compute_LC: Means of the bins according the conditional expectation or...

In EnoraBelz/Inequality: Estimation of Inequality Measures from Quantile Data

Description

Means of the bins according the conditional expectation or midpoint methods

Usage

compute_LC(ID, p, bound_min, bound_max, nb, method = "CondExp",
  whichpareto = 0.8)

Arguments

ID	vector of ID area
p	numeric, vector of probabilities
bound_min	numeric, minimum of the interval
bound_max	numeric, maximum of the interval
nb	numeric, number of the interval
method	string, type of methods ("CondExp" for conditional expectation method of "Midpoint" for midpoint method)
whichpareto	numeric, probability from which pareto tail is assumed

Value

A dataframe with the bounds, the means, the cumulative income shares and the cumulative population shares.

References

Belz (2019), Estimating Inequality Measures from Quantile Data https://halshs.archives-ouvertes.fr/halshs-02320110

See Also

run_compute_LC

Examples

data("tabulated_income")
BelAir5 = tabulated_income[tabulated_income$iris=="Bel Air 5",]
compute_LC(ID=BelAir5$iris, p=BelAir5$prop_cum_population, bound_min = BelAir5$bound_min, bound_max = BelAir5$bound_max, nb = BelAir5$prop_population, method = "CondExp")
compute_LC(ID=BelAir5$iris, p=BelAir5$prop_cum_population, bound_min = BelAir5$bound_min, bound_max = BelAir5$bound_max, nb = BelAir5$prop_population, method = "Midpoint")

EnoraBelz/Inequality documentation built on Oct. 30, 2019, 5:37 p.m.