# calculate_distance: Calculate (column-wise) distances/similarity between two... In dynutils: Common Functionality for the 'dynverse' Packages

 calculate_distance 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` integer indicating margin of similarity/distance computation. 1 indicates rows or 2 indicates columns. `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")
```

dynutils documentation built on Oct. 11, 2022, 5:07 p.m.