calcSensSpec | R Documentation |
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.
calcSensSpec(trueMatrix, estMatrix)
trueMatrix |
Posterior mean or adjacency matrix of the true network |
estMatrix |
Posterior mean or adjacency matrix of the estimated network |
The matrices trueMatrix
and estMatrix
must be of the same dimension.
TP |
Number of true positives |
FP |
Number of false positives |
FN |
Number of false negatives |
TN |
Number of true negatives |
Andrea Rau
calcAUC
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
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