# {robCompositions}

Robust Methods for Compositional Data

`using robCompositions`

data(expenditures)

p1 <- pcaCoDa(expenditures)

plot(p1)

## What is it?

- Imputation of compositional data including robust methods, methods
to impute rounded zeros
- Outlier detection for compositional data using robust methods
- Principal component analysis for compositional data using robust
methods
- Factor analysis for compositional data using robust methods
- Discriminant analysis for compositional data (Fisher rule) using
robust methods
- Robust regression with compositional predictors
- Anderson-Darling normality tests for compositional data
- log-ratio transformations (addLR, cenLR, isomLR, and their inverse
transformations).
- In addition, visualisation and diagnostic tools are implemented as
well as high and low-level plot functions for the ternary diagram.

## Goals

- never use classical statistical methods on raw compositional data
again.

## Getting Started

### Dependencies

The package has dependencies on

`R (>= 2.10), utils, robustbase, rrcov, car (>= 2.0-0), MASS, pls`

### Installation

Installion of `robCompositions`

is really easy for
registered users (when the R-tools are installed). Just use

```
library(devtools)
install_github("robCompositions", "matthias-da")
```

## 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)

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)

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 <- isomLRinv(mvrnorm(100, mu=c(0,2), Sigma=Sigma))