russel_rao: Computes the Russel-Rao similarity of two clusterings of the...
In ramhiser/clusteval: Evaluation of Clustering Algorithms

Description Usage Arguments Details Value Examples

For two clusterings of the same data set, this function calculates the Russel-Rao similarity coefficient of the clusterings from the comemberships of the observations. Basically, the comembership is defined as the pairs of observations that are clustered together.

1	russel_rao(labels1, labels2)

`labels1`	a vector of `n` clustering labels
`labels2`	a vector of `n` clustering labels

To calculate the Russel-Rao similarity, we compute the 2x2 contingency table, consisting of the following four cells:

n_11:: the number of observation pairs where both observations are comembers in both clusterings
n_10:: the number of observation pairs where the observations are comembers in the first clustering but not the second
n_01:: the number of observation pairs where the observations are comembers in the second clustering but not the first
n_00:: the number of observation pairs where neither pair are comembers in either clustering

The Russel-Rao similarity is defined as:

\frac{n_{11}}{n_{11} + n_{10} + n_{01} + n_{00}}.

To compute the contingency table, we use the comembership_table function.

the Russel-Rao index for the two sets of cluster labels

## Not run: 
# We generate K = 3 labels for each of n = 10 observations and compute the
# Russel-Rao similarity index between the two clusterings.
set.seed(42)
K <- 3
n <- 10
labels1 <- sample.int(K, n, replace = TRUE)
labels2 <- sample.int(K, n, replace = TRUE)
russel_rao(labels1, labels2)

# Here, we cluster the \code{\link{iris}} data set with the K-means and
# hierarchical algorithms using the true number of clusters, K = 3.
# Then, we compute the Russel_Rao similarity index between the two clusterings.
iris_kmeans <- kmeans(iris[, -5], centers = 3)$cluster
iris_hclust <- cutree(hclust(dist(iris[, -5])), k = 3)
russel_rao(iris_kmeans, iris_hclust)

## End(Not run)