Output | Expression | 3D PCA Plot (Correlation and Principal Components)

The 3D PCA Plot tab contains the following elements:Principal components can reveal key structure in a data set and which columns are similar, different, or outliers. They are the directions of maximal variability after adjusting for all previous components. This plot is a three-dimensional scatterplot of principal components computed on the input data. Each point represents a column in the input data set. You can spin the plot by clicking and dragging your mouse. Shift-click-throw to set it spinning automatically. You can change which principal components are plotted using the variable choosers at the bottom of the graph. The percentage of variability attributable to each principal component is included in parentheses after its name.See Principal Components Analysis Plot for more details.

• A Create Subset Experimental Design Data Set drill down optionNote: You can select multiple points in 3D PCA plots in one of two ways. Either, hold down the key while you select points (the points will highlight once you have released the key) or double-click and draw a box around the selected points (You must continue to press the left-mouse button after the second click until all of the points are selected.).Note:: To choose the design variable by which to color the points, you must select the 2D PCA Plots tab from the Tabs pane and follow the directions as described in the Tip in 2D PCA Plots (Correlation and Principal Components). Changes made to the 2D PCA Plots tab are carried over to the 3D PCA Plot tab.