Type: Package
Title: Multiplier Data Envelopment Analysis and Cross Efficiency
Version: 0.1.19
Date: 2022-09-01
Depends: lpSolveAPI
Author: Aurobindh Kalathil Puthanpura <kalat2@pdx.edu>
Maintainer: Aurobindh Kalathil Puthanpura <kalat2@pdx.edu>
Description: Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.
License: LGPL-2
Repository: CRAN
Imports: dplyr, ROI, ROI.plugin.glpk, ompr, ompr.roi
NeedsCompilation: no
LazyData: true
Packaged: 2022-09-01 22:00:49 UTC; Auro
Date/Publication: 2022-09-01 22:10:02 UTC

Data: Bank Branch Operating Efficiency data

Description

Bank Branch data for Operating Efficiency.

Usage

Bank_Branch_Operating_Efficiency

Format

A data frame containing data for 17 Bank Branches.

Branch_Code

a character vector

PH

a numeric vector

OE

a numeric vector

SQM

a numeric vector

A

a numeric vector

B

a numeric vector

C

a numeric vector

D

a numeric vector

Source

Giokas DI (1991) Bank branck operating efficiency: A comparative application of DEA and the Loglinear model, OMEGA International Journal of Management Science, 19 (6) 549-557.

References

Giokas DI (1991) Bank branck operating efficiency: A comparative application of DEA and the Loglinear model, OMEGA International Journal of Management Science, 19 (6) 549-557.

Examples

data(Bank_Branch_Operating_Efficiency)
attach(Bank_Branch_Operating_Efficiency)
x <- data.frame(PH, OE, SQM)
rownames(x) <- Branch_Code
colnames(x) <- colnames(Bank_Branch_Operating_Efficiency)[2:4]
y <- data.frame(A, B, C, D)
rownames(y) <- Branch_Code
colnames(y) <- colnames(Bank_Branch_Operating_Efficiency)[5:8]
detach(Bank_Branch_Operating_Efficiency)
# For CRS
result_CRS <- DeaMultiplierModel(x,y,"crs", "input")
# For VRS
result_VRS <- DeaMultiplierModel(x,y,"crs", "input")

Data: Relationship between benchmark tests and Microcomputer price data

Description

The Relationship between benchmark tests and Microcomputer price data.

Usage

BenchMark_Tests_And_Microcomputer

Format

A data frame containing data for 22 Microcomputers.

System

a character vector

Price

a numeric vector

MemorySize

a numeric vector

DiskCapacity

a numeric vector

CPU

a numeric vector

IO

a numeric vector

RL1

a numeric vector

RL2

a numeric vector

RL3

a numeric vector

Source

Sircar S. and Dave D (1986) Tbe relationship between benchmark tests and microcomputer price. Communications of the ACM, 29, 212-217.

References

Sircar S. and Dave D (1986) Tbe relationship between benchmark tests and microcomputer price. Communications of the ACM, 29, 212-217.

Examples

data(BenchMark_Tests_And_Microcomputer)
attach(BenchMark_Tests_And_Microcomputer)

x <- BenchMark_Tests_And_Microcomputer

detach(BenchMark_Tests_And_Microcomputer)

Cross Efficiency Model

Description

Cross Efficiency uses DEA to do peer evaluation of DMUs. Single-phase cross efficiency approach.

Usage

CrossEfficiency(x = x, y = y, rts = "crs", orientation = "input", weightRestriction)

Arguments

x

Inputs or resources used by each decision making unit.

y

Outputs or resources used by each decision making unit.

rts

Returns to scale for the application, or industry studied. Note the default rts is crs. vrs Variable returns to scale. crs Constant returns to scale.

orientation

Orientation of the DEA model - primary emphasis on input-reduction input or output-augmentation output. Note that unlike the DEA functions, the default is input orientation.

weightRestriction

Weight restriction for the model. Optional parameter.

Value

The function returns a number of values per DMU.

$ceva_matrix

Returns the cross efficiency matrix. Row is the Rating DMU and Column is the Rated DMU.

$ce_ave

Returns the cross efficiency score for the DMU.

$ceva_max

Returns the maximum cross efficiency score for the DMU.

$ceva_min

Returns the minimum cross efficiency score for the DMU.

$vx

Input weights from the model.

$uy

Output weights from the model.

$Model_Status

Returns the status of the LP model.

Note

ceva_matrix - cross-evaluation matrix. ceva_max - cross-evaluation maximum. ceva_min - cross-evaluation minimum. ce_ave - cross-efficiency scores.

Examples

#Example from Kenneth R. Baker: Optimization Modeling with Spreadsheets, Third Edition,p. 176,
#John Wiley and Sons, Inc.

dmu <- c("A", "B", "C", "D", "E", "F")
x <- data.frame(c(150,400,320,520,350,320),c(0.2,0.7,1.2,2.0,1.2,0.7))
rownames(x) <- dmu
colnames(x)[1] <- c("StartHours")
colnames(x)[2] <- c("Supplies")

y <- data.frame(c(14,14,42,28,19,14),c(3.5,21,10.5,42,25,15))
rownames(y) <- dmu
colnames(y)[1] <- c("Reimbursed")
colnames(y)[2] <- c("Private")

# Calculate the efficiency score
result <- CrossEfficiency(x,y,"crs", "input")
# Examine the cross efficiency score for DMUs
print(result$ce_ave)

Data: City data

Description

City data for Operating Efficiency.

Usage

data("Data_City")

Format

A data frame containing data for 15 city observations

DMU

a numeric vector

City

a character vector

Houseprice

a numeric vector

Rental

a numeric vector

Violent

a numeric vector

Income

a numeric vector

B.Degree

a numeric vector

Doctor

a numeric vector

Source

W.D. Cook, L. Liang, Y. Zha and J.Zhu (2009) A Modified Super-Efficiency DEA Model for Infeasibility, The Journal of the Operational Research Society Vol. 60, No. 2 (Feb., 2009), pp. 276-281.

References

W.D. Cook, L. Liang, Y. Zha and J.Zhu (2009) A Modified Super-Efficiency DEA Model for Infeasibility, The Journal of the Operational Research Society Vol. 60, No. 2 (Feb., 2009), pp. 276-281.

Examples

data(Data_City)
attach(Data_City)

DEA Multiplier Model

Description

DEA multiplier model calculates the efficieny and reference sets for each DMUs.

Usage

DeaMultiplierModel(x = x, y = y, rts = "crs", orientation = "input", weightRestriction)

Arguments

x

Inputs or resources used by each decision making unit.

y

Outputs or resources used by each decision making unit.

rts

Returns to scale for the application, or industry studied. Note the default rts is crs. vrs Variable returns to scale. crs Constant returns to scale. Available option: crs, vrs

orientation

Orientation of the DEA model - primary emphasis on input-reduction or output-augmentation output. Note that unlike the DEA functions, the default is input orientation. Available option: input, output.

weightRestriction

Weight restriction for the model. Optional parameter.

Value

The function returns a number of values per DMU. The standardized efficiency (all inefficiencies are between 0 and 1, for input and output orientation). Efficiency, and lambda values are returned.

$rts

Returns to scale of the model.

$Orientation

Orientation of the model.

$InputValues

Input Values (x) passed to the model.

$OutputValues

Output Values (y) passed to the model.

$Efficiency

Efficiency of each DMU in the model.

$Lambda

Lambdas per DMU in the model.

$HCU_Input

HCU data for inputs.

$HCU_Output

HCU data for outputs.

$vx

Input weights from the model.

$uy

Output weights from the model.

$Free_Weights

Free weights from the model. Applies only to vrs returns-to-scale.

$Model_Status

Returns the status of the LP model.

Examples

#Example from Kenneth R. Baker: Optimization Modeling with Spreadsheets, Third Edition,p. 176,
#John Wiley and Sons, Inc.

dmu <- c("A", "B", "C", "D", "E", "F")

x <- data.frame(c(150,400,320,520,350,320),c(0.2,0.7,1.2,2.0,1.2,0.7))
rownames(x) <- dmu
colnames(x)[1] <- c("StartHours")
colnames(x)[2] <- c("Supplies")

y <- data.frame(c(14,14,42,28,19,14),c(3.5,21,10.5,42,25,15))
rownames(y) <- dmu
colnames(y)[1] <- c("Reimbursed")
colnames(y)[2] <- c("Private")

#Creating the weight restriction data frame with Upper bound

weightRestriction<-data.frame(lower = c(1), numerator = c("StartHours"),
denominator = c("Supplies"), upper = c(2))

#Creating the weight restriction data frame without Upper bound
weightRestriction<-data.frame(lower = c(1), numerator = c("StartHours"),
denominator = c("Supplies"))

#Creating the weight restriction data frame with Upper bound and Na, Inf or NaN
weightRestriction<-data.frame(lower = c(1,2), numerator = c("StartHours","Reimbursed"),
denominator = c("Supplies","Private"), upper = c(2,Inf))

# Calculate the efficiency score without weight Restriction
result <- DeaMultiplierModel(x,y,"crs", "input")
# Examine the efficiency score for DMUs
print(result$Efficiency)


# Calculate the efficiency score with weight Restriction
result <- DeaMultiplierModel(x,y,"crs", "input", weightRestriction)
  # Examine the efficiency score for DMUs
print(result$Efficiency)

Data: UK University Departments Of Accounting Efficiency data.

Description

Evaluation the Efficiency of UK University Departments Of Accounting Efficiency.

Usage

Departments_Of_Accounting

Format

A data frame containing data for 20 UK University Departments Of Accounting.

Departments

a numeric vector

Undergraduates

a numeric vector

Research

a numeric vector

Taught

a numeric vector

Res.Co

a numeric vector

OtherRes

a numeric vector

OtherIncome

a numeric vector

Publications

a numeric vector

AcademicStaff

a numeric vector

Salaries

a numeric vector

OtherExp

a numeric vector

Source

Tomkins C and Green RH (1988) An experiment in the use of data envelopment analysis for evaluating the efficiency of UK university departments of accounting. Financial Accounting and Management, 4, 147-164.

References

Tomkins C and Green RH (1988) An experiment in the use of data envelopment analysis for evaluating the efficiency of UK university departments of accounting. Financial Accounting and Management, 4, 147-164.

Examples

data(Departments_Of_Accounting)
attach(Departments_Of_Accounting)

x <- data.frame(AcademicStaff)
rownames(x) <- Departments
colnames(x) <- colnames(Departments_Of_Accounting)[9]

y <- data.frame(Undergraduates, Research, Taught,(Res.Co + OtherRes + OtherIncome))
rownames(y) <- Departments
colnames(y)[1] <- colnames(Departments_Of_Accounting)[2]
colnames(y)[2] <- colnames(Departments_Of_Accounting)[3]
colnames(y)[3] <- colnames(Departments_Of_Accounting)[4]
colnames(y)[4] <- c("Total_Income")

detach(Departments_Of_Accounting)

result <- DeaMultiplierModel(x,y,"crs", "input")

Data: Educational program data

Description

Evaluation of Educational program.

Usage

Evaluation_Educational_Program

Format

A data frame containing data for 22 educational programs.

Program

a numeric vector

CCR_EFF

a numeric vector

Revenue_Generated

a numeric vector

Student_Employed

a numeric vector

Employer_Satisfaction

a numeric vector

Contact_Hours

a numeric vector

Number_of_FTE_Staff

a numeric vector

Facility_Allocation

a numeric vector

Expenditures

a numeric vector

Source

Bessent A, Bessent W, Cbames A, Cooper WW and Thorgood N (1983) Evaluation of educational program proposals by means of data envelopment analysis. Educational Administrative Quarterly, 19, 82-107.

References

Bessent A, Bessent W, Cbames A, Cooper WW and Thorgood N (1983) Evaluation of educational program proposals by means of data envelopment analysis. Educational Administrative Quarterly, 19, 82-107.

Examples

data(Evaluation_Educational_Program)
attach(Evaluation_Educational_Program)


x <- data.frame(Contact_Hours, Number_of_FTE_Staff, Facility_Allocation, Expenditures)
rownames(x) <- Program
colnames(x) <- colnames(Evaluation_Educational_Program)[6:9]


y <- data.frame(Revenue_Generated, Student_Employed, Employer_Satisfaction)
rownames(y) <- Program
colnames(y) <- colnames(Evaluation_Educational_Program)[3:5]

detach(Evaluation_Educational_Program)

result <- DeaMultiplierModel(x,y,"crs", "input")

Data: Evaluation of Non-Profit organizations data

Description

Evaluation of Non-Profit organizations efficiency.

Usage

Evaluations_Of_NonProfitOrganizations

Format

A data frame containing data for 16 Non-Profit organizations.

Hospital

a numeric vector

H0

a numeric vector

PercentOccupancy

a numeric vector

RevenuePerDay

a numeric vector

A/RTurnover

a numeric vector

CostPerDay

a numeric vector

LengthOfStay

a numeric vector

Source

Greenberg R and Nunamaker T (1987) A generalized multiple criteria model for control and evaluation of nonprofit organizations. Financial Accountability and Management, 3 (4), 331-342.

References

Greenberg R and Nunamaker T (1987) A generalized multiple criteria model for control and evaluation of nonprofit organizations. Financial Accountability and Management, 3 (4), 331-342.

Examples

data(Evaluations_Of_NonProfitOrganizations)
attach(Evaluations_Of_NonProfitOrganizations)

x <- Evaluations_Of_NonProfitOrganizations

detach(Evaluations_Of_NonProfitOrganizations)

Data: Japanese Companies data.

Description

Japanese companies data for Operating Efficiency.

Usage

data("Japanese_Companies")

Format

A data frame with 0 observations on the following 2 variables.

DMU

a numeric vector

Company

a character vector

Asset

a numeric vector

Equity

a numeric vector

Employee

a numeric vector

Revenue

a numeric vector

Source

W.D. Cook, L. Liang, Y. Zha and J.Zhu (2009) A Modified Super-Efficiency DEA Model for Infeasibility, The Journal of the Operational Research Society Vol. 60, No. 2 (Feb., 2009), pp. 276-281.

References

W.D. Cook, L. Liang, Y. Zha and J.Zhu (2009) A Modified Super-Efficiency DEA Model for Infeasibility, The Journal of the Operational Research Society Vol. 60, No. 2 (Feb., 2009), pp. 276-281.

Examples

data(Japanese_Companies)
attach(Japanese_Companies)

Malmquist Productivity Index.

Description

MPI model to calculate MPI, Technical change, Efficiency change and Scale efficiency change.

Usage

MPI(Dataset = Dataset, DMU_colName = DMU_colName, IP_colNames = IP_colNames,
OP_ColNames = OP_ColNames, Period_ColName = Period_ColName, Periods = Periods,
rts = "crs", orientation = "input", scale = FALSE)

Arguments

Dataset

The data required for the model.

DMU_colName

Column name for the DMUs in the dataset.

IP_colNames

Column name(s) for all input data in the dataset.

OP_ColNames

Column name(s) for all output data in the dataset.

Period_ColName

Column name for the period number in the dataset.

Periods

Unique periods numbers in the dataset in ascending order.

rts

Returns to scale for the application, or industry studied. Note the default rts is crs. vrs Variable returns to scale. crs Constant returns to scale.

orientation

Orientation of the DEA model - primary emphasis on input-reduction input or output-augmentation output. Note the default is input orientation.

scale

Note default value is FALSE.

Value

DMU

DMUs

et1t1.crs

The efficiencies for period 1 with reference technology from period 1 for crs returns to scale. Note: available if returns to scale is crs or scale is TRUE.

et2t2.crs

The efficiencies for period 2 with reference technology from period 2 for crs returns to scale. Note: available if returns to scale is crs or scale is TRUE.

et1t2.crs

The efficiencies for period 2 with reference technology from period 1 for crs returns to scale. Note: available if returns to scale is crs or scale is TRUE.

et2t1.crs

The efficiencies for period 1 with reference technology from period 2 for crs returns to scale. Note: available if returns to scale is crs or scale is TRUE.

et1t1.vrs

The efficiencies for period 1 with reference technology from period 1 for vrs returns to scale. Note: available if returns to scale is vrs.

et2t2.vrs

The efficiencies for period 2 with reference technology from period 2 for vrs returns to scale. Note: available if returns to scale is vrs.

et1t2.vrs

The efficiencies for period 2 with reference technology from period 1 for vrs returns to scale. Note: available if returns to scale is vrs.

et2t1.vrs

The efficiencies for period 1 with reference technology from period 2 for vrs returns to scale. Note: available if returns to scale is vrs

sec1

First componenet of the scale efficiency change. (et1t2.crs/et1t2.vrs)/(et1t1.crs/et1t1.vrs)

sec2

Second componenet of the scale efficiency change.(et2t2.crs/et2t2.vrs)/(et2t1.crs/et2t1.vrs)

sec

Scale efficiency change. (sec1 * sec2) ^ 0.5

tc1

First component of technical change. For crs, (et1t2.crs/et2t2.crs) and (et1t2.vrs/et2t2.vrs) for vrs.

tc2

Second component of technical change. For crs, (et1t1.crs/et2t1.crs) and (et1t1.vrs/et2t1.vrs) for vrs.

tc

Technical change. (tc1 * tc2) ^ 0.5

tec or ptec

Efficiency change. Note: tec for crs and ptec for vrs returns to scale.

m.crs or m.vrs

Malmquist Productivity index for the DMUs and periods.

Year

Time period underconsideration for MPI.

References

Rolf, Fare; Grosskopf, Shawna; Norris, Mary and Zhang, Zhongyang (1994) Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries, The American Economic Review Vol. 84, No. 1, pp. 66-83.

Examples


da_f <- data.frame(x= c(11, 29, 31, 61, 13, 27, 17, 61), y=c(6, 8, 11, 16, 7, 9, 10, 16),
d= c(1,2,3,4, 1,2,3,4), p=c(1,1,1,1,2,2,2,2))

mpi_r <- MPI(Dataset = da_f, DMU_colName = "d", IP_colNames = "x", OP_ColNames = "y",
Period_ColName = "p", Periods = c(1,2),rts = "vrs", orientation = "input", scale = TRUE)

# Examine the MPI for DMUs
mpi_r$m.vrs

Benevolent and Malevolent Model

Description

Two-Phase Cross efficiency approach.

Usage

Mal_Ben(x = x, y = y, rts ="crs", orientation = "input", phase = "mal",
weightRestriction, include = TRUE)

Arguments

x

Inputs or resources used by each decision making unit.

y

Outputs or resources used by each decision making unit.

rts

Returns to scale for the application, or industry studied. Note the default rts is crs. vrs Variable returns to scale. crs Constant returns to scale. Available option: crs, vrs.

orientation

Orientation of the DEA model - primary emphasis on input-reduction input or output-augmentation output. Note that unlike the DEA functions, the default is input orientation. Available option: input, output.

weightRestriction

Weight restriction for the model. Optional parameter.

phase

Second phase of the model. Malevolent or Benevolent. Note the default is mal.Available option: mal, ben.

include

In the second phase include evaluating DMU in the calculation. Default is TRUE. Available option: TRUE, FALSE.

Value

The function returns a number of values per DMU. The standardized efficiency (all inefficiencies are between 0 and 1, for input and output orientation) Efficiency, and the lambda values, lambda, are returned.

$rts

Returns to scale of the model.

$Orientation

Orientation of the model.

$InputValues

Input Values (x) passed to the model.

$OutputValues

Output Values (y) passed to the model.

$Phase1_Efficiency

Efficiency of each DMU in the model from Phase 1.

$Phase1_Lambda

Lambdas per DMU in the model from Phase 1.

$Phase1_vx

Input weights from the model from Phase 1.

$Phase1_uy

Output weights from the model from Phase 1.

$Phase1_Free_Weights

Free weights from the model from Phase 1. Applies only to vrs returns-to-scale.

$Phase1_Model_Status

Returns the status of the phase two LP model.

$Phase2_Efficiency

Efficiency of each DMU in the model from Phase 2.

$Phase2_Lambda

Lambdas per DMU in the model from Phase 2.

$Phase2_vx

Input weights from the model from Phase 2.

$Phase2_uy

Output weights from the model from Phase 2.

$Phase2_Free_weights

Free weights from the model from Phase 2. Applies only to vrs returns-to-scale.

$Phase2_Model_Status

Returns the status of the phase two LP model.

$ceva_matrix

Returns the cross efficiency matrix. Row is the Rating DMU and Column is the Rated DMU.

$ce_ave

Returns the cross efficiency score for the DMU.

$ceva_max

Returns the maximum cross efficiency score for the DMU.

$ceva_min

Returns the minimum cross efficiency score for the DMU.

Note

ceva_matrix - cross-evaluation matrix. ceva_max - cross-evaluation maximum. ceva_min - cross-evaluation minimum. ce_ave - cross-efficiency scores.

Examples

#Example from Kenneth R. Baker: Optimization Modeling with Spreadsheets, Third Edition,p. 176,
#John Wiley and Sons, Inc.

dmu <- c("A", "B", "C", "D", "E", "F")
x <- data.frame(c(150,400,320,520,350,320),c(0.2,0.7,1.2,2.0,1.2,0.7))
rownames(x) <- dmu
colnames(x)[1] <- c("StartHours")
colnames(x)[2] <- c("Supplies")
y <- data.frame(c(14,14,42,28,19,14),c(3.5,21,10.5,42,25,15))
rownames(y) <- dmu
colnames(y)[1] <- c("Reimbursed")
colnames(y)[2] <- c("Private")

# Calculate the efficiency score
result <- Mal_Ben(x, y, rts = "crs",  orientation = "input",phase = "mal", include = TRUE)
# Examine the cross efficiency score for DMUs
print(result$ce_ave)

Data: Metropolitan and London rates departments data

Description

Relative Efficiency Metropolitan and London rates departments.

Usage

Metropolitan_And_London_Rates_Departments

Format

A data frame containing data for 62 rates department authority.

Authority

a character vector

TotalCost

a numeric vector

Non-cnl

a numeric vector

Rate

a numeric vector

Summons

a numeric vector

NPV

a numeric vector

Source

Dyson RG and Thanassoulis E (1988) Reducing weight flexibility in Data Envelopment Analysis, Journal of the Operational Research Society, 39 (6), 563-576.

References

Dyson RG and Thanassoulis E (1988) Reducing weight flexibility in Data Envelopment Analysis, Journal of the Operational Research Society, 39 (6), 563-576.

Examples

data(Metropolitan_And_London_Rates_Departments)
attach(Metropolitan_And_London_Rates_Departments)

x <- data.frame(TotalCost)
rownames(x) <- Authority
colnames(x) <- colnames(Metropolitan_And_London_Rates_Departments)[2]


y <- data.frame(`Non-cnl`, Rate, Summons, NPV)
rownames(y) <- Authority
colnames(y) <- colnames(Metropolitan_And_London_Rates_Departments)[3:6]

detach(Metropolitan_And_London_Rates_Departments)

result <- DeaMultiplierModel(x,y,"crs", "input")

Super-Efficiency DEA

Description

SDEA model to calculate the efficieny for each DMUs.

Usage

SDEA(x=x, y=y, orientation = "input", rts = "crs", Cook = FALSE)

Arguments

x

Inputs or resources used by each decision making unit.

y

Outputs or resources used by each decision making unit.

orientation

Orientation of the DEA model - primary emphasis on input-reduction input or output-augmentation output. Note the default is input orientation.

rts

Returns to scale for the application, or industry studied. Note the default rts is crs. vrs Variable returns to scale. crs Constant returns to scale.

Cook

Used with variable returns to scale to address infeasibility in efficiency. Note the default is FALSE. Use TRUE when using vrs rts.

Value

Input

Input Values (x) passed to the model.

Output

Output Values (y) passed to the model.

Orientation

Orientation of the model.

RTS

Returns to scale of the model.

Efficiency

Efficiency of each DMU in the model.

Theta

Used to calculate efficiency if the model is infeasiable. Note: Available only when Cook is set to TRUE.

Beta

Used to calculate efficiency if the model is infeasiable.Note: Available only when Cook is set to TRUE.

Lambda

Lambdas per DMU in the model.

StatusData

Returns the status of the LP model.

References

W.D. Cook, L. Liang, Y. Zha and J.Zhu (2009) A Modified Super-Efficiency DEA Model for Infeasibility, The Journal of the Operational Research Society Vol. 60, No. 2 (Feb., 2009), pp. 276-281.

Examples

x <-data.frame(matrix(c(12, 26, 16, 60 ),ncol=1))
rownames(x) <- c('a','b','c','d')
y <- data.frame(matrix(c(6, 8, 9, 15 ),ncol=1))
rownames(y) <- c('a','b','c','d')


result <- SDEA(x=x, y=y, orientation = "input", rts = "crs", Cook = FALSE)
# Examine the efficiency score for DMUs
print(result$Efficiency)

Provides the solver status codes.

Description

Provides the solver status codes and description.

Examples

#List status codes and description.

dict.solveStatus

Provides the orientation option.

Description

Provides the orientation option values.

Examples

# List the orientation option used as arguments.

options.orientation.l

Provides the second phase options.

Description

Provides the second phase options available for Mal_Ben function.

Examples

# List the phase option used as arguments.

options.phase.l

Provides the rts (returns to scale) option.

Description

Provides the rts (returns to scale) option values.

Examples

# List the returns to scale option used as arguments.

options.rts.l

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