# ci_rbod: Robust Benefit of the Doubt approach (RBoD) In Compind: Composite Indicators Functions

## Description

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).

## Usage

 `1` ```ci_rbod(x,indic_col,M,B) ```

## Arguments

 `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.

## Value

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.

## References

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.

`ci_bod`

## Examples

 ```1 2 3 4 5 6 7 8``` ```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) ```

### Example output

```Loading required package: Benchmarking