R: k-Nearest Neighbour Imputation

kNN {VIM} | R Documentation

## k-Nearest Neighbour Imputation

### Description

k-Nearest Neighbour Imputation based on a variation of the Gower Distance for numerical, categorical, ordered and semi-continous variables.

### Usage

    kNN(data, variable = colnames(data), metric = NULL, k = 5,
      dist_var = colnames(data), weights = NULL, numFun = median,
      catFun = maxCat, makeNA = NULL, NAcond = NULL, impNA = TRUE,
      donorcond = NULL, mixed = vector(), mixed.constant = NULL,
      trace = FALSE, imp_var = TRUE, imp_suffix = "imp", addRF = FALSE,
      onlyRF = FALSE, addRandom = FALSE, useImputedDist = TRUE,
      weightDist = FALSE)

### Arguments

`data` | data.frame or matrix
`variable` | variables where missing values should be imputed
`metric` | metric to be used for calculating the distances between
`k` | number of Nearest Neighbours used
`dist_var` | names or variables to be used for distance calculation
`weights` | weights for the variables for distance calculation. If `weights = "auto"` weights will be selected based on variable importance from random forest regression, using function `ranger`. Weights are calculated for each variable seperately.
`numFun` | function for aggregating the k Nearest Neighbours in the case of a numerical variable
`catFun` | function for aggregating the k Nearest Neighbours in the case of a categorical variable
`makeNA` | list of length equal to the number of variables, with values, that should be converted to NA for each variable
`NAcond` | list of length equal to the number of variables, with a condition for imputing a NA
`impNA` | TRUE/FALSE whether NA should be imputed
`donorcond` | condition for the donors e.g. list(">5"), must be NULL or a list of same length as variable
`mixed` | names of mixed variables
`mixed.constant` | vector with length equal to the number of semi-continuous variables specifying the point of the semi-continuous distribution with non-zero probability
`trace` | TRUE/FALSE if additional information about the imputation process should be printed
`imp_var` | TRUE/FALSE if a TRUE/FALSE variables for each imputed variable should be created show the imputation status
`imp_suffix` | suffix for the TRUE/FALSE variables showing the imputation status
`addRF` | TRUE/FALSE each variable will be modelled using random forest regression (`ranger`) and used as additional distance variable.
`onlyRF` | TRUE/FALSE if TRUE only additional distance variables created from random forest regression will be used as distance variables.
`addRandom` | TRUE/FALSE if an additional random variable should be added for distance calculation
`useImputedDist` | TRUE/FALSE if an imputed value should be used for distance calculation for imputing another variable. Be aware that this results in a dependency on the ordering of the variables.
`weightDist` | TRUE/FALSE if the distances of the k nearest neighbours should be used as weights in the aggregation step

### Details

The function sampleCat samples with probabilites corresponding to the occurrence of the level in the NNs. The function maxCat chooses the level with the most occurrences and random if the maximum is not unique. The function gowerD is used by kNN to compute the distances for numerical, factor ordered and semi-continous variables.

### Value

the imputed data set.

### Author(s)

Alexander Kowarik, Statistik Austria

### References

A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16.

### Examples

    data(sleep)
    kNN(sleep)
    library(laeken)
    kNN(sleep, numFun = weightedMean, weightDist=TRUE)

[Package VIM version 4.8.0 Index]