[PCA-scores...][PCA-loadings...][PLS-loadings...][PLS-partial weights...]
The commands in this submenu start a 3D plot application. Follow this link for a full description of its options and commands.
Notice that this is a "tear-off menu", if you click on the dotted line on top of the menu you will transform the submenu in an independent window which will remain open until you specifically close it. This allows a fast access to the plots and is particularly useful when you plan to open many plots at the same time.
Plot>>>3D plot>>>PCA-scores...
This plot represents objects in a three dimensional space using the score vectors obtained from the PCA model.
First, the User is prompted to choose the component which will be represented in the X, Y and Z axes.
When the dialog window shows the desired settings press the OK button and the 3D-plot will be displayed. Press the Cancel button to abort the command.
If the PCA model has ever been used to produce external predictions using the command Modeling>>>External PCA pred., the dialog will show a control asking if we want to show the result of the predictions in the plot. If this control is selected, the plot will contains some additional points representing the objects in the external set, colored in red, at the positions corresponding to their predicted scores.
Plot>>>3D plot>>>PCA-loadings...
Plot>>>3D plot>>>PLS-loadings...
Plot>>>3D plot>>>PLS-partial weights...
First, the User is prompted to choose the component which will be represented in the X, Y and Z axis.
It also possible to highlight single variable blocks (correlograms) by plotting in red the symbol for the variables of this block. Click on the Mark block controls to select the correlograms to highlight. The number which appear by the side of the check boxes corresponds to the id. of the correlogram.
When the dialog window shows the desired settings press the OK button and the 3D plot will be displayed. Press the Cancel button to abort the command.
All these commands plot variables in a three-dimensional space, using the PCA-loadings, PLS-loadings or PLS-partial weights vectors obtained from PCA or PLS models.