Heterogeneity {L1centrality} | R Documentation |
Lorenz Curve and the Gini Coefficient
Description
Draws a Lorenz curve (the group heterogeneity plot) and computes the Gini coefficient (the group heterogeneity index).
Usage
Lorenz_plot(x, add = FALSE, ...)
Gini(x)
Arguments
x |
A numeric vector. |
add |
A logical value.
|
... |
Further graphical parameters supplied to the internal
|
Value
Lorenz_plot()
draws a Lorenz curve (the group heterogeneity plot) and returns an
invisible copy of a Gini coefficient (the group heterogeneity index).
Gini()
returns a Gini coefficient.
References
S. Kang and H.-S. Oh. On a notion of graph centrality based on L1 data depth. arXiv preprint arXiv:2404.13233, 2024.
M. O. Lorenz. Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 9(70):209–219, 1905.
See Also
Use the function with L1cent()
or L1centLOC()
, and compare
distributions of the centrality measurements across several groups and
graphs. Summary methods in this package come with the Gini coefficient.
Examples
vertex_weight <- igraph::V(MCUmovie)$worldwidegross
cent <- L1cent(MCUmovie, eta=vertex_weight)
gini <- Lorenz_plot(cent, asp=1)
graphics::abline(0,1,lty=2)
# group heterogeneity index
gini
gini == Gini(cent)