ci_factor {Compind} | R Documentation |
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)
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). |
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". |
Vidoli F.
OECD (2008) "Handbook on constructing composite indicators: methodology and user guide".
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)