shipunov (version 1.5)

Misclass: Misclassification (confusion) table

Description

Misclassification (confusion) table

Usage

Misclass(pred, obs, best=FALSE, ignore=NULL, quiet=FALSE, ...)

Arguments

pred

Predicted class labels

obs

Observed class labels

best

Perform a search for the classification table with minimal misclassification rate?

ignore

Vector of class labels to ignore (convert into NAs)

quiet

Output summary?

...

Arguments to 'table'

Value

Invisibly returns the table of class comparison

Details

'Misclass()' produces misclassification (confusion) 2D table based on two classifications.

The simple variant ('best=FALSE') assumes that class labels are concerted (same number of corresponding classes).

Advanced variant ('best=TRUE') can search for the best classification table (with minimal misclassification rate), this is especially useful in case of unsupervised classifications which typically return numeric labels. However, internally it generates all permutations of factor levels, thei could be very slow if there are many class labels.

Variant with 'best=TRUE' might also add empty rows (filled with zeros) to the table in case if numbers of classes are not equal.

Additional arguments could be passed for table(), for example, 'useNA="ifany"'. If supplied data contains NAs, there will be also note in the end.

It is possible to ignore (convert into NAs) some class labels with 'ignore=...', this is useful for methods like DBSCAN which output special label for outliers. In that case, note about missing data is also issued.

Alternatives: confusion matrix from 'caret::confusionMatrix()' which is more feature rich but much less flexible.

Note that partial "Misclassification errors" are reverse sensitivities, and "Mean misclassification error" is a reverse accuracy.

Association plot assocplot() is probably the best to plot misclassification table.

Examples

# NOT RUN {
iris.dist <- dist(iris[, -5], method="manhattan")
iris.hclust <- hclust(iris.dist)
iris.3 <- cutree(iris.hclust, 3)
Misclass(iris.3, iris[, 5])

set.seed(1)
iris.k <- kmeans(iris[, -5], centers=3)
Misclass(iris.k$cluster, iris[, 5])
Misclass(iris.k$cluster, iris[, 5], best=TRUE)

res <- Misclass(iris.k$cluster, iris[, 5], best=TRUE, quiet=TRUE)
## statistics implemented in caret::confusionMatrix()
binom.test(sum(diag(res)), sum(res))$conf.int
mcnemar.test(res + 1e-9) # to avoid NaN's

library(dbscan)
iris.db <- dbscan(iris[, -5], eps=0.3)
Misclass(iris.db$cluster, iris$Species, ignore=0, best=TRUE)

set.seed(NULL)
# }