DDF: Linear programming model for Directional Distance Function...

In boostingDEA: A Boosting Approach to Data Envelopment Analysis

Linear programming model for Directional Distance Function measure

Description

This function predicts the expected output through a DEA model.

Usage

DDF(
  data,
  x,
  y,
  dataOriginal = data,
  xOriginal = x,
  yOriginal = y,
  FDH = FALSE,
  direction.vector
)

Arguments

data	data.frame or matrix containing the new variables in the model.
x	Vector. Column input indexes in data.
y	Vector. Column output indexes in data.
dataOriginal	data.frame or matrix containing the original variables used to create the model.
xOriginal	Vector. Column input indexes in original data.
yOriginal	Vector. Column output indexes in original data.
FDH	Binary decision variables
direction.vector	Direction vector. Valid values are: dmu (x_0, y_0), unit (unit vector), mean (mean values of each variable) and a user specific vector of the same length as the number of input and output variables

Value

matrix with the the predicted score

boostingDEA documentation built on May 31, 2023, 6:33 p.m.