Rate of change (RoC) calculation using DEA

Description

Employs dm.dea over time to calculate RoCs.

Usage

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roc.dea(xdata, ydata, date, t, rts="crs", orientation,
        sg="ssm", ftype="d", ncv=NULL, env=NULL, 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
"crs" Constant RTS (default)
"vrs" Variable RTS
"irs" Increasing RTS
"drs" Decreasing RTS

orientation

Orientation of the measurement
"i" Input-orientation
"o" Output-orientation

sg

Employs second-stage optimization
"ssm" Slack-sum maximization (default)
"max" Date-sum maximization
"min" Date-sum minimization

ftype

Frontier type
"d" Dynamic frontier (default)
"s" Static frontier

ncv

Non-controllable variable index(binary) for internal NDF (1 by (m+s))

env

Environment index for external NDF (n by 1)

cv

Convexity assumption
"convex" Convexity holds (default)
"fdh" Free disposal hull (this will override rts)

Value

$eff_r

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

$eff_t

Efficiency at t

$lambda_t

Intensity vector at t

$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

Lim, Dong-Joon, 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, 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, et al. Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs). Handbook of Operations Analytics Using Data Envelopment Analysis. Springer (forthcoming), 2016.

See Also

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

Examples

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# Reproduce Table 3 in Lim, D-J. 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, D-J. 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, D-J. et al., 2016).