dm.sf | R Documentation |
Implements Luenberger's shortage (benefit) function (radial & non-oriented measure).
dm.sf(xdata, ydata, rts="crs", g=NULL,
wd=NULL, se=FALSE, sg="ssm", date=NULL, cv="convex", o=NULL)
xdata |
Input(s) vector (n by m) |
ydata |
Output(s) vector (n by s) |
rts |
Returns to scale assumption |
g |
Directional vector indicating a measurement direction (n by (m+s)) |
wd |
Weak disposability vector indicating (an) undesirable output(s) (1 by s) |
se |
Implements super-efficiency model alike Anderson & Peterson's model if |
sg |
Employs second-stage optimization |
date |
Production date (n by 1) |
cv |
Convexity assumption |
o |
DMU index to calc. |
$eff |
Efficiency score |
$lambda |
Intensity vector |
$mu |
Secondary intensity vector for weak disposability under VRS |
$xslack |
Input slack |
$yslack |
Output slack |
$w |
Input (dual) weight |
$p |
Output (dual) weight |
$u |
Free (dual) variable |
Dong-Joon Lim, PhD
Luenberger, David G. "Benefit functions and duality." Journal of mathematical economics 21.5 (1992): 461~481.
Chambers, Robert G., Yangho Chung, and Rolf Fare. "Profit, directional distance functions, and Nerlovian efficiency." Journal of optimization theory and applications 98.2 (1998): 351~364.
dm.ddf
Distance measure using DDF
dm.dea
Distance measure using DEA
dm.hdf
Distance measure using HDF
dm.sbm
Distance measure using SBM
dm.sf
Distance measure using SF
# Additive form shortage function
# ready
x <- matrix(c(5, 1, 4), ncol = 1)
y <- matrix(c(8, 3, 5, 6, 4, 1), ncol = 2)
g <- matrix(c(1), nrow = 3, ncol = 3)
w <- matrix(c(1, 0), ncol = 2)
# go
dm.sf(x, y, "crs", g, w)
# Multiplicative form shortage function
# ready
g <- cbind(x, y)
# go
dm.sf(x, y, "crs", g, w)
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