klaR (version 0.6-15)

# errormatrix: Tabulation of prediction errors by classes

## Description

Cross-tabulates true and predicted classes with the option to show relative frequencies.

## Usage

`errormatrix(true, predicted, relative = FALSE)`

## Arguments

true

Vector of true classes.

predicted

Vector of predicted classes.

relative

Logical. If `TRUE` rows are normalized to show relative frequencies (see below).

## Value

A (named) matrix.

## Details

Given vectors of true and predicted classes, a (symmetric) table of misclassifications is constructed.

Element [i,j] shows the number of objects of class i that were classified as class j; so the main diagonal shows the correct classifications. The last row and column show the corresponding sums of misclassifications, the lower right element is the total sum of misclassifications.

If ‘`relative`’ is `TRUE`, the rows are normalized so they show relative frequencies instead. The lower right element now shows the total error rate, and the remaining last row sums up to one, so it shows “where the misclassifications went”.

`table`

## Examples

```# NOT RUN {
data(iris)
library(MASS)
x <- lda(Species ~ Sepal.Length + Sepal.Width, data=iris)
y <- predict(x, iris)

# absolute numbers:
errormatrix(iris\$Species, y\$class)

# relative frequencies:
errormatrix(iris\$Species, y\$class, relative = TRUE)

# percentages:
round(100 * errormatrix(iris\$Species, y\$class, relative = TRUE), 0)

# expected error rate in case of class prior:
indiv.rates <- errormatrix(iris\$Species, y\$class, relative = TRUE)[1:3, 4]
prior <- c("setosa" = 0.2, "versicolor" = 0.3, "virginica" = 0.5)
total.rate <- t(indiv.rates) %*% prior
total.rate
# }
```