Compositional Data Analysis

Methods for analysis of compositional data including robust methods (), imputation of missing values (), methods to replace rounded zeros (, , ), count zeros (), methods to deal with essential zeros (), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis () and p-splines (), contingency () and compositional tables (, , ) and (robust) Anderson-Darling normality tests for compositional data as well as popular 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.

{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)

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))