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

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]