# ci_mpi: Mazziotta-Pareto Index (MPI) method In Compind: Composite Indicators Functions

## 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

 `1` ```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".

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.

`ci_bod`, `normalise_ci`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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") ```

### Example output

```Loading required package: Benchmarking