compareCluster: Clustering comparison criteria
In michaelfop/LCAvarsel: Variable Selection for Latent Class Analysis

Description Usage Arguments Details Value References Examples

Computes some criteria for comparing two classifications of the data points.

1	compareCluster(class1, class2)

`class1`	A numeric or character vector of class labels.
`class2`	A numeric or character vector of class labels. Must be same length of `class1`.

The Jaccard, Rand and adjusted Rand indices measure the agreement between two partitions of the units. These indices vary in the interval [0,1] and a value of 1 corresponds to a perfect correspondence. Note that sometimes the adjusted Rand index could take negative values (see Hubert, Arabie, 1985). The variation of information is a measure of the distance between the two clusterings and a small value is indication of closeness.

A list containing:

`tab`	The confusion matrix between the two clusterings.
`jaccard`	Jaccard index.
`RI`	Rand index.
`ARI`	Adjusted Rand index.
`varInfo`	Variation of information between the two clusterings.

Hubert, L. and Arabie, P. (1985). Comparing partitions. Journal of Classification, 2193-218.

Meila, M. (2007). Comparing clusterings - an information based distance. Journal of Multivariate Analysis, 98, 873-895.

cl1 <- sample(1:3, 100, replace = TRUE)
cl2 <- sample(letters[1:4], 100, replace = TRUE)
compareCluster(cl1, cl2)
compareCluster(cl1, cl1)   # perfect matching