Gini.coef {LorenzRegression}

Concentration index of y wrt x

Description

Gini.coef computes the concentration index of a vector y with respect to another vector x. If y and x are identical, the obtained concentration index boils down to the Gini coefficient.

Usage

Gini.coef(
  y,
  x = y,
  na.rm = TRUE,
  ties.method = c("mean", "random"),
  seed = NULL,
  weights = NULL
)

Arguments

y

variable of interest.

x

variable to use for the ranking. By default x=y, and the obtained concentration index is the Gini coefficient of y.

na.rm

should missing values be deleted. Default value is TRUE. If FALSE is selected, missing values generate an error message

ties.method

What method should be used to break the ties in the rank index. Possible values are "mean" (default value) or "random". If "random" is selected, the ties are broken by further ranking in terms of a uniformly distributed random variable. If "mean" is selected, the average rank method is used.

seed

fixes what seed is imposed for the generation of the vector of uniform random variables used to break the ties. Default is NULL, in which case no seed is imposed.

weights

vector of sample weights. By default, each observation is given the same weight.

Value

The value of the concentration index (or Gini coefficient)

See Also

Lorenz.curve, Lorenz.graphs

Examples

data(Data.Incomes)
# We first compute the Gini coefficient of Income
Y <- Data.Incomes$Income
Gini.coef(y = Y)
# Then we compute the concentration index of Income with respect to Age
X <- Data.Incomes$Age
Gini.coef(y = Y, x = X)