| dist {mva} | R Documentation |
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", diag = FALSE, upper = FALSE)
print.dist(dist.obj, diag = NULL, upper = NULL)
as.matrix.dist(dist.obj)
as.dist(m, diag = NULL, upper = NULL)
Arguments
x |
A matrix or (data frame). Distances between the rows of
|
method |
The distance measure to be used. This must be one of
|
diag |
A logical value indicating whether the diagonal of the
distance matrix should be printed by |
upper |
A logical value indicating whether the upper triangle of the
distance matrix should be printed by |
m |
A distance matrix to be converted to a dist object (only lower triangle is used, the rest is ignored). |
Details
Available distance measures are (written for two vectors x and
y):
Euclidean: Usual square distance between the two vectors (2 norm).
Maximum: Maximum distance between two components of
xandy(supremum norm)Manhattan: Absolute distance between the two vectors (1 norm).
Canberra:
\sum_i |x_i - y_i| / |x_i + y_i|Count the number of different bits in
xandywhere at least one of the two bits is 1, i.e., components where both bits are zero are ignored.
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.
Value
The lower triangle of the distance matrix stored by columns in a
single vector. The vector has the attributes "Size",
"Diag", "Upper", "Labels" and "class" equal
to "dist".
References
Mardia, K. V., J. T. Kent and J. M. Bibby (1979). Multivariate Analysis, London: Academic Press.
See Also
hclust.
Examples
x <- matrix(rnorm(100),nrow=5)
dist(x)
dist(x, diag = TRUE)
dist(x, upper = TRUE)
m <- as.matrix(dist(x))
as.dist(m)