The Functional Data Explorer red triangle menu contains the following options:
Summaries
A submenu of the following options for functional summary statistics:
Plot Mean
Shows or hides a plot of the functional mean in the Summaries report. On by default.
Plot Standard Deviation
Shows or hides a plot of the functional standard deviation in the Summaries report. On by default.
Plot Median
Shows or hides a plot of the functional median in the Summaries report.
Models
A submenu of the following model options:
B-Splines
Fits a basis spline (B-Spline) model to the data. Use the B-Spline model for non-periodic data.
P-Splines
Fits a penalized basis spline (P-Spline) model to the data.
Fourier Basis
Fits a Fourier Basis model to the data. Use the Fourier Basis model for periodic data. A periodic model assumes that the function finishes where it starts. See Fourier Basis Model.
Wavelets
Fits several wavelets models to the data. A Wavelets model is a type of basis function model that is useful for data that contain a lot of peaks. This option requires data to be on an evenly spaced grid. If data is not evenly spaced, a grid is automatically created before the wavelet routine.
When the Wavelets option is selected, several wavelets models are fit from the following families of models: Haar, Daubechies, Symlet, Coiflet, and Biorthogonal. These are all flexible functions with different shapes and types of peaks based on the parameters. The model fit results from these models are shown in a table in the Model Selection report, ordered with the best fitting model at the top. See Wavelets Models.
For more information about wavelets, see Nason (2008).
Note: If there are fewer than three unique input values, neither a Fourier basis model or a P-Spline model can be fit to the data, and a warning message appears.
Model Controls
Shows a submenu that enables you to open the Model Controls panel prior to fitting a model. See Model Controls. This option also enables the Model Selection red triangle menu option to change to the model selection criteria prior to fitting a model.
Direct Functional PCA
(Not available if there is only a single function.) Performs functional principal components analysis directly on the data, without fitting a basis function model first. This reduces computation time, particularly for large data sets. The implementation of Direct Functional PCA is as follows:
1. Align the input data to be between 0 and 1 and interpolate the observations to a common grid of input values.
2. Perform functional principal components analysis on the data.
3. Smooth the first eigenfunction using a P-Spline model with a knot at each grid point.
4. Remove the first smoothed eigenfunction from the data and repeat step 2 to step 4 until a large amount of the variation in the data is explained.
Once you perform a Direct Functional PCA, a Functional PCA report is shown. See Functional PCA.
Save Data
Saves the processed data to a new data table. The processed data are saved in the stacked data format.
See Local Data Filters in JMP Reports, Redo Menus in JMP Reports, and Save Script Menus in JMP Reports in Using JMP for more information about the following options:
Local Data Filter
Shows or hides the local data filter that enables you to filter the data used in a specific report.
Redo
Contains options that enable you to repeat or relaunch the analysis. In platforms that support the feature, the Automatic Recalc option immediately reflects the changes that you make to the data table in the corresponding report window.
Platform Preferences
Contains options that enable you to view the current platform preferences or update the platform preferences to match the settings in the current JMP report.
Save Script
Contains options that enable you to save a script that reproduces the report to several destinations.
Save By-Group Script
Contains options that enable you to save a script that reproduces the platform report for all levels of a By variable to several destinations. Available only when a By variable is specified in the launch window.