crossval (version 1.0.5)

diagnosticErrors: Compute Diagnostic Errors: Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Log Odds Ratio

Description

diagnosticErrors computes various diagnostic errors useful for evaluating the performance of a diagnostic test or a classifier: accuracy (acc), sensitivity (sens), specificity (spec), positive predictive value (ppv), negative predictive value (npv), and log-odds ratio (lor).

Usage

diagnosticErrors(cm)

Value

diagnostic errors returns a vector containing various diagnostic errors.

Arguments

cm

a vector containing the true positives, false positives etc, as computed by confusionMatrix.

Author

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

Details

The diagnostic errors are computed as follows:

acc = (TP+TN)/(FP+TN+TP+FN)

sens = TP/(TP+FN)

spec = TN/(FP+TN)

ppv = TP/(FP+TP)

npv = TN/(TN+FN)

lor = log(TP*TN/(FN*FP))

See Also

confusionMatrix.

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"