View source: R/summary.dea_fuzzy.R
summary.dea_fuzzy | R Documentation |
Summary of the results obtained by a fuzzy DEA model.
## S3 method for class 'dea_fuzzy'
summary(object, ..., exportExcel = FALSE, filename = NULL, returnList = FALSE)
object |
An object of class |
... |
Extra options. |
exportExcel |
Logical value. If TRUE (FALSE by default) the results are also exported to an Excel file. |
filename |
Character string. Absolute file name (including path) of the exported Excel file. If NULL, then the file name will be "ResultsDEA" + timestamp.xlsx. |
returnList |
Logical value. If TRUE then the results are given as a list of data frames. If FALSE (default) all the data frames are merged into a single data frame. |
If the model is that from Guo and Tanaka (modelfuzzy_guotanaka
), it returns a data.frame
with columns: DMU, alpha cuts and efficiencies.
For the possibilistic model (modelfuzzy_possibilistic
) it returns a data.frame with columns:
DMU, alpha-cuts, efficiencies and the corresponding lambda values
For the Kao-Liu model (modelfuzzy_kaoliu
), the result may depend on the crisp sub-model used.
It will contain a data.frame with the efficiencies (if any), the slacks and superslacks (if any),
the lambda values and the targets.
If exportExcel
is TRUE, then an Excel file will be created containing as many
sheets as necessary depending on the variables returned.
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)
Leon, T.; Liern, V. Ruiz, J.; Sirvent, I. (2003). "A Possibilistic Programming Approach to the Assessment of Efficiency with DEA Models", Fuzzy Sets and Systems, 139, 407–419. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0165-0114(02)00608-5")}
data("Leon2003")
data_example <- make_deadata_fuzzy(Leon2003,
inputs.mL = 2,
inputs.dL = 3,
outputs.mL = 4,
outputs.dL = 5)
result <- modelfuzzy_possibilistic(data_example,
h = seq(0, 1, by = 0.1),
orientation = "io",
rts = "vrs")
summary(result)
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