normalization_abov_below_mean: Normalization above or below the mean

In Indicator: Composite 'Indicator' Construction and Imputation Data

Normalization above or below the mean

Description

This transformation considers the indicators which are above and below an arbitrarily defined threshold, p, around the mean. The threshold p builds a neutral region around the mean, where the transformed indicator is zero. This reduces the sharp discontinuity, from -1 to +1, which exists across the mean value to two minor discontinuities, from -1 to 0 and from 0 to +1, across the thresholds

Usage

normalization_abov_below_mean(data, p = 0.01)

Arguments

data	dataframe with rows = observations and columns = quantiative variables
p	threshold for the window

Details

This function to normalize in -1, 0, and 1

Value

It returns a datafame of normalized data

References

OECD/European Union/EC-JRC (2008), Handbook on Constructing Composite Indicators: Methodology and User Guide, OECD Publishing, Paris, <https://doi.org/10.1787/9789264043466-en>

Examples

data("Education")
Indicator=normalization_abov_below_mean(Education)
print(Indicator)

#---With different threshold
Indicator=normalization_abov_below_mean(Education,p=0.1)
print(Indicator)

Indicator documentation built on April 4, 2025, 4:35 a.m.