`distance()`

The `distance()`

function implemented in `philentropy`

is able to compute 46 different distances/similarities between probability density functions (see `?philentropy::distance`

for details).

The `distance()`

function is implemented using the same *logic* as R’s base functions `stats::dist()`

and takes a `matrix`

or `data.frame`

as input. The corresponding `matrix`

or `data.frame`

should store probability density functions (as rows) for which distance computations should be performed.

```
# define a probability density function P
P <- 1:10/sum(1:10)
# define a probability density function Q
Q <- 20:29/sum(20:29)
# combine P and Q as matrix object
x <- rbind(P,Q)
```

Please note that when defining a `matrix`

from vectors, probability vectors should be combined as rows (`rbind()`

).

```
library(philentropy)
# compute the Euclidean Distance with default parameters
distance(x, method = "euclidean")
```

```
euclidean
0.1280713
```

For this simple case you can compare the results with R’s base function to compute the euclidean distance `stats::dist()`

.

```
# compute the Euclidean Distance using R's base function
stats::dist(x, method = "euclidean")
```

```
P
Q 0.1280713
```

However, the R base function `stats::dist()`

only computes the following distance measures: `"euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski"`

, whereas `distance()`

allows you to choose from 46 distance/similarity measures.

To find out which `method`

s are implemented in `distance()`

you can consult the `getDistMethods()`

function.

```
# names of implemented distance/similarity functions
getDistMethods()
```

```
[1] "euclidean" "manhattan" "minkowski" "chebyshev"
[5] "sorensen" "gower" "soergel" "kulczynski_d"
[9] "canberra" "lorentzian" "intersection" "non-intersection"
[13] "wavehedges" "czekanowski" "motyka" "kulczynski_s"
[17] "tanimoto" "ruzicka" "inner_product" "harmonic_mean"
[21] "cosine" "hassebrook" "jaccard" "dice"
[25] "fidelity" "bhattacharyya" "hellinger" "matusita"
[29] "squared_chord" "squared_euclidean" "pearson" "neyman"
[33] "squared_chi" "prob_symm" "divergence" "clark"
[37] "additive_symm" "kullback-leibler" "jeffreys" "k_divergence"
[41] "topsoe" "jensen-shannon" "jensen_difference" "taneja"
[45] "kumar-johnson" "avg"
```

Now you can choose any distance/similarity `method`

that serves you.

```
# compute the Jaccard Distance with default parameters
distance(x, method = "jaccard")
```

```
jaccard
0.133869
```

Analogously, in case a probability matrix is specified the following output is generated.

```
# combine three probabilty vectors to a probabilty matrix
ProbMatrix <- rbind(1:10/sum(1:10), 20:29/sum(20:29),30:39/sum(30:39))
# compute the euclidean distance between all
# pairwise comparisons of probability vectors
distance(ProbMatrix, method = "euclidean")
```

```
vec.1 vec.2 vec.3
vec.1 0.0000000 0.12807130 0.13881717
vec.2 0.1280713 0.00000000 0.01074588
vec.3 0.1388172 0.01074588 0.00000000
```

This output differs from the output of `stats::dist()`

.

```
# compute the euclidean distance between all
# pairwise comparisons of probability vectors
# using stats::dist()
stats::dist(ProbMatrix, method = "euclidean")
```

```
1 2
2 0.12807130
3 0.13881717 0.01074588
```

Whereas `distance()`

returns a symmetric distance matrix, `stats::dist()`

returns only one part of the symmetric matrix.

Now let’s compare the run times of base R and `philentropy`

. For this purpose you need to install the `microbenchmark`

package.

```
# install.packages("microbenchmark")
library(microbenchmark)
microbenchmark(
distance(x,method = "euclidean", test.na = FALSE),
dist(x,method = "euclidean"),
euclidean(x[1 , ], x[2 , ], FALSE)
)
```

```
Unit: microseconds
expr min lq mean median uq max neval
distance(x, method = "euclidean", test.na = FALSE) 26.518 28.3495 29.73174 29.2210 30.1025 62.096 100
dist(x, method = "euclidean") 11.073 12.9375 14.65223 14.3340 15.1710 65.130 100
euclidean(x[1, ], x[2, ], FALSE) 4.329 4.9605 5.72378 5.4815 6.1240 22.510 100
```

As you can see, although the `distance()`

function is quite fast, the internal checks cause it to be 2x slower than the base `dist()`

function (for the `euclidean`

example). Nevertheless, in case you need to implement a faster version of the corresponding distance measure you can type `philentropy::`

and then `TAB`

allowing you to select the base distance computation functions (written in C++), e.g. `philentropy::euclidean()`

which is almost 3x faster than the base `dist()`

function.

The advantage of `distance()`

is that it implements 46 distance measures based on base C++ functions that can be accessed individually by typing `philentropy::`

and then `TAB`

. In future versions of `philentropy`

I will optimize the `distance()`

function so that internal checks for data type correctness and correct input data will take less termination time than the base `dist()`

function.

The vast amount of available similarity metrics raises the immediate question which metric should be used for which application. Here, I will review the origin of each individual metric and will discuss the most recent literature that aims to compare these measures. I hope that users will find valuable insights and might be stimulated to conduct their own comparative research since this is a field of ongoing research.

The euclidean distance is (named after Euclid) a straight line distance between two points. Euclid argued that that the **shortest** distance between two points is always a line.

\(d = \sqrt{\sum_{i = 1}^N | P_i - Q_i |^2)}\)

\(d = \sum_{i = 1}^N | P_i - Q_i |\)

\(d = ( \sum_{i = 1}^N | P_i - Q_i |^p)^{1/p}\)

\(d = max | P_i - Q_i |\)