map.soa.dea: SOA mapping using DEA

In DJL: Distance Measure Based Judgment and Learning

SOA mapping using DEA

Description

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

Usage

map.soa.dea(xdata, ydata, date, rts="crs", orientation,
            sg="ssm", ncv=NULL, env=NULL, 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
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 (only if date is defined) "min" Date-sum minimization (only if date is defined)
ncv	Non-controllable variable index(binary) for internal NDF (1 by (m+s))
env	Environment index for external NDF (n by 1)
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

See Also

map.soa.ddf SOA mapping using DDF
map.soa.dea SOA mapping using DEA
map.soa.hdf SOA mapping using HDF
map.soa.sbm SOA mapping using SBM
map.soa.sf SOA mapping using SF

Examples

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

# Subset for forced induction systems
  fis <- subset(df, grepl("^.C..", df[, 8]))

# Parameters
  x <- subset(fis, select = 4)
  y <- subset(fis, select = 6 : 7)
  d <- subset(fis, select = 2)

# Generate an SOA map
  map.soa.dea(x, y, d, "crs", "o")

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