ABCanalysis (version 1.1.0)

calculatedABCanalysis: Computed ABC analysis: calculates a division of the data in 3 classes A, B and C

Description

divide the Data in 3 classes A, B and C such that A=Data[Aind] : with low effort much yield B=Data[Bind] : yield and effort are about equal C=Data[Cind] : with much effort low yield

Usage

calculatedABCanalysis(Data)

Arguments

Data

vector(1:n) describes an array of data: n cases in rows of one variable, if matrix or dataframe then first column will be used.

Value

• Output is of type list which parts are described in the following
• Aind
  vector [1:j], A==Data(Aind) : with little effort much Yield
• Bind
  vector [1:l], B==Data(Bind) : effort and Yield are balanced
• Cind
  (vector [1:m], C==Data(Cind) : much effort for little Yield
• smallestAData
  Boundary AB, defined by point A or B with ABexchanged
• smallestBData
  Boundary BC, defined by point C

Details

Pareto point: Minimum distance to (0,1) = minimal unrealized potential BreakEven Point: B_x is the x value of the point, where the slope of ABCcurve equals one. For further description to p in variable AlimitIndInInterpolation see ABCcurve

References

Ultsch. A ., Lotsch J.: Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data, PLoS One, Jun 10, 10(6), e0129767, 2015.

See Also

ABCanalysis

Examples

data("SwissInhabitants")
	abc=calculatedABCanalysis(SwissInhabitants)
	A=abc$Aind
	B=abc$Bind
	C=abc$Cind
	Agroup=SwissInhabitants[A]
	Bgroup=SwissInhabitants[B]
	Cgroup=SwissInhabitants[C]