R: Calculate (column-wise) distances/similarity between two...

calculate_distance {dynutils}

R Documentation

Calculate (column-wise) distances/similarity between two matrices

Description

These matrices can be dense or sparse.

Usage

calculate_distance(
  x,
  y = NULL,
  method = c("pearson", "spearman", "cosine", "euclidean", "chisquared", "hamming",
    "kullback", "manhattan", "maximum", "canberra", "minkowski"),
  margin = 1,
  diag = FALSE,
  drop0 = FALSE
)

list_distance_methods()

calculate_similarity(
  x,
  y = NULL,
  margin = 1,
  method = c("spearman", "pearson", "cosine"),
  diag = FALSE,
  drop0 = FALSE
)

list_similarity_methods()

Arguments

`x`	A numeric matrix, dense or sparse.
`y`	(Optional) a numeric matrix, dense or sparse, with `nrow(x) == nrow(y)`.
`method`	Which distance method to use. Options are: `"cosine"`, `"pearson"`, `"spearman"`, `"euclidean"`, and `"manhattan"`.
`margin`	Which margin to use for the pairwise comparison. 1 => rowwise, 2 => columnwise.
`diag`	if `TRUE`, only compute diagonal elements of the similarity/distance matrix; useful when comparing corresponding rows or columns of 'x' and 'y'.
`drop0`	if `TRUE`, zero values are removed regardless of min_simil or rank.

Examples

## Generate two matrices with 50 and 100 samples
library(Matrix)
x <- Matrix::rsparsematrix(50, 1000, .01)
y <- Matrix::rsparsematrix(100, 1000, .01)

dist_euclidean <- calculate_distance(x, y, method = "euclidean")
dist_manhattan <- calculate_distance(x, y, method = "manhattan")
dist_spearman <- calculate_distance(x, y, method = "spearman")
dist_pearson <- calculate_distance(x, y, method = "pearson")
dist_angular <- calculate_distance(x, y, method = "cosine")

[Package dynutils version 1.0.8 Index]