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Tip revision: 81afe0ecccdc17114e19306afac7d2079eae4b2f authored by Florian Lerch on 13 March 2017, 13:31:38 UTC
version 1.2.1
Tip revision: 81afe0e
%- Also NEED an '\alias' for EACH other topic documented here.
Computed ABC analysis: calculates a division of the data in 3 classes A, B and C 
 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
%- maybe also 'usage' for other objects documented here.
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.

Pareto point: Minimum distance to (0,1) = minimal unrealized potential

BreakEven Point: \code{B_x} is the x value of the point, where the slope of ABCcurve equals one.

For further description to \code{p} in variable \code{AlimitIndInInterpolation} see \link{ABCcurve}
Output is of type list which parts are described in the following
	\item{Aind}{vector [1:j],  A==Data(Aind) : with little effort much Yield}
	\item{Bind}{vector [1:l],  B==Data(Bind) : effort and Yield are balanced}
	\item{Cind}{(vector [1:m],  C==Data(Cind) : much effort for little Yield}
	\item{smallestAData}{Boundary AB, defined by point A or B with ABexchanged}
	\item{smallestBData}{Boundary BC, defined by point C}

Michael Thrun




Ultsch. A ., Lotsch J.: Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data, PloS one, Vol. 10(6), pp. e0129767. doi 10.1371/journal.pone.0129767, 2015.
\keyword{ABC analysis}
\keyword{Lorenz curve}
\keyword{Computed ABC analysis}
\keyword{Lorenz}% __ONLY ONE__ keyword per line
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