Compind (version 2.0)

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")

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).

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".

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

# NOT RUN {
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")
# }