View source: R/distance_functions.r
rdist | R Documentation |
rdist
provide a common framework to calculate distances. There are three main functions:
rdist
computes the pairwise distances between observations in one matrix and returns a dist
object,
pdist
computes the pairwise distances between observations in one matrix and returns a matrix
, and
cdist
computes the distances between observations in two matrices and returns a matrix
.
In particular the cdist
function is often missing in other distance functions. All
calculations involving NA
values will consistently return NA
.
rdist(X, metric = "euclidean", p = 2L)
pdist(X, metric = "euclidean", p = 2)
cdist(X, Y, metric = "euclidean", p = 2)
X, Y |
A matrix |
metric |
The distance metric to use |
p |
The power of the Minkowski distance |
Available distance measures are (written for two vectors v and w):
"euclidean"
: \sqrt{\sum_i(v_i - w_i)^2}
"minkowski"
: (\sum_i|v_i - w_i|^p)^{1/p}
"manhattan"
: \sum_i(|v_i-w_i|)
"maximum"
or "chebyshev"
: \max_i(|v_i-w_i|)
"canberra"
: \sum_i(\frac{|v_i-w_i|}{|v_i|+|w_i|})
"angular"
: \cos^{-1}(cor(v, w))
"correlation"
: \sqrt{\frac{1-cor(v, w)}{2}}
"absolute_correlation"
: \sqrt{1-|cor(v, w)|^2}
"hamming"
: (\sum_i v_i \neq w_i) / \sum_i 1
"jaccard"
: (\sum_i v_i \neq w_i) / \sum_i 1_{v_i \neq 0 \cup w_i \neq 0}
Any function that defines a distance between two vectors.
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