Methods

K-Means methodology is a commonly used clustering technique. In this analysis the user starts with a collection of samples and attempts to group them into 'k' Number of Clusters based on certain specific distance measurements. More..

PLS Discriminant Analysis (PLS-DA) is performed in order to sharpen the separation between groups of observations, by hopefully rotating PCA (Principal Components Analysis) components such that a maximum separation among More..

PLS Regression is a recent technique that generalizes and combines features from Principal Component Analysis and Multiple Regression. It is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent More..

Principal Component Analysis (PCA) is a projection method that helps you visualize all the information contained in a data table. More..

Classification in PLS is performed, in the SIMCA (Soft Independent Modeling of Class Analogy) approach, in order to identify local models for possible groups and to predict a probable class membership for new observations. More..