Description Usage Arguments Value Author(s) References See Also Examples
Employs dm.dea
over time to calculate RoCs.
1 2 
xdata 
Input(s) vector (n by m) 
ydata 
Output(s) vector (n by s) 
date 
Production date (n by 1) 
t 
A vantage point from which the RoC is captured 
rts 
Returns to scale assumption 
orientation 
Orientation of the measurement 
sg 
Employs secondstage optimization 
ftype 
Frontier type 
ncv 
Noncontrollable variable index(binary) for internal NDF (1 by (m+s)) 
env 
Environment index for external NDF (n by 1) 
cv 
Convexity assumption 

Efficiency at release (i.e., at each production date) 

Efficiency at 

Intensity vector at 

Effective date 

RoC observed from the obsolete DMUs in the past 

Average RoC 

Local RoC 
DongJoon Lim, PhD
Lim, DongJoon, Timothy R. Anderson, and Oliver Lane Inman. "Choosing effective dates from multiple optima in Technology Forecasting using Data Envelopment Analysis (TFDEA)." Technological Forecasting and Social Change 88 (2014): 91~97.
Lim, DongJoon, et al. "Comparing technological advancement of hybrid electric vehicles (HEV) in different market segments." Technological Forecasting and Social Change 97 (2015): 140~153.
Lim, DongJoon, et al. Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs). Handbook of Operations Analytics Using Data Envelopment Analysis. Springer (forthcoming), 2016.
dm.dea
Distance measure using DEA
roc.dea
RoC calculation using DEA
map.soa.dea
SOA mapping using DEA
target.arrival.dea
Arrival target setting using DEA
target.spec.dea
Spec target setting using DEA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  # Reproduce Table 3 in Lim, DJ. et al.(2014)
# Load airplane dataset
df < dataset.airplane.2017
# ready
x < data.frame(Flew = rep(1, 28))
y < subset(df, select = 3 : 7)
d < subset(df, select = 2)
# go
roc.dea(x, y, d, 2007, "vrs", "o", "min", "d")$roc_past
# Reproduce Table 3 in Lim, DJ. et al.(2015)
# Load hev dataset
df < dataset.hev.2013
# ready
x < subset(df, select = 3)
y < subset(df, select = 4 : 6)
d < subset(df, select = 2)
c < subset(df, select = 7)
# go
results < roc.dea(x, y, d, 2013, "vrs", "o", "min", "d", env = c)
hev < which(results$roc_local > 0)
data.frame(Class = c[hev, ],
SOA = hev,
LocalRoC = results$roc_local[hev, ])[order(c[hev, ]), ]
# NOTE: the published results include a typo on roc_local[82,]
# this will be corrected in forthcoming book chapter(Lim, DJ. et al., 2016).

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