https://github.com/javierbarbero/DataEnvelopmentAnalysis.jl
Tip revision: 48c782a09f14b708153f8f8228b1d9e452637f5b authored by Javier Barbero on 29 October 2023, 09:20:13 UTC
Add compat requirements for Julia standard libraries
Add compat requirements for Julia standard libraries
Tip revision: 48c782a
radialboot.md
```@meta
CurrentModule = DataEnvelopmentAnalysis
```
# Bootstrap Radial Model
The bootstrap radial DEA model (Simar and Wilson, 1998) can be calculated with the `deaboot` function, indicating the number of bootstrap replications in the `nreps` parameter. The other parameters work the same as in the radial DEA model.
A random number generator can be specified in the `rng` parameter for reproducibility.
```@example radialboot
using DataEnvelopmentAnalysis
using StableRNGs
X = [2, 4, 3, 5, 6]
Y = [1, 2, 3, 4, 5]
ioboot = deaboot(X, Y, orient = :Input, rts = :VRS, nreps = 200, rng = StableRNG(1234567))
```
!!! warning "Number of bootstrap replications"
The example above uses 200 bootstrap replications for illustrative purposes. In practice, at least 1000 replications are recommended.
Bias-corrected efficiency scores are returned with the `efficiency` function:
```@example radialboot
efficiency(ioboot)
```
The bias, calculated as the difference between the reference efficiency score and the bias-corrected efficiency score, is returned with the `bias` function:
```@example radialboot
bias(ioboot)
```
Confidence intervals at the $95\%$, or any other desired level, are calculated with the `confint` function:
```@example radialboot
confint(ioboot, level = 0.95)
```
The optimal bandwidth computed for the model is returned with the `bandwidth` function:
```@example radialboot
bandwidth(ioboot)
```
### deaboot Function Documentation
```@docs
deaboot
bias(::BootstrapRadialDEAModel)
confint(::BootstrapRadialDEAModel)
bandwidth(::BootstrapRadialDEAModel)
```