classError: Classification error

Description

Computes the errore rate of a given classification relative to the known classes, and the location of misclassified data points.

Usage

classError(classification, class)

Value

A list with the following two components:

misclassified: The indexes of the misclassified data points in a minimum error mapping between the predicted classification and the known true classes.
errorRate: The error rate corresponding to a minimum error mapping between the predicted classification and the known true classes.

Arguments

classification: A numeric, character vector or factor specifying the predicted class labels. Must have the same length as class.
class: A numeric, character vector or factor of known true class labels. Must have the same length as classification.

Details

If more than one mapping between predicted classification and the known truth corresponds to the minimum number of classification errors, only one possible set of misclassified observations is returned.

Examples

Run this code

(a <- rep(1:3, 3))
(b <- rep(c("A", "B", "C"), 3))
classError(a, b)

(a <- sample(1:3, 9, replace = TRUE))
(b <- sample(c("A", "B", "C"), 9, replace = TRUE))
classError(a, b)

class <- factor(c(5,5,5,2,5,3,1,2,1,1), levels = 1:5)
probs <- matrix(c(0.15, 0.01, 0.08, 0.23, 0.01, 0.23, 0.59, 0.02, 0.38, 0.45, 
                  0.36, 0.05, 0.30, 0.46, 0.15, 0.13, 0.06, 0.19, 0.27, 0.17, 
                  0.40, 0.34, 0.18, 0.04, 0.47, 0.34, 0.32, 0.01, 0.03, 0.11, 
                  0.04, 0.04, 0.09, 0.05, 0.28, 0.27, 0.02, 0.03, 0.12, 0.25, 
                  0.05, 0.56, 0.35, 0.22, 0.09, 0.03, 0.01, 0.75, 0.20, 0.02),
                nrow = 10, ncol = 5)
cbind(class, probs, map = map(probs))
classError(map(probs), class)