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Last data update: 2014.03.03

R: Rate of change (RoC) calculation using DEA

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, orientation,
        sg="ssm", ftype="d", ncv=NULL, env=NULL)

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 `"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)

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.

Examples

# Reproduce Table 3 in Lim, D-J. et al.(2014)
  # Load airplane dataset
  data(dataset.airplane.2017)
  
  # ready
  x<-data.frame(Flew=rep(1,28))
  y<-subset(dataset.airplane.2017,select=3:7)
  d<-subset(dataset.airplane.2017,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
  data(dataset.hev.2013)
  
  # ready
  x<-subset(dataset.hev.2013,select=3)
  y<-subset(dataset.hev.2013,select=4:6)
  d<-subset(dataset.hev.2013,select=2)
  c<-subset(dataset.hev.2013,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).

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DJL)
Loading required package: car
Loading required package: combinat

Attaching package: 'combinat'

The following object is masked from 'package:utils':

    combn

Loading required package: lpSolveAPI
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DJL/roc.dea.Rd_%03d_medium.png", width=480, height=480)
> ### Name: roc.dea
> ### Title: Rate of change (RoC) calculation using DEA
> ### Aliases: roc.dea
> 
> ### ** Examples
> 
> # Reproduce Table 3 in Lim, D-J. et al.(2014)
>   # Load airplane dataset
>   data(dataset.airplane.2017)
>   
>   # ready
>   x<-data.frame(Flew=rep(1,28))
>   y<-subset(dataset.airplane.2017,select=3:7)
>   d<-subset(dataset.airplane.2017,select=2)
> 
>   # go
>   roc.dea(x,y,d,2007,"vrs","o","min","d")$roc_past
          [,1]
 [1,] 1.001630
 [2,] 1.000537
 [3,] 1.000635
 [4,] 1.000694
 [5,]       NA
 [6,]       NA
 [7,] 1.001415
 [8,]       NA
 [9,]       NA
[10,] 1.002128
[11,]       NA
[12,]       NA
[13,]       NA
[14,] 1.004655
[15,] 1.002274
[16,]       NA
[17,]       NA
[18,]       NA
[19,]       NA
[20,] 1.002942
[21,] 1.004579
[22,]       NA
[23,]       NA
[24,]       NA
[25,]       NA
[26,]       NA
[27,]       NA
[28,]       NA
> 
> # Reproduce Table 3 in Lim, D-J. et al.(2015)
>   # Load hev dataset
>   data(dataset.hev.2013)
>   
>   # ready
>   x<-subset(dataset.hev.2013,select=3)
>   y<-subset(dataset.hev.2013,select=4:6)
>   d<-subset(dataset.hev.2013,select=2)
>   c<-subset(dataset.hev.2013,select=7)
> 
>   # go
>   results<-roc.dea(x,y,d,2013,"vrs","o","min","d",env=c)
>   hev<-which(results$roc_local>0)
>   data.frame(Class=c[hev,],SOA=hev,LocalRoC=results$roc_local[hev,])[order(c[hev,]),]
   Class SOA LocalRoC
1      3  21 1.015619
3      3  40 1.004222
5      3  56 1.000826
8      3  67 1.008673
9      3  80 1.036642
13     3 145 1.019605
14     3 152 1.013672
4      5  51 1.040672
6      5  58 1.038538
7      5  59 1.050823
11     5  94 1.030800
2      6  26 1.037208
10     6  82 1.020235
12     7 142 1.037208
>   # 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).
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>