ci_factor_mixed: Weighting method based on Factor analysis of mixed data (FAMD)

Description

Factor analysis of mixed data (FAMD) can be seen as a principal component method dedicated to analyze a data set containing both quantitative and qualitative variables making possible to compute composite indicators taking into account continous, dummy, or factor variables

Usage

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

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_mixed".

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).

Author

Luis Carlos Castillo Tellez

Examples

Run this code

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)
i3 <- seq(0, 1, len = 100)
i3 = as.factor(ifelse(i3>0.5,1,0))
Indic = data.frame(i1, i2, i3)

CI  = ci_factor_mixed(Indic,c(1:3))
CI2 = ci_factor_mixed(Indic,c(1:3), method="ALL")
CI3 = ci_factor_mixed(Indic,c(1:3), method="CH", dim=2)