confusionMatrix: Compute Confusion Matrix

Description

confusionMatrix computes the confusion matrix, i.e. it counts the number of false positives (FP), true positives (TP), true negatives (TN), and false negatives (FN).

Despite its name the functions returns a vector rather than an actual matrix for easier use with the crossval function.

Usage

confusionMatrix(actual, predicted, negative="control")

Value

confusionMatrix returns a vector of length 4 containing the counts for FP, TP, TN, and FN.

Arguments

actual: a vector containing the actual correct labels for each sample (e.g. "cancer" or "control").
predicted: a vector containing the predicted labels.
negative: the label of a negative "null" sample (default: "control").

Author

Korbinian Strimmer (https://strimmerlab.github.io).

Examples

Run this code

# load crossval library
library("crossval")

# true labels
a = c("cancer", "cancer", "control", "control", "cancer", "control", "control")

# predicted labels
p = c("cancer", "control", "control", "control", "cancer", "control", "cancer")

# confusion matrix (a vector)
cm = confusionMatrix(a, p, negative="control") 
cm
# FP TP TN FN 
# 1  2  3  1 
# attr(,"negative")
# [1] "control"

# corresponding accuracy, sensitivity etc.
diagnosticErrors(cm)
#       acc      sens      spec       ppv       npv       lor 
# 0.7142857 0.6666667 0.7500000 0.6666667 0.7500000 1.7917595
# attr(,"negative")
# [1] "control"