Number of true positives

FP

Number of false positives

FN

Number of false negatives

TN

Number of true negatives

Author(s)

Andrea Rau

See Also

calcAUC

Examples

library(ebdbNet)
tmp <- runif(1) ## Initialize random number generator
set.seed(16933) ## Set seed
P <- 10 ## 10 genes

## Create artificial true D matrix
Dtrue <- matrix(0, nrow = P, ncol = P)
index <- expand.grid(seq(1:P),seq(1:P))
selected.index <- sample(seq(1:(P*P)), ceiling(0.25 * P * P))
selected.edges <- index[selected.index,]
for(edge in 1:ceiling(0.25 * P * P)) {
	tmp <- runif(1)
	if(tmp > 0.5) {
		Dtrue[selected.edges[edge,1], selected.edges[edge,2]] <-
			runif(1, min = 0.2, max = 1)
	}
	else {
		Dtrue[selected.edges[edge,1], selected.edges[edge,2]] <-
			runif(1, min = -1, max = -0.2)
	}
}

## Create artificial estimated D matrix
Dest <- matrix(0, nrow = P, ncol = P)
index <- expand.grid(seq(1:P),seq(1:P))
selected.index <- sample(seq(1:(P*P)), ceiling(0.25 * P * P))
selected.edges <- index[selected.index,]
for(edge in 1:ceiling(0.25 * P * P)) {
	tmp <- runif(1)
	if(tmp > 0.5) {
		Dest[selected.edges[edge,1], selected.edges[edge,2]] <-
			runif(1, min = 0.2, max = 1)
	}
	else {
		Dest[selected.edges[edge,1], selected.edges[edge,2]] <-
			runif(1, min = -1, max = -0.2)
	}
}

check <- calcSensSpec(Dtrue, Dest)
check$TP ## 5 True Positives
check$FP ## 20 False Positives
check$TN ## 55 True Negatives
check$FN ## 20 False Negatives


[Package ebdbNet version 1.2.6 Index]