# inequity: Inequity (Inequality) Measures In genieclust: Fast and Robust Hierarchical Clustering with Noise Points Detection

 gini_index R Documentation

## Inequity (Inequality) Measures

### Description

gini_index() gives the normalised Gini index, bonferroni_index() implements the Bonferroni index, and devergottini_index() implements the De Vergottini index.

### Usage

gini_index(x)

bonferroni_index(x)

devergottini_index(x)

### Arguments

 x numeric vector of non-negative values

### Details

These indices can be used to quantify the "inequity" of a numeric sample. They can be perceived as measures of data dispersion. For constant vectors (perfect equity), the indices yield values of 0. Vectors with all elements but one equal to 0 (perfect inequity), are assigned scores of 1. They follow the Pigou-Dalton principle (are Schur-convex): setting x_i = x_i - h and x_j = x_j + h with h > 0 and x_i - h ≥q x_j + h (taking from the "rich" and giving to the "poor") decreases the inequity.

These indices have applications in economics, amongst others. The Genie clustering algorithm uses the Gini index as a measure of the inequality of cluster sizes.

The normalised Gini index is given by:

G(x_1,…,x_n) = \frac{ ∑_{i=1}^{n} (n-2i+1) x_{σ(n-i+1)} }{ (n-1) ∑_{i=1}^n x_i },

The normalised Bonferroni index is given by:

B(x_1,…,x_n) = \frac{ ∑_{i=1}^{n} (n-∑_{j=1}^i \frac{n}{n-j+1}) x_{σ(n-i+1)} }{ (n-1) ∑_{i=1}^n x_i }.

The normalised De Vergottini index is given by:

V(x_1,…,x_n) = \frac{1}{∑_{i=2}^n \frac{1}{i}} ≤ft( \frac{ ∑_{i=1}^n ≤ft( ∑_{j=i}^{n} \frac{1}{j}\right) x_{σ(n-i+1)} }{∑_{i=1}^{n} x_i} - 1 \right).

Here, σ is an ordering permutation of (x_1,…,x_n).

Time complexity: O(n) for sorted (increasingly) data. Otherwise, the vector will be sorted.

### Value

The value of the inequity index, a number in [0, 1].

### Author(s)

Marek Gagolewski and other contributors

### References

Bonferroni C., Elementi di Statistica Generale, Libreria Seber, Firenze, 1930.

Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 2016, pp. 8-23. doi: 10.1016/j.ins.2016.05.003

Gini C., Variabilita e Mutabilita, Tipografia di Paolo Cuppini, Bologna, 1912.

The official online manual of genieclust at https://genieclust.gagolewski.com/

Gagolewski M., genieclust: Fast and robust hierarchical clustering, SoftwareX 15:100722, 2021, doi: 10.1016/j.softx.2021.100722.

### Examples

gini_index(c(2, 2, 2, 2, 2))   # no inequality
gini_index(c(0, 0, 10, 0, 0))  # one has it all
gini_index(c(7, 0, 3, 0, 0))   # give to the poor, take away from the rich
gini_index(c(6, 0, 3, 1, 0))   # (a.k.a. Pigou-Dalton principle)
bonferroni_index(c(2, 2, 2, 2, 2))
bonferroni_index(c(0, 0, 10, 0, 0))
bonferroni_index(c(7, 0, 3, 0, 0))
bonferroni_index(c(6, 0, 3, 1, 0))
devergottini_index(c(2, 2, 2, 2, 2))
devergottini_index(c(0, 0, 10, 0, 0))
devergottini_index(c(7, 0, 3, 0, 0))
devergottini_index(c(6, 0, 3, 1, 0))

genieclust documentation built on Sept. 5, 2022, 9:05 a.m.