R: Imputation of missing values in compositional data using knn...

impKNNa {robCompositions} | R Documentation

## Imputation of missing values in compositional data using knn methods

### Description

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

### Usage

    impKNNa(
      x,
      method = "knn",
      k = 3,
      metric = "Aitchison",
      agg = "median",
      primitive = FALSE,
      normknn = TRUE,
      das = FALSE,
      adj = "median"
    )

### Arguments

`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.

### Details

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)).

### Value

`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

### Author(s)

Matthias Templ

### References

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.

### See Also

`impCoda`

### Examples

    data(expenditures)
    x <- expenditures
    x[1,3]
    x[1,3] <- NA
    xi <- impKNNa(x)$xImp
    xi[1,3]

[Package robCompositions version 2.2.1 Index]