impKNNa.Rd

```
\name{impKNNa}
\alias{impKNNa}
\title{ 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{
\item{x}{ data frame or matrix }
\item{method}{ method (at the moment, only \dQuote{knn} can be used) }
\item{k}{ number of nearest neighbors chosen for imputation }
\item{metric}{ \dQuote{Aichison} or \dQuote{Euclidean} }
\item{agg}{ \dQuote{median} or \dQuote{mean}, for the aggregation of the nearest neighbors }
\item{primitive}{ if TRUE, a more enhanced search for the $k$-nearest neighbors is obtained (see details)}
\item{normknn}{ An adjustment of the imputed values is performed if TRUE }
\item{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.}
\item{adj}{ either \sQuote{median} (default) or \sQuote{sum} can be chosen for the adjustment of the nearest
neighbors, see Hron et al., 2010. }
}
\details{
The Aitchison \code{metric} should be chosen when dealing with compositional data, the Euclidean \code{metric} otherwise.
If \code{primitive} \eqn{==} FALSE, a sequential search for the \eqn{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 \code{primitive} \eqn{==} TRUE, a search of the
\eqn{k}-nearest neighbors among observations is applied where in addition to the variable
to be imputed any further cells are non-missing.
If \code{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{
\item{xOrig }{Original data frame or matrix}
\item{xImp }{Imputed data}
\item{w }{Amount of imputed values}
\item{wind }{Index of the missing values in the data}
\item{metric }{Metric used}
}
\references{
Aitchison, J. and Barcelo-Vidal, C. and Martin-Fernandez, J.A. and Pawlowsky-Glahn, V. (2000)
Logratio analysis and compositional distance, Mathematical Geology 32(3):271-275.
Hron, K. and Templ, M. and Filzmoser, P. (2010) Imputation of missing values for compositional data using classical and robust methods
\emph{Computational Statistics and Data Analysis}, vol 54 (12), pages 3095-3107.
}
\author{ Matthias Templ }
\seealso{ \code{\link{impCoda}} }
\examples{
data(expenditures)
x <- expenditures
x[1,3]
x[1,3] <- NA
xi <- impKNNa(x)$xImp
xi[1,3]
}
\keyword{ manip }
\keyword{ multivariate }
```