DataLab is a compact statistics package aiming at exploratory data analysis. Please visit the DataLab Web site for more information.... 
Home Features of DataLab Mathematical/Statistical Analysis Classification & Clustering PLS Discriminant Analysis  
See also: Create an LDA Classifier, Create a kNN Model


PLS Discriminant Analysis
In order to create a classifier using PLS Discriminant Analysis (PLSDA) one has to specify both the independent and the dependent variables by clicking the corresponding fields at the top left and the top right, respectively. The dependent variables have to be dichotomous. Further, one can select one of two scaling options: "mean centering" and "standardization" with the first method being the standard PLS approach. After selecting the variables the classifier can be calculated by clicking the "Calculate" button. The PLS algorithm works on a number of predefined factors (default is currently 20) which, of course, also depends on the dimension of the data matrix. Thus the number of actual factors may decrease in some cases.
After the successful calculation of the PLSDA classifier it can be stored on disk (button "Save Model") for its later application to new data. Further, the following information is available on different tabs:
Classification resultsThe classification results are presented in form of confusion matrices which show the false positives and false negatives in orange color, the true positives in green, and the true negatives in gray. Each field of a confusion matrix contains the counts of the objects falling into the particular category. The optimum decision threshold is calculated from the ROC curve at the lower right. In order to switch between the ROC curves of the target variables you can either select one of the target variables at the top right list of variables, or doubleclick the corresponding confusion matrix.
