roc.dea: Rate of change (RoC) calculation using DEA
In DJL: Distance Measure Based Judgment and Learning

roc.dea

R Documentation

Rate of change (RoC) calculation using DEA

Description

Employs dm.dea over time to calculate RoCs.

Usage

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, and Dong-Hyuk Yang. "Assessment of Regulatory Requirements on Technological Changes: The Increasing Dominance of Downsized Turbo/Super-Charged Engines Over Naturally Aspirated Engines." IEEE Access 7 (2019): 84839-84848.

Examples

# 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 has been corrected in Lim, D-J. et al. (2016).

DJL documentation built on March 31, 2023, 9:05 p.m.