Orthogonal Partial Least Squares (OPLS) in R

July 28, 2013
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I often need to analyze and model very wide data (variables >>>samples), and because of this I gravitate to robust yet relatively simple methods. In my opinion partial least squares (PLS) is a particular useful algorithm. Simply put, PLS is an extension of principal components analysis (PCA), a non-supervised  method to maximizing  variance explained in X, which instead maximizes the covariance between X and Y(s). Orthogonal partial least squares (OPLS) is a variant of PLS which uses orthogonal signal correction to maximize the explained covariance between X and Y on the first latent variable, and components >1 capture variance in X which is orthogonal (or unrelated) to Y.

Because R does not have a simple interface for OPLS, I am in the process of writing a package, which depends on the existing package pls.

Today I wanted to make a small example of conducting OPLS in R, and  at the same time take a moment to try out the R package knitr and RStudio for markdown generation.

You can take a look at the markdown document or the raw R code.

I was extremely impressed with ease of using knitr and generating markdown from code using RStudio. A big thank you to Yihui Xie and the RStudio developers (Joe Cheng). This is an amazing capability which I will make much more use of in the future!

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