run_compute_LC: Means of the bins according the conditional expectation or...
In EnoraBelz/Inequality: Estimation of Inequality Measures from Quantile Data

Description Usage Arguments Value References Examples

Means of the bins according the conditional expectation or midpoint methods for several areas

1 2	run_compute_LC(ID, p, bound_min, bound_max, nb, method = "CondExp", whichpareto = 0.8)

`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

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

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

1
2

data("tabulated_income")
run_compute_LC(ID=tabulated_income$iris, p=tabulated_income$prop_cum_population, bound_min = tabulated_income$bound_min, bound_max = tabulated_income$bound_max, nb = tabulated_income$prop_population, method = "CondExp")