ci_ampi: Adjusted Mazziotta-Pareto Index (AMPI) method

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/ci_ampi.R

Description

Adjusted Mazziotta-Pareto Index (AMPI) is a non-compensatory composite index that allows to take into account the time dimension, too. The calculation part is similat to the MPI framework, but the standardization part make the scores obtained over the years comparable.

Usage

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ci_ampi(x, indic_col, gp, time, polarity, penalty = "POS")

Arguments

x

A data.frame containing simple indicators in a Long Data Format.

indic_col

Simple indicators column number.

gp

Goalposts; to facilitate the interpretation of results, the goalposts can be chosen so that 100 represents a reference value (e.g., the average in a given year).

time

The time variable (mandatory); if the analysis is carried out over a single year, it is necessary to create a constant variable (i.e. [email protected] <- 2014).

polarity

Polarity vector: "POS" = positive, "NEG" = negative. The polarity of a individual indicator is the sign of the relationship between the indicator and the phenomenon to be measured (e.g., in a well-being index, "GDP per capita" has 'positive' polarity and "Unemployment rate" has 'negative' polarity).

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).

Details

Author thanks Leonardo Alaimo for their help and for making available the original code of the AMPI function.

Value

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

ci_ampi_est

Composite indicator estimated values.

ci_method

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

Author(s)

Fusco E., Alaimo L.

References

Mazziotta, M., Pareto A. (2013) "A Non-compensatory Composite Index for Measuring Well-being over Time", Cogito. Multidisciplinary Research Journal Vol. V, no. 4, pp. 93-104

See Also

ci_bod, normalise_ci

Examples

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data(EU_2020)

data_test = EU_2020[,c("employ_2010","employ_2011","finalenergy_2010","finalenergy_2011")] 

EU_2020_long<-reshape(data_test, 
                      varying=c("employ_2010","employ_2011","finalenergy_2010","finalenergy_2011"), 
                      direction="long", 
                      idvar="geo", 
                      sep="_")

CI <- ci_ampi(EU_2020_long, 
              indic_col=c(2:3),
              gp=c(50, 100), 
              time=EU_2020_long[,1], 
              polarity= c("POS", "POS"), 
              penalty="POS")
CI$ci_ampi_est

Compind documentation built on March 13, 2019, 1:04 a.m.