ci_factor function

Weighting method based on Factor Analysis

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.

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).

Returns

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".

References

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

Author(s)

Vidoli F.

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)