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Last data update: 2014.03.03

R: Arrival target setting using DEA

target.arrival.dea

R Documentation

Arrival target setting using DEA

Description

Employs dm.dea over time to estimate the arrival of known specifications.

Usage

target.arrival.dea(xdata, ydata, date, t, rts, orientation, sg="ssm", ftype="d")

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

Value

`$eff_t`	Efficiency at `t`
`$lambda_t`	Intensity vector at `t`
`$eft_date`	Effective date
`$roc_avg`	Average RoC
`$roc_local`	Local RoC
`$roc_ind`	Individualized RoC
`$arrival_avg`	Estimated arrival using `roc_avg`
`$arrival_seg`	Estimated arrival using `roc_ind`

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, 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. Wiley (forthcoming), 2016.

Examples

# Reproduce Table 4 in Lim, D-J., and Timothy R. Anderson.(2016)
  # 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
  target.arrival.dea(x,y,d,2007,"vrs","o","min","d")$arrival_seg

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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

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/target.arrival.dea.Rd_%03d_medium.png", width=480, height=480)
> ### Name: target.arrival.dea
> ### Title: Arrival target setting using DEA
> ### Aliases: target.arrival.dea
> 
> ### ** Examples
> 
> # Reproduce Table 4 in Lim, D-J., and Timothy R. Anderson.(2016)
>   # 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
>   target.arrival.dea(x,y,d,2007,"vrs","o","min","d")$arrival_seg
          [,1]
 [1,]       NA
 [2,]       NA
 [3,]       NA
 [4,]       NA
 [5,]       NA
 [6,]       NA
 [7,]       NA
 [8,]       NA
 [9,]       NA
[10,]       NA
[11,]       NA
[12,]       NA
[13,]       NA
[14,]       NA
[15,]       NA
[16,]       NA
[17,]       NA
[18,]       NA
[19,]       NA
[20,]       NA
[21,]       NA
[22,]       NA
[23,]       NA
[24,]       NA
[25,] 2011.488
[26,] 2013.453
[27,] 2012.446
[28,] 2020.158
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>