mld.wtd: Mean log deviation
In ReneSchulenberg/dineq: Decomposition of (Income) Inequality

Description Usage Arguments Details Value Source References Examples

Returns the (optional weighted) mean log deviation for a vector.

1	mld.wtd(x, weights = NULL)

`x`	a numeric vector containing at least non-negative elements.
`weights`	an optional vector of weights of x to be used in the computation of the mean log deviation. Should be NULL or a numeric vector.

The mean log deviation is a measure of inequality among values of a distribution. It is a member of the Generalized Entropy Measures. Also referred to as GE(0). A value of zero is the lowest possible inequality. The measure does not have an upper bound for the highest inequality. It uses a logarithmic transformation of the values of the distribution. Therefore it cannot handle negative or zero values. Those are excluded from the computation in this function. The mean log deviation is more sensitive for changes in the lower tail of the distribution.

Extension of the calcGEI function in IC2 package in order to handle missings.

the value of the mean log deviation index.

Plat, D. (2012). IC2: Inequality and Concentration Indices and Curves. R package version 1.0-1. https://CRAN.R-project.org/package=IC2

Haughton, J. and S. Khandker. (2009) Handbook on poverty and inequality, Washington, DC: World Bank.

Cowell F. (2000) Measurement of Inequality. In Atkinson A. and Bourguignon F. (eds.) Handbook of Income Distribution. Amsterdam: Elsevier, p. 87-166.

#calculate mean log deviation using Mexican Income data set
data(mex_inc_2008)

#unweighted mean log deviation:
mld.wtd(mex_inc_2008$income)

#weighted mean log deviation:
mld.wtd(x=mex_inc_2008$income, weights=mex_inc_2008$factor)