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

roc.hdf

R Documentation

Rate of change (RoC) calculation using HDF

Description

Employs dm.hdf over time to calculate RoCs.

Usage

roc.hdf(xdata, ydata, date, t,
        rts="crs", wd=NULL, sg="ssm", ftype="d", 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
`wd`	Weak disposability vector indicating (an) undesirable output(s) (1 by s)
`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
`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

D.-J. Lim, Internal combustion engine race: naturally aspirated vs turbo/super-charged, working paper (2015).

Examples

# Load engine dataset
  df <- dataset.engine.2015

# Subset for 8 cylinder TC-P engines
  et <- subset(df, df[, 3] == 8 & df[, 8] == "TC-P")
  
# Parameters
  x <- subset(et, select = 4)
  y <- subset(et, select = 5 : 7)
  d <- subset(et, select = 2)
  w <- matrix(c(1, 0, 0), ncol = 3)

# Calc local Roc
  roc.hdf(x, y, d, 2015, "vrs", w, "min")

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