% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/impKNNa.R
\name{impKNNa}
\alias{impKNNa}
\title{Imputation of missing values in compositional data using knn methods}
\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.}
}
\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}
}
\description{
This function offers several k-nearest neighbor methods for the imputation
of missing values in compositional data.
}
\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)).
}
\examples{

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

}
\references{
Aitchison, J., Barcelo-Vidal, C., Martin-Fernandez, J.A.,
Pawlowsky-Glahn, V. (2000) Logratio analysis and compositional distance,
\emph{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
\emph{Computational Statistics and Data Analysis}, 54 (12),
3095-3107.
}
\seealso{
\code{\link{impCoda}}
}
\author{
Matthias Templ
}
\keyword{manip}
\keyword{multivariate}