Description Usage Arguments Details Value Author(s) References See Also Examples

This function offers several k-nearest neighbor methods for the imputation of missing values in compositional data.

1 2 3 4 5 6 7 8 9 10 11 |

`x` |
data frame or matrix |

`method` |
method (at the moment, only “knn” can be used) |

`k` |
number of nearest neighbors chosen for imputation |

`metric` |
“Aichison” or “Euclidean” |

`agg` |
“median” or “mean”, for the aggregation of the nearest neighbors |

`primitive` |
if TRUE, a more enhanced search for the $k$-nearest neighbors is obtained (see details) |

`normknn` |
An adjustment of the imputed values is performed if TRUE |

`das` |
depricated. if TRUE, the definition of the Aitchison distance, based on simple logratios of the compositional part, is used (Aitchison, 2000) to calculate distances between observations. if FALSE, a version using the clr transformation is used. |

`adj` |
either ‘median’ (default) or ‘sum’ can be chosen for the adjustment of the nearest neighbors, see Hron et al., 2010. |

The Aitchison `metric`

should be chosen when dealing with compositional
data, the Euclidean `metric`

otherwise.

If `primitive`

*==* FALSE, a sequential search for the
*k*-nearest neighbors is applied for every missing value where all
information corresponding to the non-missing cells plus the information in
the variable to be imputed plus some additional information is available. If
`primitive`

*==* TRUE, a search of the *k*-nearest neighbors
among observations is applied where in addition to the variable to be
imputed any further cells are non-missing.

If `normknn`

is TRUE (prefered option) the imputed cells from a nearest
neighbor method are adjusted with special adjustment factors (more details
can be found online (see the references)).

`xOrig ` |
Original data frame or matrix |

`xImp ` |
Imputed data |

`w ` |
Amount of imputed values |

`wind ` |
Index of the missing values in the data |

`metric ` |
Metric used |

Matthias Templ

Aitchison, J., Barcelo-Vidal, C., Martin-Fernandez, J.A.,
Pawlowsky-Glahn, V. (2000) Logratio analysis and compositional distance,
*Mathematical Geology*, 32(3), 271-275.

Hron, K., Templ, M., Filzmoser, P. (2010) Imputation of missing values
for compositional data using classical and robust methods
*Computational Statistics and Data Analysis*, 54 (12),
3095-3107.

1 2 3 4 5 6 | ```
data(expenditures)
x <- expenditures
x[1,3]
x[1,3] <- NA
xi <- impKNNa(x)$xImp
xi[1,3]
``` |

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