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 |

### Author(s)

Michael Thrun

http://www.uni-marburg.de/fb12/datenbionik

### 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.

### See Also

### Examples

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

*ABCanalysis*version 1.2.1 Index]