The following options are available in the Functional PCA red triangle menu:
Diagnostic Plots
Shows or hides the Actual by Predicted and the Residual by Predicted plots. Use these plots help assess how well the model fits the data, given the selected number of functional principal components.
Score Plot
Shows or hides a score plot of the FPC scores. Use the lists under Select Component to specify which FPCs are plotted on each axis of the Score Plot. If there is only one FPC, the FPC scores are plotted on the line y = x and the lists to change the components are not shown. Score plots are useful for detecting outliers. In the case of FPC scores, the Score Plot is useful for detecting levels of the ID variable that have outlier functions. If you select a point in the score plot, the FPC Profiler is set to the scores for that function.
Tip: Hover over a point in the score plot to view a prediction plot of the fitted curve for that level of the ID variable.
FPC Profiler
Shows or hides a profiler of the FPC scores. The FPC Profiler includes a column for the input variable and a column for each FPC score. If a target function is specified, there are additional button options above the profiler graphs. You can optimize the target function and show or hide the target profilers. If you select Show Target Profilers, two additional profilers are added to the report. One measures the difference from the target function, and the other measures the integrated error from the target function. For more information about FPC Profiler red triangle menu options, see “Profiler” in Profilers.
Tip: Use the Reset button to reset all of the FPC scores to 0 in the profiler.
Customize Number of FPCs
Specifies the number of FPC scores to show in the Functional PCA. Specifying the number of FPC scores in this option also updates the Function Summaries report. To view the mean model, set the number of FPCs to 0.