pca_weighting: Function that weight the quantitative variable by PCA method

In Indicator: Composite 'Indicator' Construction and Imputation Data

Function that weight the quantitative variable by PCA method

Description

The pca_weighting function is designed to perform a principal component analysis (PCA) on the input data to calculate weights that correct for overlapping information between related indicators. This process makes it possible to create a composite indicator that captures as much information as possible from individual indicators while reducing the dimensionality of the data

Usage

pca_weighting(data)

Arguments

data	dataframe with rows = observations and columns = quantitative variables

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_pca=pca_weighting(Education)
print(Indicator_pca)

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