dist_one_many {philentropy}

Distances and Similarities between One and Many Probability Density Functions

Description

This functions computes the distance/dissimilarity between one probability density functions and a set of probability density functions.

Usage

dist_one_many(
  P,
  dists,
  method,
  p = NA_real_,
  testNA = TRUE,
  unit = "log",
  epsilon = 1e-05
)

Arguments

P

a numeric vector storing the first distribution.

dists

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 vector of distance values

Examples

set.seed(2020-08-20)
P <- 1:10 / sum(1:10)
M <- t(replicate(100, sample(1:10, size = 10) / 55))
dist_one_many(P, M, method = "euclidean", testNA = FALSE)