| dist {stats} | R Documentation |
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.
dist(x,
method = "euclidean",
diag = FALSE,
upper = FALSE,
p = 2);Available distance measures are (written for two vectors x and y):
+ euclidean: Usual distance between the two vectors (2 norm aka L2), sqrt(sum((xi - y_i)^2)).
+ maximum: Maximum distance between two components Of x And y (supremum norm)
+ manhattan: Absolute distance between the two vectors (1 norm aka L_1).
+ canberra:
sum(|x_i - y_i| / (|x_i| + |y_i|)). Terms with zero numerator And
denominator are omitted from the sum And treated as if the values were
missing.
This Is intended for non-negative values (e.g., counts), in which case the
denominator can be written in various equivalent ways; Originally, R used
x_i + y_i, then from 1998 to 2017, |xi + yi|, And then the correct
|x_i| + |y_i|.
+ binary: (aka asymmetric binary): The vectors are regarded As binary bits, so non-zero elements are 'on’ and zero elements are ‘off’. The distance is the proportion of bits in which only one is on amongst those in which at least one is on.
+ minkowski: The p norm, the pth root Of the sum Of the pth powers Of the differences Of the components.
Missing values are allowed, And are excluded from all computations involving the rows within which they occur. Further, When Inf values are involved, all pairs Of values are excluded When their contribution To the distance gave NaN Or NA. If some columns are excluded In calculating a Euclidean, Manhattan, Canberra Or Minkowski 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 "dist" method of as.matrix() And as.dist() can be used for conversion between objects of class "dist" And conventional distance matrices.
as.dist() Is a generic function. Its default method handles objects inheriting
from class "dist", Or coercible to matrices using as.matrix(). Support for
classes representing distances (also known as dissimilarities) can be added
by providing an as.matrix() Or, more directly, an as.dist method for such
a class.