% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/robCompositions-package.R
\docType{package}
\name{robCompositions-package}
\alias{robCompositions-package}
\alias{robCompositions}
\title{Robust Estimation for Compositional Data.}
\description{
The package contains methods for imputation of compositional data including
robust methods, (robust) outlier detection for compositional data, (robust)
principal component analysis for compositional data, (robust) factor
analysis for compositional data, (robust) discriminant analysis (Fisher
rule) and (robust) Anderson-Darling normality tests for compositional data
as well as popular log-ratio transformations (alr, clr, ilr, and their
inverse transformations).
}
\details{
\tabular{ll}{ Package: \tab robCompositions\cr Type: \tab Package\cr
Version: \tab 1.3.3\cr Date: \tab 2009-11-28\cr License: \tab GPL 2\cr
LazyLoad: \tab yes\cr }
}
\examples{

## k nearest neighbor imputation
data(expenditures)
expenditures[1,3]
expenditures[1,3] <- NA
impKNNa(expenditures)$xImp[1,3]

## iterative model based imputation
data(expenditures)
x <- expenditures
x[1,3]
x[1,3] <- NA
xi <- impCoda(x)$xImp
xi[1,3]
s1 <- sum(x[1,-3])
impS <- sum(xi[1,-3])
xi[,3] * s1/impS

xi <- impKNNa(expenditures)
xi
summary(xi)
\dontrun{plot(xi, which=1)}
plot(xi, which=2)
plot(xi, which=3)

## pca
data(expenditures)
p1 <- pcaCoDa(expenditures)
p1
plot(p1)

## outlier detection
data(expenditures)
oD <- outCoDa(expenditures)
oD
plot(oD)

## transformations
data(arcticLake)
x <- arcticLake
x.alr <- addLR(x, 2)
y <- addLRinv(x.alr)
addLRinv(addLR(x, 3))
data(expenditures)
x <- expenditures
y <- addLRinv(addLR(x, 5))
head(x)
head(y)
addLRinv(x.alr, ivar=2, useClassInfo=FALSE)

data(expenditures)
eclr <- cenLR(expenditures)
inveclr <- cenLRinv(eclr)
head(expenditures)
head(inveclr)
head(cenLRinv(eclr$x.clr))

require(MASS)
Sigma <- matrix(c(5.05,4.95,4.95,5.05), ncol=2, byrow=TRUE)
z <- pivotCoordInv(mvrnorm(100, mu=c(0,2), Sigma=Sigma))

}
\references{
Aitchison, J. (1986) \emph{The Statistical Analysis of
Compositional Data} Monographs on Statistics and Applied Probability.
Chapman \& Hall Ltd., London (UK). 416p. \

Filzmoser, P., and Hron, K. (2008) Outlier detection for compositional data
using robust methods. \emph{Math. Geosciences}, \bold{40} 233-248.

Filzmoser, P., Hron, K., Reimann, C. (2009) Principal Component Analysis for
Compositional Data with Outliers. \emph{Environmetrics}, \bold{20} (6),
621--632.

P. Filzmoser, K. Hron, C. Reimann, R. Garrett (2009): Robust Factor Analysis
for Compositional Data.  \emph{Computers and Geosciences}, \bold{35} (9),
1854--1861.

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}, \bold{54} (12),
3095--3107.

C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter (2008): Statistical
Data Analysis Explained.  \emph{Applied Environmental Statistics with R}.
John Wiley and Sons, Chichester, 2008.
}
\author{
Matthias Templ, Peter Filzmoser, Karel Hron,

Maintainer: Matthias Templ <templ@tuwien.ac.at>
}
\keyword{package}