classError {mclust} | R Documentation |
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)
Arguments
classification |
A numeric, character vector or factor specifying the predicted class
labels. Must have the same length as |
class |
A numeric, character vector or factor of known true class labels.
Must have the same length as |
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.
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. |
See Also
Examples
(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)