PLS Results - Quick Tab

Partial Least Squares (PLS)

Select the Quick tab of the PLS Results dialog box to access the options described here.

Note: For specific details on R2X and R2Y, SPE(Q), and other technical functions mentioned in the option descriptions below, see PCA and PLS Technical Notes.
Element Name Description
Summary Click this button to generate a summary spreadsheet for the principal components. This output includes the R2X and R2Y, the vector of eigenvalues, number of iterations, and other forms of useful statistics for model interpretation and review.
Summary overview Click this button to generate a histogram of cumulative R2Y and SPE(Q).
Variable importance Click this button to display a spreadsheet of variable importance. The measure of importance of a variable is given by its modeling power. A variable with a modeling power equal to one is completely relevant for building the PLS model. Variables with modeling power close to "number of components" divided by "number of variables" are regarded to be less significant.
Note: variable importance is computed as Power for PCA models (proportion of variability accounted for in the X variables) and VIP (Variable Importance in the Projection; similar to power but further weighted by the variability accounted for by the components) in PLS models. Refer to the Technical Notes topic for details.
Variable importance Click this button to create a histogram of variable importance.
Sort variables by importance Select this check box to sort variables by their importance in the Variable importance spreadsheet and histogram (see above).
Control charts The following options are available in the Control charts group box:
T2 Chart Click this button to create a Hotelling T2 chart for the individual observations in the data set together with the line plot of the control limit.
Click this button to produce a spreadsheet of the Hotelling T2 for the individual observations in the data set. Data cases with values of Hotelling T2 exceeding the control limit will be displayed in red.
SPE(Q) Chart Click this button to create an SPE(Q) (squares prediction error SPE) histogram together with the line plot of the control limit. SPE represents noise (unstructured variation) in the data that cannot be captured by the model (since it does not follow a particular pattern).
Click this button to produce an SPE(Q) (squares prediction error SPE) spreadsheet. SPE represents noise (unstructured variation) in the data that cannot be captured by the model (since it does not follow a particular pattern). Data cases with values of SPE exceeding the control limit will be displayed in red.
Control limit Enter in this box the confidence limit for defining the upper and lower bounds of the control charts.
Warning limit Select this check box and enter in the adjacent box the warning limit.
Auto-fit more components by cross-validation You can use this option after adding or deleting one or more component from the PLS model. This action, which is equivalent to clicking the OK button in the PLS dialog box, invokes the v-fold cross-validation method to rebuild the model after you have manually added or removed one or more component. This option is available only if cross-validation was used to fit the model.