ci_geom_gen {Compind} | R Documentation |
This function use the geometric mean to aggregate the single indicators. Two weighting criteria has been implemented: EQUAL: equal weighting and BOD: Benefit-of-the-Doubt weights following the Puyenbroeck and Rogge (2017) approach.
ci_geom_gen(x,indic_col,meth,up_w,low_w,bench)
x |
A data.frame containing simple indicators. |
indic_col |
A numeric list indicating the positions of the simple indicators. |
meth |
"EQUAL" = Equal weighting set, "BOD" = Benefit-of-the-Doubt weighting set. |
up_w |
if meth="BOD"; upper bound of the weighting set. |
low_w |
if meth="BOD"; lower bound of the weighting set. |
bench |
Row number of the benchmark unit used to normalize the data.frame |
An object of class "CI". This is a list containing the following elements:
If meth = "EQUAL":
ci_mean_geom_est: Composite indicator estimated values.
ci_method: Method used; for this function ci_method="mean_geom".
If meth = "BOD":
ci_geom_bod_est: Constrained composite indicator estimated values.
ci_geom_bod_weights: Raw constrained weights assigned to the simple indicators.
ci_method: Method used; for this function ci_method="geometric_bod".
Rogge N., Vidoli F.
Van Puyenbroeck T. and Rogge N. (2017) "Geometric mean quantity index numbers with Benefit-of-the-Doubt weights", European Journal of Operational Research, Volume 256, Issue 3, Pages 1004 - 1014
i1 <- seq(0.3, 1, len = 100) - rnorm (100, 0.1, 0.03)
i2 <- seq(0.3, 0.5, len = 100) - rnorm (100, 0.1, 0.03)
i3 <- seq(0.3, 0.5, len = 100) - rnorm (100, 0.1, 0.03)
Indic = data.frame(i1, i2,i3)
geom1 = ci_geom_gen(Indic,c(1:3),meth = "EQUAL")
geom1$ci_mean_geom_est
geom1$ci_method
geom2 = ci_geom_gen(Indic,c(1:3),meth = "BOD",0.7,0.3,100)
geom2$ci_geom_bod_est
geom2$ci_geom_bod_weights