amap (version 0.8-7)

Dist: Distance Matrix Computation

Description

This function computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.

Usage

Dist(x, method = "euclidean", nbproc = 2, diag = FALSE, upper = FALSE)

Value

An object of class "dist".

The lower triangle of the distance matrix stored by columns in a vector, say do. If n is the number of observations, i.e., n <- attr(do, "Size"), then for $i < j <= n$,="" the="" dissimilarity="" between="" (row)="" i="" and="" j="" is="" do[n*(i-1) - i*(i-1)/2 + j-i]. The length of the vector is $n*(n-1)/2$, i.e., of order $n^2$.

The object has the following attributes (besides "class" equal to "dist"):Sizeinteger, the number of observations in the dataset.Labelsoptionally, contains the labels, if any, of the observations of the dataset.Diag, Upperlogicals corresponding to the arguments diag and upper above, specifying how the object should be printed.calloptionally, the call used to create the object.methodsoptionally, the distance method used; resulting form dist(), the (match.arg()ed) method argument.

Details

Available distance measures are (written for two vectors $x$ and $y$): [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Missing values are allowed, and are excluded from all computations involving the rows within which they occur. If some columns are excluded in calculating a Euclidean, Manhattan or Canberra distance, the sum is scaled up proportionally to the number of columns used. If all pairs are excluded when calculating a particular distance, the value is NA.

The functions as.matrix.dist() and as.dist() can be used for conversion between objects of class "dist" and conventional distance matrices and vice versa.

References

Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979) Multivariate Analysis. London: Academic Press.

Wikipedia http://en.wikipedia.org/wiki/Kendall_tau_distance

See Also

daisy in the cluster package with more possibilities in the case of mixed (contiuous / categorical) variables. dist hcluster.

Examples

x <- matrix(rnorm(100), nrow=5)
Dist(x)
Dist(x, diag = TRUE)
Dist(x, upper = TRUE)


## compute dist with 8 threads
Dist(x,nbproc=8)


Dist(x,method="abscorrelation")
Dist(x,method="kendall")