R: Linear Programming for the Data Envelopment Analysis
effdea.b.f
R Documentation
Linear Programming for the Data Envelopment Analysis
Description
Solve input(output)-oriented DEA under the CRS (VRS) with
convexhull. Do not use when the total number of inputs and outputs are
greater than eight. If used, it may take more than hundreds day to get
results.
A data set for DMUs to be evaluated. A data frame with
J1*(M+N) dimention, where J1 is the number of DMUs, M for the number
of inputs, and N for the number of outputs.
frontier
A data set for DMUs to be used in constructing a
production possibility set (PPS). A data frame with J2*(M+N)
dimention, where J2 is the number of DMUs, M for the number of
inputs, and N for the number of outputs.
noutput
The number of outputs (N).
orientation
Orientation of measurement. 1 for the input-oriented
measure, and 2 for the output-oriented measure.
rts
Returns to scale. 1 for the CRS assumption, and 2 for the
VRS assumption.
convhull
Logical. If this is TRUE, very efficient calculation of
efficiency score is used. However, when the total number of inputs and
outputs is larger than eight, it is very slow for this option. In cases
when the total number of inputs and outputs is larger than eight, use
FALSE for this argument.
Details
This function uses the convhull function in geometry package. After
finding convex hull of frontier by using the convhull
function. points on the convex hull are used in constructing
the second production possibility set (PPS). Then efficiency scores in
base are calculated based on the second PPS.
Value
A data frame with J1*1 dimension, which shows efficiency scores.
Cooper, W., Seiford, L. and Tone, K. (2007). Data envelopment
analysis: a comprehensive text with models, applications, references
and DEA-solver software (2nd ed.). Springer Verlag, New York.
Lee, J. and Oh, D. (forthcoming). Efficiency Analysis: Data
Envelopment Analysis. Press (in Korean)
See Also
dual.dea
Examples
## input-oriented DEA under the CRS assumption (1 input and 1 output)
tab3.1.dat <- data.frame(y = c(1, 2, 4, 6, 7, 9, 9),
x = c(3, 2, 6, 4, 8, 8, 10))
(re <- effdea.b.f(base = tab3.1.dat, noutput = 1, orientation = 1, rts =
1, convhull = TRUE))