ci_ampi {Compind}

R Documentation

Adjusted Mazziotta-Pareto Index (AMPI) method

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

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

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. dataframe@year <- 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 "NEG" (default) in case of 'increasing' or 'positive' composite index (e.g., well-being index)), "POS" 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., Giovagnoli C., Patelli 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

Mazziotta, M., Pareto, A. (2016)."On a Generalized Non-compensatory Composite Index for Measuring Socio-economic Phenomena", Cogito. Social Indicators Research, Vol. 127, no. 3, pp. 983-1003

See Also

ci_bod, normalise_ci

Examples

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

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