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Finds the maximal friends subsets of a given set of DMUs, according to Tone (2010). It uses an ascending algorithm in order to find directly maximal subsets.
maximal_friends(datadea,
dmu_ref = NULL,
rts = c("crs", "vrs", "nirs", "ndrs"),
tol = 1e-6,
silent = FALSE)
A list with numeric vectors representing maximal friends subsets of DMUs.
A deadata
object with n
DMUs, m
inputs and s
outputs.
A numeric vector containing which DMUs are the evaluation reference set,
i.e. the cluster of DMUs from which we want to find maximal friends.
If NULL
(default), all DMUs are considered.
A string, determining the type of returns to scale, equal to "crs" (constant), "vrs" (variable), "nirs" (non-increasing) or "ndrs" (non-decreasing).
Numeric, a tolerance margin for checking efficiency. It is 1e-6 by default.
Logical, if FALSE
(default) steps are printed.
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)
Tone, K. (2010). "Variations on the theme of slacks-based measure of efficiency in DEA", European Journal of Operational Research, 200, 901-907. tools:::Rd_expr_doi("10.1016/j.ejor.2009.01.027")
is.friends
, model_sbmeff
if (FALSE) {
data("PFT1981")
datadea <- make_deadata(PFT1981,
ni = 5,
no = 3)
# We find maximal friends of a cluster formed by the first 20 DMUs
result <- maximal_friends(datadea = datadea,
dmu_ref = 1:20)
}
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