PLS Results

Partial Least Squares (PLS)

Click the OK button in the PLS dialog box to display the PLS Results dialog box.

Note: When the PLS analysis is accessed via the Multivariate Statistical Process Control Startup Panel, the PLS Results dialog box will contain five tabs: Quick, Quality, Plots, Advanced, and MD. When the PLS analysis is accessed via the PCA/PLS Startup Panel, the PLS Results dialog box will contain four tabs: Quick, Plots, Advanced, and MD.

Use these options to review the results of the PLS analysis. A wide variety of output in the form of spreadsheets and graphs - including descriptive statistics, predictions, residuals, score, and loading factors - can be generated for the predictor and dependent variables by executing models.

Note: For specific details on next and least significant principal components, R2X and R2Y statistics, and other technical functions mentioned in the option descriptions below, see PCA and PLS Technical Notes.

The area at the top of this dialog box displays the principal components and their properties. Further results can be examined and analyzed using the rest of the options provided in this dialog box.

Element Name Description
Add next Click this button to add the next significant principal component to the PLS model. The maximum number of components that can be added to a model is the "number of X variables - 1".
Remove last Click this button to remove the least significant principal component from the PLS model.
Remove all Click this button to remove all the principal components from the PLS model. By clicking this button, you will effectively delete the model you have built. Nonetheless, you can reconstruct the model via the Add next button or the Auto-fit more components by cross-validation button on the Quick tab.
Summary Click this button to generate a summary spreadsheet for the principal components. This output includes the R2X and R2Y statistics and their cumulative versions, the vector of eigenvalues, number of iterations, and other forms of useful statistics for model interpretation and review.
Cancel Click this button to close the Results dialog box and return to the PLS dialog box.
Options Click the Options button to display the Options menu.
By Group Click the By Group button to display the By Group specification dialog box.
Code generator If your program is licensed to include this feature, you can generate computer code to implement the current model for predicting new observations. When you click this button, a menu is displayed that contains the following commands:
STATISTICA Visual Basic (SVB) Select this command to generate a STATISTICA Visual Basic program containing the code implementing the model. This code will be generated in a form compatible with the nodes of STATISTICA Data Miner; however, you can also simply copy/paste the relevant portion of this code to include it in your custom Visual Basic programs. The code will automatically be displayed in the STATISTICA Visual Basic program editor window.
C/C++ language Select this command to generate code compatible with the C/C++ language. This option is useful if you want to include the information provided by the final model into custom (C/C++) programs (see also Using C/C++/C# Code for Deployment).
PMML script This command will generate code in Predictive Model Markup Language (PMML) which is an XML-based language for storing information about fully trained (parameterized) models and for sharing those models with other applications. STATISTICA and STATISTICA Enterprise Server contain facilities to use this information to compute predicted values or classifications, i.e., to quickly and efficiently deploy models (typically in the context of data mining projects). You can also execute saved models in PMML format using the Deployment analysis part of the NIPALS (PCA/PLS) program.
Deployment to STATISTICA Enterprise Select this command to deploy the results as an Analysis Configuration in STATISTICA Enterprise. Note that appropriately formatted data must be available in a STATISTICA Enterprise Data Configuration before the results can be deployed to an Analysis Configuration.