Discovering Partial Least Squares with JMP®
Ian Cox, PhD, Solutions Manager for JMP Sales and Marketing – SAS
Marie Gaudard, PhD, Consultant – North Haven Group
This talk provides an introduction to partial least squares (PLS), an approach that can be used to find relationships between sets of variables, typically a set of predictors and a set of responses. PLS exploits the correlations within and between the sets of variables to reveal underlying or latent structures and, unlike regression, PLS can be used when the predictors outnumber the observations. Although a favorite with chemometricians, PLS is a versatile technique that can find application in many areas. We illustrate PLS using an example from computational chemistry (Umetrics, 1995). In this example, the goal is to measure the biological activity of a compound indirectly, using relatively inexpensive chemical measurements. The data set contains 33 observations and 15 predictors.
