# errormatrix

0th

Percentile

##### Tabulation of prediction errors by classes

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

Keywords
multivariate
##### 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).
##### 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.

##### Value

• A (named) matrix.

##### Note

Concerning the case that relative is TRUE: If a prior distribution over the classes is given, the misclassification rate that is returned as the lower right element (which is only the fraction of misclassified data) is not an estimator for the expected misclassification rate. In that case you have to multiply the individual error rates for each class (returned in the last column) with the corresponding prior probabilities and sum these up (see example below). Both error rate estimates are equal, if the fractions of classes in the data are equal to the prior probabilities.

latin1

##### concept

• Visualizing classification results
• Confusion matrix

table

• errormatrix
##### Examples
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
Documentation reproduced from package klaR, version 0.6-11, License: GPL-2

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