dea: DEA - Data Envelopment Analysis

Description

Data Envelopment Analysis, DEA, computes, for a set of Decision Making Units, DMU, a relative efficiency score, comparing one unit with the others.

Usage

dea(
  input,
  output,
  orientation = c("input", "output"),
  name = "",
  solver = "auto"
)

Value

This function return a dea class object with the following named members:

name: A label of the model
orientation: DEA model orientation 'input' or 'output'
inputnames: Variable input names
outputnames: Variable output names
eff: is a vector with DMU's scores
ux: A set of weights for inputs
vy: A set of weights for output
vinput: Standardized virtual input dividing by the sum of the weights, see [Costa2006] in adea-package.
voutput: Standardized virtual output dividing by the sum of the weights, see [Costa2006] in adea-package
solver: The solver used for the resolution of the optimization problem

Arguments

input: A matrix or a data frame containing the inputs of the units to be evaluated, with one row for each DMU and one column for each input.
output: A matrix or a data frame containing the outputs of the units to be evaluated, with one row for each DMU and one column for each output.
orientation: Use "input" for input orientation or "output" for output orientation in DEA model. The default is "input".
name: An optional descriptive name for the model. The default is an empty string. This name will be displayed in printed and summarized results.
solver: The solver used by ROI to solve the DEA optimization problem. The default is "auto." The solver must be installed and capable of solving linear programming problems. Use ROI_installed_solvers() to list them.

Details

Each DMU transforms inputs into outputs. The set of inputs and outputs is the same for all the DMUs, but not their quantities.

This function computes a relative efficiency score and weights for each input and output variable in the model. All these for each DMU.

Examples

Run this code

# Load data
data('cardealers4')

# Define input and output
input <- cardealers4[, c('Employees', 'Depreciation')]
output <- cardealers4[, c('CarsSold', 'WorkOrders')]

# Compute dea model
model <- dea(input, output, name = 'DEA for cardealers4 dataset')

# Print DMU efficiencies
model
# Dealer A  Dealer B  Dealer C  Dealer D  Dealer E  Dealer F
# 0.9915929 1.0000000 0.8928571 0.8653846 1.0000000 0.6515044

# Summarize the model and print aditional information
summary(model)
# Model name   DEA for cardealers4 dataset
# Orientation                        input
# Inputs            Employees Depreciation
# Outputs              CarsSold WorkOrders
# nInputs                                2
# nOutputs                               2
# nVariables                             4
# nEfficients                            2
# Eff. Mean               0.90022318389575
# Eff. sd                0.135194867030839
# Eff. Min.              0.651504424778761
# Eff. 1st Qu.           0.872252747252747
# Eff. Median            0.942225031605562
# Eff. 3rd Qu.           0.997898230088496
# Eff. Max.                              1