ABCanalysis {ABCanalysis} | R Documentation |
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
ABCanalysis(Data,ABCcurvedata,PlotIt=FALSE)
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. |
ABCcurvedata |
only for internal usage, list from ABCcurve |
PlotIt |
default(FALSE), if variable is used, a plot is made, set with arbitrary value |
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
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 |
ABexchanged |
Boolean, TRUE if Point A is the Break Even and point B is the Pareto Point, FALSE otherwise |
A |
c(Ax,Ay), Pareto point or BreakEven Point indicated by ABexchanged |
B |
c(Bx,By), Pareto point or BreakEven Point indicated by ABexchanged |
C |
Submarginal point: minimum distance to |
smallestAData |
Boundary AB, defined by point A or B with ABexchanged |
smallestBData |
Boundary BC, defined by point C |
AlimitIndInInterpolation |
index of AB Boundary in [ |
BlimitIndInInterpolation |
index of BC Boundary in [ |
Michael Thrun
http://www.uni-marburg.de/fb12/datenbionik
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.
data("SwissInhabitants")
abc=ABCanalysis(SwissInhabitants,PlotIt=TRUE)
A=abc$Aind
B=abc$Bind
C=abc$Cind
Agroup=SwissInhabitants[A]
Bgroup=SwissInhabitants[B]
Cgroup=SwissInhabitants[C]