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

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

true

Vector of true classes.

predicted

Vector of predicted classes.

relative

Logical. If `TRUE`

rows are normalized
to show relative frequencies (see below).

A (named) matrix.

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”.

# 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 # }