Description Usage Arguments Details Value Examples

For two clusterings of the same data set, this function calculates the adjusted Rand similarity coefficient of the clusterings from the comemberships of the observations.

1 | ```
adjusted_rand(labels1, labels2)
``` |

`labels1` |
a vector of |

`labels2` |
a vector of |

The adjusted Rand index is a variant of the Rand index that is corrected for chance. We refer the interested reader to the Wikipedia entry for an overview of the formula: http://en.wikipedia.org/wiki/Rand_index#Adjusted_Rand_index

the adjusted Rand index for the two sets of cluster labels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Not run:
# We generate K = 3 labels for each of n = 10 observations and compute the
# adjusted Rand 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)
adjusted_rand(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 adjusted Rand index between the two clusterings.
iris_kmeans <- kmeans(iris[, -5], centers = 3)$cluster
iris_hclust <- cutree(hclust(dist(iris[, -5])), k = 3)
adjusted_rand(iris_kmeans, iris_hclust)
## End(Not run)
``` |

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