View source: R/target.arrival.sf.R
target.arrival.sf | R Documentation |
Employs dm.sf
over time to estimate the arrival of known specifications. This function is valid only when multiplicative form of directional vector is used.
target.arrival.sf(xdata, ydata, date, t, rts="crs", g=NULL,
wd=NULL, sg="ssm", ftype="d", cv="convex", anc=FALSE)
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 |
anc |
Implements a stepwise RoC computation if |
$eff_t |
Efficiency at |
$lambda_t |
Intensity vector at |
$eft_date |
Effective date |
$roc_avg |
Average RoC |
$roc_anc |
Local RoCs across the periods |
$roc_local |
Local RoC |
$roc_ind |
Individualized RoC |
$arrival_avg |
Estimated arrival using |
$arrival_seg |
Estimated arrival using |
Dong-Joon Lim, PhD
Lim, Dong-Joon, et al. "Comparing technological advancement of hybrid electric vehicles (HEV) in different market segments." Technological Forecasting and Social Change 97 (2015): 140~153.
Lim, Dong-Joon, and Timothy R. Anderson. Time series benchmarking analysis for new product scheduling: who are the competitors and how fast are they moving forward?. Advances in DEA Theory and Applications: with Examples in Forecasting Models. (2017): 443-458.
dm.sf
Distance measure using SF
roc.sf
RoC calculation using SF
map.soa.sf
SOA mapping using SF
target.arrival.sf
Arrival target setting using SF
# Estimate arrivals of MY2013 hev models
# Load hev dataset
df <- dataset.hev.2013
# ready
x <- subset(df, select = 3)
y <- subset(df, select = 4 : 6)
d <- subset(df, select = 2)
g <- data.frame(x, y)
# go
target.arrival.sf(x, y, d, 2012, "vrs", g)$arrival_seg
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