kNN: k-Nearest Neighbour Imputation

In VIM: Visualization and Imputation of Missing Values

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,
  methodStand = "range",
  ordFun = medianSamp
)

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::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	list of length equal to the number of variables, with a donorcond condition as character string. e.g. a list element can be ">5" or c(">5","<10). If the list element for a variable is NULL no condition will be applied for this 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::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
methodStand	either "range" or "iqr" to be used in the standardization of numeric vaiables in the gower distance
ordFun	function for aggregating the k Nearest Neighbours in the case of a ordered factor variable

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.

See Also

Other imputation methods: hotdeck(), impPCA(), irmi(), matchImpute(), medianSamp(), rangerImpute(), regressionImp(), sampleCat()

Examples

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

VIM documentation built on Aug. 25, 2022, 5:07 p.m.