Compositional Data Analysis.
{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))