Sampling points from curved data typically is not the most accurate way to create a predictive model. In many cases the sampled points miss variability that could impact outcome. This demo uses a case study to show how to use JMP functional Data Explorer to address this challenge.
The Case Study: Use spectra to determine factors that will predict the octane rating of gasoline.
The Main Steps:
- Y is the absorbance, X is the wavelength, ID Function is the sample associated with each spectra, and use Z supplementary role for response you want to predict.
- Interactively use Cleanup, Transform, and Align from FDE interface to focus on the region of interest in the spectra before building the model.
- Build the basis model to define the shape of the spectra.
- Interpret accuracy of model by examining graphs and looking at the Model Selection criteria.
- Interact with Functional Principal Component Profiler to see how spectra change as Principal components change. Use Functional DOE Profiler to see how the spectra look for different response levels.
- Extract the Functional Principal Components Analysis into a separate table from the Functional Summaries report; use the new table to build a predictive model for your response.
Questions answered by Jason Wiggins @Jason_Wiggins, @Peter_Hersh and @jiancao after the demo:
