Rtapas (version 1.2)

gini_ci: Plot the confidence intervals of Gini coefficient

Description

Computes and displays in a boxplot the Gini coefficient and their confidence intervals of the frequency (or residual/corrected frequencies) distributions of the estimated (in)congruence metric (with any of the three global-fit methods) of the individual host-symbiont associations.

Usage

gini_ci(LF_1, M01, ylab = "Gini coefficient", plot = TRUE, ...)

Value

The Gini values obtained and their representation in a boxplot, with their confidence intervals.

Arguments

LF_1

Vector of statistics produced with max_cong() or max_incong() for "geoD", "paco" or "paraF".

M01

Matrix produced with prob_statistic() for "geoD", "paco" or "paraF" using LF_1.

ylab

Title of the y label.

plot

Default is "TRUE", plots the Gini coefficient and its confidence intervals in a boxplot.

...

Any optional argument admissible in boxplot()

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

data(nuc_cp)
N = 1 #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)

# Loaded directly from dataset
# THSC <- trimHS_maxC(N, np_matrix, n)
# pp_treesPACo_cong <- prob_statistic(ths = THSc, np_matrix, NUC_500tr[1:10],
#                         CP_500tr[1:10], freqfun = "paco", NPc_PACo,
#                         symmetric = FALSE, ei.correct = "sqrt.D",
#                         percentile = 0.01, correction = "none")

gini_ci(LF_1 = NPc_PACo, M01 = pp_treesPACo_cong,
         ylab = "Gini Coefficient (G)",
         plot = TRUE, ylim = c(0.3, 0.8))
abline(h = 1/3) # because res.fq = TRUE