ABCanalysis: 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
ABCanalysis(Data,PlotIt,ABCcurvedata)
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.
PlotIt
if variable is used, a plot is made, set with arbitrary value
Output is of type list which parts are described in the following
Aindvector [1:j], A==Data(Aind) : with little effort much Yield
Bindvector [1:l], B==Data(Bind) : effort and Yield are balanced
Cind(vector [1:m], C==Data(Cind) : much effort for little Yield
ABexchangedBoolean, TRUE if Point A is the Break Even and point B is the Pareto Point, FALSE otherwise
Ac(Ax,Ay), Pareto point or BreakEven Point indicated by ABexchanged
Bc(Bx,By), Pareto point or BreakEven Point indicated by ABexchanged
CSubmarginal point: minimum distance to [B_x,1]
smallestADataBoundary AB, defined by point A or B with ABexchanged
smallestBDataBoundary BC, defined by point C
AlimitIndInInterpolationindex of AB Boundary in [p, ABC], the interpolation of the ABC plot
BlimitIndInInterpolationindex of BC Boundary in [p, ABC], the interpolation of the ABC plot
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.