geometric_aggregation: Geometric Aggregation

In Indicator: Composite 'Indicator' Construction and Imputation Data

Geometric Aggregation

Description

The purpose of the Geometric Aggregation function is to calculate a synthetic index based on the geometric mean for a set of variables

Usage

geometric_aggregation(
  data,
  weights = rep(1/ncol(data), ncol(data)),
  geo_wo = 0
)

Arguments

data	dataframe with rows = observations and columns = quantitative
weights	vector of weights (default all weights = 1/ ncol(dataframe))
geo_wo	(geometric mean workaround to deal with negative values), positive number to make all variables positive

Details

This is simply the product of each indicator to the power of its weight, all raised the the power of the inverse of the sum of the weights

The geometric mean is less compensatory than the arithmetic mean – low values in one indicator only partially substitute high values in others. For this reason, the geometric mean may sometimes be preferred when indicators represent “essentials”. An example might be quality of life: a longer life expectancy perhaps should not compensate severe restrictions on personal freedoms

Value

It returns a dataframe with rows = observations and column = composite indicator

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=geometric_aggregation(Education)
print(Indicator)

#-----When there are negative values
set.seed(123)
Data=matrix(rnorm(100),nrow = 10,ncol = 10)
Indicator=geometric_aggregation(Data,geo_wo = 100)
print(Indicator)

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