Orthogonal Partial Least Squares (OPLS) in R

July 28, 2013
By

(This article was first published on imDEV » r-bloggers, and kindly contributed to R-bloggers)

Clipboard04

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!


To leave a comment for the author, please follow the link and comment on his blog: imDEV » r-bloggers.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.