Computes the adjusted Rand index comparing two classifications.

adjustedRandIndex(x, y)

## Arguments

x A numeric or character vector of class labels. A numeric or character vector of class labels. The length of y should be the same as that of x.

## Value

The adjusted Rand index comparing the two partitions (a scalar). This index has zero expected value in the case of random partition, and it is bounded above by 1 in the case of perfect agreement between two partitions.

L. Hubert and P. Arabie (1985) Comparing Partitions, Journal of the Classification, 2, pp. 193-218.

classError, mapClass, table

## Examples

a <- rep(1:3, 3)
a
#>  1 2 3 1 2 3 1 2 3b <- rep(c("A", "B", "C"), 3)
b
#>  "A" "B" "C" "A" "B" "C" "A" "B" "C"adjustedRandIndex(a, b)
#>  1
a <- sample(1:3, 9, replace = TRUE)
a
#>  3 2 3 3 2 3 1 2 2b <- sample(c("A", "B", "C"), 9, replace = TRUE)
b
#>  "A" "A" "B" "A" "B" "C" "C" "B" "B"adjustedRandIndex(a, b)
#>  0.08695652
a <- rep(1:3, 4)
a
#>   1 2 3 1 2 3 1 2 3 1 2 3b <- rep(c("A", "B", "C", "D"), 3)
b
#>   "A" "B" "C" "D" "A" "B" "C" "D" "A" "B" "C" "D"adjustedRandIndex(a, b)
#>  -0.2790698
irisHCvvv <- hc(modelName = "VVV", data = iris[,-5])
cl3 <- hclass(irisHCvvv, 3)
adjustedRandIndex(summary(irisBIC,iris[,-5])$classification,iris[,5]) #>  0.5681159adjustedRandIndex(summary(irisBIC,iris[,-5],G=3)$classification,iris[,5])
#>  0.9038742