calculatedABCanalysis {ABCanalysis} R Documentation

## 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.

### 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

### 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

Michael Thrun

### References

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.

`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]

```

[Package ABCanalysis version 1.2.1 Index]