1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
|
\name{adjustedRandIndex}
\alias{adjustedRandIndex}
\title{
Adjusted Rand Index
}
\description{
Computes the adjusted Rand index comparing two classifications.
}
\usage{
adjustedRandIndex(x, y)
}
\arguments{
\item{x}{
A numeric or character vector of class labels.
}
\item{y}{
A numeric or character vector of class labels.
The length of \code{y} should be the same as that of \code{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.
}
\references{
L. Hubert and P. Arabie (1985) Comparing Partitions, \emph{Journal of the Classification}, 2, pp. 193-218.
}
\seealso{
\code{\link{classError}},
\code{\link{mapClass}},
\code{\link{table}}
}
\examples{
a <- rep(1:3, 3)
a
b <- rep(c("A", "B", "C"), 3)
b
adjustedRandIndex(a, b)
a <- sample(1:3, 9, replace = TRUE)
a
b <- sample(c("A", "B", "C"), 9, replace = TRUE)
b
adjustedRandIndex(a, b)
a <- rep(1:3, 4)
a
b <- rep(c("A", "B", "C", "D"), 3)
b
adjustedRandIndex(a, b)
irisHCvvv <- hc(modelName = "VVV", data = iris[,-5])
cl3 <- hclass(irisHCvvv, 3)
adjustedRandIndex(cl3,iris[,5])
irisBIC <- mclustBIC(iris[,-5])
adjustedRandIndex(summary(irisBIC,iris[,-5])$classification,iris[,5])
adjustedRandIndex(summary(irisBIC,iris[,-5],G=3)$classification,iris[,5])
}
\keyword{cluster}
|
