dea.sbm.ccr.io {DEA}R Documentation

CCR input oriented slack-based measure of efficiency

Description

Fits an input oriented slack-based DEA model with constant returns to scale. This model corresponds to eq. 4.78 in Cooper et al., 2007 (see References).

Usage

dea.sbm.ccr.io( X , Y , pslv = FALSE , dual = FALSE , infor = FALSE )

Arguments

X inputs. Bidimensional data frame or object that can be coerced to a data frame. Each row corresponds to a DMU, each column to an input.
Y outputs. Bidimensional data frame or object that can be coerced to a data frame. Each row corresponds to a DMU, each column to an output.
pslv logical. Defaults to FALSE. If TRUE, the simplex presolver will be used.
dual logical. Defaults to FALSE. IF TRUE and the initial basic solution is dual feasible, dual simplex is used.
infor logical. Defaults to FALSE. If TRUE, information about the convergence of the optimization process will be sent to the console.

Details

Missing values are not allowed.newline newline If pslv=TRUE, a glpk built-in LP presolver is enabled. It may improve the numeric properties of the LP problem. newline newline If infor=TRUE, each line of information sent to the console consists of five columns with some details about the calculus of the efficiency for each DMU. First column indicates if the linear program has been successfully solved, or not ("OK" or "FAULT"). The other ones contain information reported by the glpk solver about the quality of the solution. Their values can be "H", "M", "L" or "?", which mean high, medium, low or wrong, respectively. If all the indicators show high or medium quality, the user can be sure that the solution is quite accurate. For more details see Makhorin (2005).

Value

a list with components:

ro a vector containing the slack-based-measure of efficiency of the DMUs.
lambda a bidimensional data frame containing the λ coefficients. Each row corresponds to a DMU. The columns correspond to the λs.
slack a bidimensional data frame containing the slacks for each DMU. Each row corresponds to a DMU.

References

Cooper, W. W., Seiford, L. M. and Tone, K. (2007): Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. Second edition. Springer. New York.

Makhorin, A. (2005): GNU Linear Programming Kit. Reference Manual. Version 4.8. Available at http://www.gnu.org/software/glpk/

See Also

dea.sbm.bcc.io


[Package DEA version 0.1-2 Index]