{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)

summary(xi)

plot(xi, which=1)

plot(xi, which=2)

plot(xi, which=3)

pca

data(expenditures)

p1 <- pcaCoDa(expenditures)

plot(p1)

outlier detection

data(expenditures)

oD <- outCoDa(expenditures)

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

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