impRZalr.Rd
\name{impRZalr}
\alias{impRZalr}
\title{ alr EM-based Imputation for Rounded Zeros }
\description{
A modified EM alr-algorithm for replacing rounded zeros in
compositional data sets.
}
\usage{
impRZalr(x, pos = ncol(x), dl = rep(0.05, ncol(x) - 1), eps = 1e-04, maxit = 50, bruteforce=FALSE, method="lm", step=FALSE)
}
\arguments{
\item{x}{ Compositional data }
\item{pos}{ Position of the rationing variable for alr transformation }
\item{dl}{ Detection limit for each part }
\item{eps}{ convergence criteria }
\item{maxit}{ maximum number of iterations }
\item{bruteforce}{ if TRUE, imputations over dl are set to dl. If FALSE, truncated (Tobit) regression is applied.  }
\item{method}{ either \dQuote{lm} (default) or \dQuote{MM} }
\item{step}{ if TRUE, a stepwise (AIC) procedure is applied when fitting models }
}
\details{
Statistical analysis of compositional data including zeros runs into problems, because log-ratios cannot be applied.
Usually, rounded zeros are considerer as missing not at random missing values.
The algorithm first applies an additive log-ratio transformation to the compositions. Then the rounded zeros are imputed
using a modified EM algorithm.
}
\value{
\item{xOrig }{Original data frame or matrix}
\item{xImp }{Imputed data}
\item{wind }{Index of the missing values in the data}
\item{iter }{Number of iterations}
\item{eps }{eps}
}
\author{ Matthias Templ and Karel Hron }
\keyword{ multivariate }