rdist: an R package for distances

Source: R/distance_functions.r, R/rdist-package.r

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)

Arguments

X, Y

A matrix
metric

The distance metric to use
p

The power of the Minkowski distance

Details

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.