roc.sf {DJL} | R Documentation |
Rate of change (RoC) calculation using SF
Description
Employs dm.sf
over time to calculate RoCs. This function is valid only when multiplicative form of directional vector is used.
Usage
roc.sf(xdata, ydata, date, t,
rts="crs", g=NULL, wd=NULL, sg="ssm", ftype="d", cv="convex")
Arguments
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 |
g |
Directional vector indicating a measurement direction (n by (m+s)) |
wd |
Weak disposability vector indicating (an) undesirable output(s) (1 by s) |
sg |
Employs second-stage optimization |
ftype |
Frontier type |
cv |
Convexity assumption |
Value
$eff_r |
Efficiency at release (i.e., at each production date) |
$eff_t |
Efficiency at |
$lambda_t |
Intensity vector at |
$eft_date |
Effective date |
$roc_past |
RoC observed from the obsolete DMUs in the past |
$roc_avg |
Average RoC |
$roc_local |
Local RoC |
Author(s)
Dong-Joon Lim, PhD
References
D.-J. Lim, Internal combustion engine race: naturally aspirated vs turbo/super-charged, working paper (2015).
See Also
dm.sf
Distance measure using SF
roc.sf
RoC calculation using SF
map.soa.sf
SOA mapping using SF
target.arrival.sf
Arrival target setting using SF
Examples
# Reproduce Mercedes-Benz CLA45 AMG's local RoC in Table 5 in Lim, D-J. (2015)
# Load engine dataset
df <- dataset.engine.2015
# Subset for 4 cylinder engines
fce <- subset(df, df[, 3] == 4)
# Parameters
x <- subset(fce, select = 4)
y <- subset(fce, select = 5 : 7)
d <- subset(fce, select = 2)
g <- as.matrix(data.frame(0, y))
w <- matrix(c(1, 0, 0), ncol = 3)
# Calc local Roc
roc.sf(x, y, d, 2014, "crs", g, w, "min")$roc_local[348, ]