Compind (version 3.2)

ci_mpi: Mazziotta-Pareto Index (MPI) method

Description

Mazziotta-Pareto Index (MPI) is a non-linear composite index method which transforms a set of individual indicators in standardized variables and summarizes them using an arithmetic mean adjusted by a "penalty" coefficient related to the variability of each unit (method of the coefficient of variation penalty).

Usage

ci_mpi(x, indic_col, penalty="POS")

Value

An object of class "CI". This is a list containing the following elements:

ci_mpi_est

Composite indicator estimated values.

ci_method

Method used; for this function ci_method="mpi".

Arguments

x

A data.frame containing simple indicators.

indic_col

Simple indicators column number.

penalty

Penalty direction; Use "POS" (default) in case of 'increasing' or 'positive' composite index (e.g., well-being index)), "NEG" in case of 'decreasing' or 'negative' composite index (e.g., poverty index).

Author

Vidoli F.

References

De Muro P., Mazziotta M., Pareto A. (2011), "Composite Indices of Development and Poverty: An Application to MDGs", Social Indicators Research, Volume 104, Number 1, pp. 1-18.

See Also

ci_bod, normalise_ci

Examples

data(EU_NUTS1)

# Please, pay attention. MPI can be calculated only with two standardizations methods:
# Classic MPI - method=1, z.mean=100 and z.std=10
# Correct MPI - method=2
# For more info, please see references.

data_norm = normalise_ci(EU_NUTS1,c(2:3),c("NEG","POS"),method=1,z.mean=100, z.std=10)
CI = ci_mpi(data_norm$ci_norm, penalty="NEG")

data(EU_NUTS1)
CI = ci_mpi(EU_NUTS1,c(2:3),penalty="NEG")