confusionMatrix: Compute Confusion Matrix

In crossval: Generic Functions for Cross Validation

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") 

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").

Value

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

Author(s)

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

See Also

diagnosticErrors.

Examples

# 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"

crossval documentation built on July 9, 2023, 6:26 p.m.