View source: R/calculate_distance.R
calculate_distance | R Documentation |
These matrices can be dense or sparse.
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()
x |
A numeric matrix, dense or sparse. |
y |
(Optional) a numeric matrix, dense or sparse, with |
method |
Which distance method to use. Options are: |
margin |
integer indicating margin of similarity/distance computation. 1 indicates rows or 2 indicates columns. |
diag |
if |
drop0 |
if |
## 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")
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