map.soa.hdf: SOA mapping using HDF
In DJL: Distance Measure Based Judgment and Learning

map.soa.hdf

R Documentation

SOA mapping using HDF

Description

Employs dm.hdf over time to generate a state-of-the-art map.

Usage

map.soa.hdf(xdata, ydata, date,
            rts="crs", wd=NULL, sg="ssm", cv="convex", mk="dmu")

Arguments

`xdata`	Input(s) vector (n by m)
`ydata`	Output(s) vector (n by s)
`date`	Production date (n by 1)
`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 (only if `date` is defined) `"min"` Date-sum minimization (only if `date` is defined)
`cv`	Convexity assumption `"convex"` Convexity holds (default) `"fdh"` Free disposal hull (this will override `rts`)
`mk`	Marker on the map `"dmu"` DMU index (default) `"eff"` Efficiency score

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 SC/TC 8 cylinder engines
  stc.8 <- subset(df, grepl("^.C..", df[, 8]) & df[, 3] == 8)

# Parameters
  x <- subset(stc.8, select = 4)
  y <- subset(stc.8, select = 5:7)
  d <- subset(stc.8, select = 2)

# Generate an SOA map
  map.soa.hdf(x, y, d, "vrs")

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