# R/ARI.R In VarSelLCM: Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

```###################################################################################
##'
##' @description
##' This function computes the Adjusted Rand Index
##'
##' @param x vector defining a partition.
##' @param y vector defining a partition of whose length is equal to the length of x.
##'
##' @return numeric
##'
##' @references L. Hubert and P. Arabie (1985) Comparing Partitions, Journal of the Classification, 2, pp. 193-218.
##' @examples
##' x <- sample(1:2, 20, replace=TRUE)
##' y <- x
##' y[1:5] <- sample(1:2, 5, replace=TRUE)
##' ARI(x, y)
##' @export
##'
##'
ARI <- function (x, y){
#### Tests on the input arguments
if ((length(x) != length(y)))
stop("The two partitions must be vectors of same length")

####

ari <- 0
if ((length(unique(x)) + length(unique(y))) == 2) ari <- 1
conting <- table(x, y)
a <- sum(choose(conting, 2))
b <- sum(choose(rowSums(conting), 2)) - a
c <- sum(choose(colSums(conting), 2)) - a
d <- choose(sum(conting), 2) - a - b - c
ari <- (a - (a + b) * (a + c)/(a + b + c + d))/((a + b +
a + c)/2 - (a + b) * (a + c)/(a + b + c + d))
return(ari)
}
```

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VarSelLCM documentation built on May 2, 2019, 4:59 p.m.