deaR (version 1.4.1)

summary.dea_fuzzy: Summary Fuzzy DEA models.

Description

Summary of the results obtained by a fuzzy DEA model.

Usage

# S3 method for dea_fuzzy
summary(object, ..., exportExcel = FALSE, filename = NULL, returnList = FALSE)

Value

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.

Arguments

object

An object of class "dea_fuzzy" obtained with a fuzzy DEA model function (modelfuzzy_guotanaka, modelfuzzy_kaoliu, modelfuzzy_possibilistic).

...

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.

Author

Vicente Coll-Serrano (). Quantitative Methods for Measuring Culture (MC2). Applied Economics.

Vicente Bolós (). Department of Business Mathematics

Rafael Benítez (). Department of Business Mathematics

University of Valencia (Spain)

References

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. tools:::Rd_expr_doi("10.1016/S0165-0114(02)00608-5")

Examples

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)