Rotating 3-Dimensional Scatter Plot Enhances Visualization
Scatter plots are often used to present information about a data set, presenting each sample on a 2-dimensional graphic with one variable on the x-axis and another on the y-axis. When a data set has more than 2 variables, the information from the 3rd variable is not visible in a 2-dimensional plot. A 3-dimensional scatter plot can offer more information in a single view and is the default plot format for most of Pirouette’s data viewing. But, a static 3-dimensional view is only part of the picture. Free rotation of the 3-D view will reveal relationships among samples or variables not visible in a static 2-D plot. And, if there are more than 3 variables, Pirouette makes it easy to swap which variables get plotted.

Dynamic Linking Highlights Samples Across Views
Visualization is a key component in multivariate analysis, and Pirouette makes this easy. In particular, with large data sets, it is critical to know how samples appear in relation to others. A simple way to do this is by “highlighting” a sample to make it stand out (also applicable to groups of samples). In Pirouette, we have taken this to the next step that those in dynamic graphics call linking. A highlighted sample will appear highlighted in all sample-oriented views, whether presented as a table or a graphic. Combined with Cloaking, this is a powerful tool for investigating relationships in your data.

Cloaking Allows Focus on Selected Samples/Variables
As powerful as dynamic linking is, there are times when there is so much data, that it is hard to see where highlighted samples appear in a sea of samples. Cloaking comes to the rescue. The cloaking button is a 3-way toggle: hit it once to temporarily hide all highlighted samples; hit it a second time to show only the highlighted samples; and, a third time to once again show all samples, highlighted or not. Cloaking can be used on scatter plots or line plots, on plots that are sample-oriented or those that are variable-oriented. Combined with dynamic linking, you can make your selections in one view and see which samples appear in the cloaked view.

Decision Diagrams Facilitate Understanding
Most Pirouette algorithms offer outlier diagnostics to help you optimize the training set and corresponding model. You can view these diagnostics as a table or graphically. Plotting one diagnostic against another allows quick evaluation of outliers by comparing a sample point to the thresholds for each metric. SIMCA also presents the Class Distances as a decision diagram in which the distances of the samples to a class model are shown for 2 classes at a time. In each of these cases, the multiple thresholds divide the plot space into subregions of membership and not.