Robust Methods for Compositional Data

```
using robCompositions
```

data(expenditures)

p1 <- pcaCoDa(expenditures)

plot(p1)

- 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.

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

The package has dependencies on

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

Installion of `robCompositions`

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

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

data(expenditures)

expenditures[1,3]

expenditures[1,3] <- NA

impKNNa(expenditures)$xImp[1,3]

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)

data(expenditures)

p1 <- pcaCoDa(expenditures)

p1

plot(p1)

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))

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