WAM {boostingDEA}

Linear programming model for Weighted Additive Model

Description

This function predicts the expected output through a DEA model.

Usage

WAM(
  data,
  x,
  y,
  dataOriginal = data,
  xOriginal = x,
  yOriginal = y,
  FDH = FALSE,
  weights
)

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

weights

Weights. Valid values are: MIP (Measure of Inefficiency Proportions), RAM (Range Adjusted Measure), BAM (Bounded Adjusted Measure), normalized (normalized weighted additive model) and a user specific vector of the same length as the number of input and output variables

Value

matrix with the the predicted score