ci_bod_constr function

Constrained Benefit of the Doubt approach (BoD)

The constrained Benefit of the Doubt function lets to introduce additional constraints to the weight variation in the optimization procedure so that all the weights obtained are greater than a lower value (low_w) and less than an upper value (up_w).

ci_bod_constr(x,indic_col,up_w,low_w)

Arguments

• x: A data.frame containing simple indicators.
• indic_col: A numeric list indicating the positions of the simple indicators.
• up_w: Importance weights upper bound.
• low_w: Importance weights lower bound.

Returns

An object of class "CI". This is a list containing the following elements: - ci_bod_constr_est: Constrained composite indicator estimated values.

• ci_method: Method used; for this function ci_method="bod_constrained".

• ci_bod_constr_weights: Raw constrained weights assigned to the simple indicators.

References

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.

Author(s)

Rogge N., Vidoli F.

See Also

ci_bod_dir,ci_bod

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_bod_constr(Indic,up_w=1,low_w=0.05)

data(EU_NUTS1)
data_norm = normalise_ci(EU_NUTS1,c(2:3),polarity = c("POS","POS"), method=2)
CI = ci_bod_constr(data_norm$ci_norm,c(1:2),up_w=1,low_w=0.05)