ci_factor {Compind}

Weighting method based on Factor Analysis

Description

Factor analysis groups together collinear simple indicators to estimate a composite indicator that captures as much as possible of the information common to individual indicators.

Usage

ci_factor(x,indic_col,method="ONE",dim)

Arguments

x

A data.frame containing score of the simple indicators.

indic_col

Simple indicators column number.

method

If method = "ONE" (default) the composite indicator estimated values are equal to first component scores; if method = "ALL" the composite indicator estimated values are equal to component score multiplied by its proportion variance; if method = "CH" it can be choose the number of the component to take into account.

dim

Number of chosen component (if method = "CH", default is 3).

Value

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

ci_factor_est

Composite indicator estimated values.

loadings_fact

Variance explained by principal factors (in percentage terms).

ci_method

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

Author(s)

Vidoli F.

References

OECD (2008) "Handbook on constructing composite indicators: methodology and user guide".

See Also

ci_bod, ci_mpi

Examples

i1 <- seq(0.3, 0.5, len = 100) - rnorm (100, 0.2, 0.03)
i2 <- seq(0.3, 1, len = 100)   - rnorm (100, 0.2, 0.03)
Indic = data.frame(i1, i2)
CI = ci_factor(Indic)

data(EU_NUTS1)
CI = ci_factor(EU_NUTS1,c(2:3), method="ALL")

data(EU_2020)
data_norm = normalise_ci(EU_2020,c(47:51),polarity = c("POS","POS","POS","POS","POS"), method=2)
CI3 = ci_factor(data_norm$ci_norm,c(1:5),method="CH", dim=3)