R: Classification error

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`.
`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.

### 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

`map` `mapClass`, `table`

### 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)

[Package mclust version 5.4.9 Index]