It solves directional, basic DEA models under constant, variable, non-increasing, non-decreasing or generalized returns to scale. By default, models are solved in a two-stage process (slacks are maximized).
model_dir(datadea,
dmu_eval = NULL,
dmu_ref = NULL,
dir_input = NULL,
dir_output = NULL,
d_input = 1,
d_output = 1,
rts = c("crs", "vrs", "nirs", "ndrs", "grs"),
L = 1,
U = 1,
maxslack = TRUE,
weight_slack_i = 1,
weight_slack_o = 1,
returnlp = FALSE,
...)A list of class dea with the results for the evaluated DMUs (DMU component,
we note that we call "targets" to the "efficient projections"
in the strongly efficient frontier),
along with any other necessary information to replicate the results, such as
the name of the model and parameters orientation_param, rts,
dmu_eval and dmu_ref.
A deadata object with n DMUs, m inputs and s
outputs.
A numeric vector containing which DMUs have to be evaluated.
If NULL (default), all DMUs are considered.
A numeric vector containing which DMUs are the evaluation
reference set.
If NULL (default), all DMUs are considered.
A value, vector of length m, or matrix m x ne
(where ne is the length of dmu_eval) with the input directions.
If dir_input == input matrix (of DMUS in dmu_eval) and
dir_output == 0, it is equivalent to input oriented (beta = 1 -
efficiency). If dir_input is omitted, input matrix (of DMUS in
dmu_eval) is assigned.
A value, vector of length s, or matrix s x ne
(where ne is the length of dmu_eval) with the output directions.
If dir_input == 0 and dir_output == output matrix (of DMUS in
dmu_eval), it is equivalent to output oriented (beta = efficiency - 1).
If dir_output is omitted, output matrix (of DMUS in dmu_eval) is assigned.
A value, vector of length m, or matrix m x ne
(where ne is the length of dmu_eval) with the input orientation parameters.
If d_input == 1 (default) and d_output == 0, it is equivalent
to input oriented. It is an alternative for dir_input.
A value, vector of length s, or matrix s x ne
(where ne is the length of dmu_eval) with the output orientation parameters.
If d_input == 0 and d_output == 1 (default), it is equivalent
to output oriented. It is an alternative to dir_output.
A string, determining the type of returns to scale, equal to "crs" (constant), "vrs" (variable), "nirs" (non-increasing), "ndrs" (non-decreasing) or "grs" (generalized).
Lower bound for the generalized returns to scale (grs).
Upper bound for the generalized returns to scale (grs).
Logical. If it is TRUE, it computes the max slack solution.
A value, vector of length m, or matrix m x ne
(where ne is the length of dmu_eval) with the weights of the input slacks
for the max slack solution.
A value, vector of length s, or matrix s x ne
(where ne is the length of dmu_eval) with the weights of the output
slacks for the max slack solution.
Logical. If it is TRUE, it returns the linear problems
(objective function and constraints) of stage 1.
Ignored, for compatibility issues.
Vicente Coll-Serrano (vicente.coll@uv.es). Quantitative Methods for Measuring Culture (MC2). Applied Economics.
Vicente Bolós (vicente.bolos@uv.es). Department of Business Mathematics
Rafael Benítez (rafael.suarez@uv.es). Department of Business Mathematics
University of Valencia (Spain)
Chambers, R.G.; Chung, Y.; Färe, R. (1996). "Benefit and Distance Functions", Journal of Economic Theory, 70(2), 407-419.
Chambers, R.G.; Chung, Y.; Färe, R. (1998). "Profit Directional Distance Functions and Nerlovian Efficiency", Journal of Optimization Theory and Applications, 95, 351-354.
model_basic, model_lgo, model_qgo
data("PFT1981")
# Selecting DMUs in Program Follow Through (PFT)
PFT <- PFT1981[1:49, ]
PFT <- make_deadata(PFT,
inputs = 2:6,
outputs = 7:9 )
eval_pft <- model_dir(PFT)
efficiencies(eval_pft)
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