An R package for [sparse] Partial least squares discriminant analysis and biplots for compositional data analysis.
This package is the implementation for the method developed in Lee et al. (2014)  for the classification of independently-sampled microbial compositions based on Helminth-infection status of a people in Malaysia. Under an assumption of model sparsity (that is, relatively few microbial populations truly correlate with Helminth-status) we get a factor analysis as well as a classifier.
Currently, this package consists of functions for compositionally-robust data transformations that you can apply prior to (sparse) partial least squares discriminant analysis: here, just wrappers to key functions from the caret package . As described in the paper, I've added parameter selection via cross-validation and bootstrap-based p-value calculation to assess PLS model coefficients (for feature selection). This package also includes some methods for ggplot2-based biplots for PLS-DA output, and for a few other commonly used projection/ordination/classification methods. This code was forked from vqv's biplot package .
This package is still under development and I am currently adding features, based on some new and exciting microbiome data being generated by the good people at the Loke lab  and collaborators. Installation and example instructions will be added soon.
This development package requires the
devtools package for installation. Additionally,
compPLS depends on the
MASS packages. Suggested packages are
boot, (for bootstrapping)
scales (for biplots).
Some minimal examples for running 1) PLS 2) sparse PLS and biplots for the results.
# a too low-dim example, for code demo purposes only data(ArcticLake) # clr transform the data along row margin (1) ALake.clr <- clr(ArcticLake[,1:3], 1) res <- plsDA(ALake.clr, grouping=ArcticLake[,4], K=2) ggbiplot(res, grouping=ArcticLake[,4], group.ellipse=TRUE, label.loadings=TRUE, label.offset=.2, alpha=.6) # alternative biplot biplot(res) ## a higher dim example set.seed(1100) data(Hydrochem) Hchem.clr <- clr(Hydrochem[,6:19], 1) # try without bootstrapping res <- plsDA_main(Hchem.clr, grouping=Hydrochem$River, K=8:10, nboots=0) #, n.core=4) # if on multicore system ggbiplot(res$plsda, grouping=Hydrochem$River, group.ellipse=TRUE, alpha=.5, plot.loadings=FALSE, label.loadings=TRUE) optK <- res$plsda$ncomp # do bootstrapping, warning: this can take a long time res <- plsDA_main(Hchem.clr, grouping=Hydrochem$River, K=optK, nboots=999) #, n.core=4) # if on multicore system