| calculate_distance {dynutils} | R Documentation |
These matrices can be dense or sparse.
calculate_distance(x, y = NULL, method = c("pearson", "spearman",
"cosine", "euclidean", "manhattan"), margin = 1)
list_distance_methods()
calculate_similarity(x, y = NULL, margin = 1, method = c("spearman",
"pearson", "cosine"))
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 |
Which margin to use for the pairwise comparison. 1 => rowwise, 2 => columnwise. |
## 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")