eff: Calculate efficiencies for Farrell and sfa object

In Benchmarking: Benchmark and Frontier Analysis Using DEA and SFA

Calculate efficiencies for Farrell and sfa object

Description

Calculate efficiencies for Farrell and sfa object. For a sfa there are several types

Usage

eff( object, ... )
efficiencies( object, ... )
## Default S3 method:
efficiencies( object, ... )
## S3 method for class 'Farrell'
efficiencies(object, type = "Farrell", ...)
## S3 method for class 'Farrell'
eff(object, type = "Farrell", ...)
## S3 method for class 'sfa'
efficiencies(object, type = "BC", ...)
## S3 method for class 'sfa'
eff(object, type = "BC", ...)

Arguments

object	A Farrell object returned from a DEA function like dea, sdea, or mea or an sfa object returned from the function sfa.
type	The type of efficiencies to be calculated. For a Farrell object the possibilities are “Farrell” efficiency or “Shephard” efficiency. For a sfa object the possibilities are “BC”, “Mode”, “J”, or “add”.
...	Further arguments ...

Details

The possible types for class Farrell (an object returned from dea et al. are “Farrell” and “Shephard”.

The possible types for class sfa efficiencies are

BC

Efficiencies estimated by minimizing the mean square error; Eq. (7.21) in Bogetoft and Otto (2011, 219) and Battese and Coelli (1988, 392)

Mode

Efficiencies estimates using the conditional mode approach; Bogetoft and Otto (2011, 219), Jondrow et al. (1982, 235).

J

Efficiencies estimates using the conditional mean approach Jondrow et al. (1982, 235).

add

Efficiency in the additive model, Bogetoft and Otto (2011, 219)

Value

The efficiencies are returned as an array.

Note

For the Farrell object the orientation is determined by the calculations that led to the object and cannot be changed here.

Author(s)

Peter Bogetoft and Lars Otto [e-mail]

References

Bogetoft and Otto; Benchmarking with DEA, SFA, and R, Springer 2011

See Also

dea and sfa.

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

Benchmarking documentation built on April 4, 2025, 5:07 a.m.