sensitivity: 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

sensitivity(trueMatrix, estMatrix)

Arguments

trueMatrix

Posterior mean or adjacency matrix of the true network

estMatrix

Posterior mean or adjacency matrix of the estimated network

Value

TPNumber of true positives
FPNumber of false positives
FNNumber of false negatives
TNNumber of true negatives

Details

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

Examples

Run this code

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