R: Distances and Similarities between Many Probability Density...

dist_many_many {philentropy}

R Documentation

Distances and Similarities between Many Probability Density Functions

Description

This functions computes the distance/dissimilarity between two sets of probability density functions.

Usage

dist_many_many(
  dists1,
  dists2,
  method,
  p = NA_real_,
  testNA = TRUE,
  unit = "log",
  epsilon = 1e-05
)

Arguments

`dists1`	a numeric matrix storing distributions in its rows.
`dists2`	a numeric matrix storing distributions in its rows.
`method`	a character string indicating whether the distance measure that should be computed.
`p`	power of the Minkowski distance.
`testNA`	a logical value indicating whether or not distributions shall be checked for `NA` values.
`unit`	type of `log` function. Option are `unit = "log"` `unit = "log2"` `unit = "log10"`
`epsilon`	epsilon a small value to address cases in the distance computation where division by zero occurs. In these cases, x / 0 or 0 / 0 will be replaced by `epsilon`. The default is `epsilon = 0.00001`. However, we recommend to choose a custom `epsilon` value depending on the size of the input vectors, the expected similarity between compared probability density functions and whether or not many 0 values are present within the compared vectors. As a rough rule of thumb we suggest that when dealing with very large input vectors which are very similar and contain many `0` values, the `epsilon` value should be set even smaller (e.g. `epsilon = 0.000000001`), whereas when vector sizes are small or distributions very divergent then higher `epsilon` values may also be appropriate (e.g. `epsilon = 0.01`). Addressing this `epsilon` issue is important to avoid cases where distance metrics return negative values which are not defined and only occur due to the technical issues of computing x / 0 or 0 / 0 cases.

Value

A matrix of distance values

Examples

  set.seed(2020-08-20)
  M1 <- t(replicate(10, sample(1:10, size = 10) / 55))
  M2 <- t(replicate(10, sample(1:10, size = 10) / 55))
  result <- dist_many_many(M1, M2, method = "euclidean", testNA = FALSE)

[Package philentropy version 0.6.0 Index]