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Instruction 8

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PLS-EDA (Enhanced Discriminant Analysis using PLS)
PLS-EDA (PLS Enhanced Discriminant Analysis) was developed to separate categories more clearly than PLS-DA, and to extract key variables which contribute separation of categories. PLS-EDA is developed based on O-PLS-DA algorithm. In this chapter, let's see the differences between PLS-EDA and PLS-DA.

To test PLS-EDA, go back to "Appliance" sheet and click gMultibase_2015h and gOpen Formh. When Preparation dialog box appears, select gPLS-EDAh and click gDoneh button.
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After "PLS-EDA" dialog box opens, click "Done" without any changes.



New worksheet "Multibase_PLSEDA" and "Multibase_Result" will be generated and loading and score plot with PC1, PC2 and PC3 will be displayed on Multibase_PLSEDA sheet as below.



You can see that the separation between groups become much clearer than PLS-DA. You can see "facsimile" contributes to the separation of Pennsylvania (red) and the other states.

Axis Rotation for Factor Matching
Understanding of underlying factors given by PCA, PLS-DA and PLS-EDA are sometimes difficult. In this case, rotating the axes helps you interpret factor meanings better. In this chapter, let's understand the effect of gAxis Rotation for Factor Matchingh.

Go back to "Multibase_Preparation" and click gMultibase_2015h and gOpen Formh in Add-ins menu. When Calculation dialog box appears, check gAxis Rotation for Factor Matchingh and click gDoneh button.



New worksheet "Multibase_PLSEDA" and "Multibase_Result" will be generated and the loading and the score plots with PC1, PC2 and PC3 will be displayed on Multibase_PLSEDA sheet as shown below.



gAxis Rotation for Factor Matchingh maximizes variable distribution on (or around) axes. In the case of Florida (Blue), Car Speakers, SD Cards, and Stereos & Components are distributed on left-hand side of PC1, and it means that those appliances are sold well in Florida.

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