ci_rbod {Compind} | R Documentation |
Robust Benefit of the Doubt approach (RBoD) is the robust version of the BoD method. It is based on the concept of the expected minimum input function of order-m so "in place of looking for the lower boundary of the support of F, as was typically the case for the full-frontier (DEA or FDH), the order-m efficiency score can be viewed as the expectation of the maximal score, when compared to m units randomly drawn from the population of units presenting a greater level of simple indicators", Daraio and Simar (2005).
ci_rbod(x,indic_col,M,B)
x |
A data.frame containing score of the simple indicators. |
indic_col |
Simple indicators column number. |
M |
The number of elements in each of the bootstrapped samples. |
B |
The number of bootstrap replicates. |
An object of class "CI". This is a list containing the following elements:
ci_rbod_est |
Composite indicator estimated values. |
ci_method |
Method used; for this function ci_method="rbod". |
Vidoli F.
Daraio, C., Simar, L. "Introducing environmental variables in nonparametric frontier models: a probabilistic approach", Journal of productivity analysis, 2005, 24(1), 93 - 121.
Vidoli F., Mazziotta C., "Robust weighted composite indicators by means of frontier methods with an application to European infrastructure endowment", Statistica Applicata, Italian Journal of Applied Statistics, 2013.
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_rbod(Indic,B=10) data(EU_NUTS1) data_norm = normalise_ci(EU_NUTS1,c(2:3),polarity = c("POS","POS"), method=2) CI = ci_rbod(data_norm$ci_norm,c(1:2),M=10,B=20)