Gini(x, n = rep(1, length(x)), unbiased = TRUE, conf.level = NA, R = 1000, type = "bca", na.rm = FALSE)
NA
, if x contains negative elements.TRUE
the bootstrap confidence intervals are calculated.
If set to NA
(default) no confidence intervals are returned.
"norm"
,"basic"
, "stud"
,
"perc"
or "bca"
).
This argument is ignored if no confidence intervals are to be calculated.conf.level
is set to NA
then the result will be
then the result will be
and
if a conf.level
is provided, a named numeric vector with 3 elements:type
argument ("bca"
).
Dixon (1987) describes a refinement of the bias-corrected method known as 'accelerated' -
this produces values very closed to conventional bias corrected intervals.
(Iain Buchan (2002) Calculating the Gini coefficient of inequality, see: http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm)Cowell, F. A. (1995) Measuring Inequality Harvester Wheatshef: Prentice Hall.
Marshall, Olkin (1979) Inequalities: Theory of Majorization and Its Applications. New York: Academic Press.
Glasser C. (1962) Variance formulas for the mean difference and coefficient of concentration. Journal of the American Statistical Association 57:648-654.
Mills JA, Zandvakili A. (1997). Statistical inference via bootstrapping for measures of inequality. Journal of Applied Econometrics 12:133-150.
Dixon, PM, Weiner J., Mitchell-Olds T, Woodley R. (1987) Boot-strapping the Gini coefficient of inequality. Ecology 68:1548-1551.
Efron B, Tibshirani R. (1997) Improvements on cross-validation: The bootstrap method. Journal of the American Statistical Association 92:548-560.
Herfindahl
, Rosenbluth
for concentration measures,
Lc
for the Lorenz curve
ineq()
in the package ineq contains additional inequality measures# generate vector (of incomes) x <- c(541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261) # compute Gini coefficient Gini(x) # working with weights fl <- c(2.5, 7.5, 15, 35, 75, 150) # midpoints of classes n <- c(25, 13, 10, 5, 5, 2) # frequencies # with confidence intervals Gini(fl, n, conf.level=0.95, unbiased=FALSE) # some special cases x <- c(10, 10, 0, 0, 0) plot(Lc(x)) Gini(x, unbiased=FALSE) # the same with weights Gini(x=c(10, 0), n=c(2,3), unbiased=FALSE) # perfect balance Gini(c(10, 10, 10))
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