dea {adea} | R Documentation |
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"
)
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 |
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.
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
See Also
Examples
# 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