When you want to get to know and love your data

Orthogonal Signal Correction Partial Least Squares (O-PLS) in R


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 signal correction partial least squares (O-PLS) 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 O-PLS, 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 O-PLS 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 O-PLS/O-PLS-DA tutorials.

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!

2 responses

  1. Albert

    The link to the raw R code seems to be down (or any of the links actually). Is there a chance to re-up it?

    July 22, 2015 at 2:50 pm

  2. Thanks for the heads up. I added a link to my TeachingDemos (https://github.com/dgrapov/TeachingDemos/) site which is current. Note you will need to download and source my devium (https://github.com/dgrapov/devium) project R script to enable the various functions. One day, when I have more time I will finally make it a package :)

    July 25, 2015 at 8:08 pm

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