DJL (version 3.9)

roc.hdf: 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")

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

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)

Author

Dong-Joon Lim, PhD

References

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

See Also

dm.hdf Distance measure using HDF
roc.hdf RoC calculation using HDF
map.soa.hdf SOA mapping using HDF
target.arrival.hdf Arrival target setting using HDF

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")