| model_profit | R Documentation |
Cost, revenue and profit efficiency DEA models.
model_profit(datadea,
dmu_eval = NULL,
dmu_ref = NULL,
price_input = NULL,
price_output = NULL,
rts = c("crs", "vrs", "nirs", "ndrs", "grs"),
L = 1,
U = 1,
restricted_optimal = TRUE,
returnlp = FALSE,
...)
datadea |
A |
dmu_eval |
A numeric vector containing which DMUs have to be evaluated.
If |
dmu_ref |
A numeric vector containing which DMUs are the evaluation reference set.
If |
price_input |
Unit prices of inputs for cost or profit efficiency models.
It is a value, vector of length |
price_output |
Unit prices of outputs for revenue or profit efficiency models.
It is a value, vector of length |
rts |
A string, determining the type of returns to scale, equal to "crs" (constant), "vrs" (variable), "nirs" (non-increasing), "ndrs" (non-decreasing) or "grs" (generalized). |
L |
Lower bound for the generalized returns to scale (grs). |
U |
Upper bound for the generalized returns to scale (grs). |
restricted_optimal |
Logical. If it is |
returnlp |
Logical. If it is |
... |
Ignored, for compatibility issues. |
A list of class dea with the results for the evaluated DMUs (DMU component),
along with any other necessary information to replicate the results, such as
the name of the model and parameters orientation, rts,
dmu_eval and dmu_ref.
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)
Coelli, T.; Prasada Rao, D.S.; Battese, G.E. (1998). An introduction to efficiency and productivity analysis. Jossey-Bass, San Francisco, pp 73–104. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/ev.1441")}
model_deaps, model_nonradial,
model_sbmeff
# Example 1. Replication of results in Coelli et al. (1998, p.166).
# Cost efficiency model.
data("Coelli_1998")
# Selection of prices: input_prices is the transpose where the prices for inputs are.
input_prices <- t(Coelli_1998[, 5:6])
data_example1 <- make_deadata(Coelli_1998,
ni = 2,
no = 1)
result1 <- model_profit(data_example1,
price_input = input_prices,
rts = "crs",
restricted_optimal = FALSE)
# notice that the option by default is restricted_optimal = TRUE
efficiencies(result1)
# Example 2. Revenue efficiency model.
data("Coelli_1998")
# Selection of prices for output: output_prices is the transpose where the prices for outputs are.
output_prices <- t(Coelli_1998[, 7])
data_example2 <- make_deadata(Coelli_1998,
ni = 2,
no = 1)
result2 <- model_profit(data_example2,
price_output = output_prices,
rts = "crs",
restricted_optimal = FALSE)
# notice that the option by default is restricted_optimal = TRUE
efficiencies(result2)
# Example 3. Profit efficiency model.
data("Coelli_1998")
# Selection of prices for inputs and outputs: input_prices and output_prices are
# the transpose where the prices (for inputs and outputs) are.
input_prices <- t(Coelli_1998[, 5:6])
output_prices <- t(Coelli_1998[, 7])
data_example3 <- make_deadata(Coelli_1998,
ni = 2,
no = 1)
result3 <- model_profit(data_example3,
price_input = input_prices,
price_output = output_prices,
rts = "crs",
restricted_optimal = FALSE)
# notice that the option by default is restricted_optimal = TRUE
efficiencies(result3)
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