Rtapas (version 1.2)

gini_RSV: The Gini coefficient adjusted for negative attributes (Raffinetti, Siletti, & Vernizzi, 2015)

Description

Computes the Gini coefficient adjusted for negative (even weighted) data.

Usage

gini_RSV(y)

Value

The value of the Gini coefficient adjusted for negative attributes.

Arguments

y

a vector of attributes containing even negative elements

NOTE

It produces a conventional Gini coefficient (G) (Ultsch and Lötsch 2017) if all output values are positive, or a normalized Gini coefficient (G*) (Raffinetti et al. 2015) if negative values are produced due to corrected frequencies (if res.fq = TRUE or diff.fq = TRUE). For more details see Raffinetti et al. (2015).

References

Ultsch A., Lötsch J. (2017). A data science based standardized Gini index as a Lorenz dominance preserving measure of the inequality of distributions. PLOS ONE. 12:e0181572. tools:::Rd_expr_doi("10.1371/journal.pone.0181572")

Raffinetti E., Siletti E., Vernizzi A. (2015). On the Gini coefficient normalization when attributes with negative values are considered. Stat Methods Appl. 24:507–521. tools:::Rd_expr_doi("10.1007/s10260-014-0293-4")

Examples

# \donttest{
data(nuc_cp)
N = 10 #for the example, we recommend 1e+4 value
n = 15
# Maximizing congruence
NPc_PACo <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco",
               symmetric = FALSE, ei.correct = "sqrt.D",
               percentile = 0.01, res.fq = FALSE)
gini_RSV(y = NPc_PACo)
# }