SVEMnet-package

SVEMnet: Self-Validated Ensemble Models with Elastic Net Regression

The SVEMnet package implements Self-Validated Ensemble Models (SVEM) using Elastic Net (including lasso and ridge) regression via glmnet. SVEM averages predictions from multiple models fitted to fractionally weighted bootstraps of the data, tuned with anti-correlated validation weights. package

Functions

• SVEMnet: Fit an SVEMnet model using Elastic Net regression.
• svem_significance_test: Perform a whole-model significance test for SVEM models.
• svem_significance_test_parallel: Perform a whole-model significance test for SVEM models. Parallelized version.
• predict.svem_model: Predict method for SVEM models.
• plot.svem_model: Plot method for SVEM models.
• coef.svem_model: Plot method for SVEM models.
• glmnet_with_cv: Wrapper for cv.glmnet

Acknowledgments

Development of this package was assisted by GPT o1-preview, which helped in constructing the structure of some of the code and the roxygen documentation. The code for the significance test is taken from the supplementary material of Karl (2024) (it was handwritten by that author).

References

Gotwalt, C., & Ramsey, P. (2018). Model Validation Strategies for Designed Experiments Using Bootstrapping Techniques With Applications to Biopharmaceuticals. JMP Discovery Conference. https://community.jmp.com/t5/Discovery-Summit-2018/Model-Validation-Strategies-for-Designed-Experiments-Using/ta-p/73730

Karl, A. T. (2024). A randomized permutation whole-model test heuristic for Self-Validated Ensemble Models (SVEM). Chemometrics and Intelligent Laboratory Systems, 249, 105122. tools:::Rd_expr_doi("10.1016/j.chemolab.2024.105122")

Karl, A., Wisnowski, J., & Rushing, H. (2022). JMP Pro 17 Remedies for Practical Struggles with Mixture Experiments. JMP Discovery Conference. tools:::Rd_expr_doi("10.13140/RG.2.2.34598.40003/1")

Lemkus, T., Gotwalt, C., Ramsey, P., & Weese, M. L. (2021). Self-Validated Ensemble Models for Design of Experiments. Chemometrics and Intelligent Laboratory Systems, 219, 104439. tools:::Rd_expr_doi("10.1016/j.chemolab.2021.104439")

Xu, L., Gotwalt, C., Hong, Y., King, C. B., & Meeker, W. Q. (2020). Applications of the Fractional-Random-Weight Bootstrap. The American Statistician, 74(4), 345–358. tools:::Rd_expr_doi("10.1080/00031305.2020.1731599")

Ramsey, P., Gaudard, M., & Levin, W. (2021). Accelerating Innovation with Space Filling Mixture Designs, Neural Networks and SVEM. JMP Discovery Conference. https://community.jmp.com/t5/Abstracts/Accelerating-Innovation-with-Space-Filling-Mixture-Designs/ev-p/756841

Ramsey, P., & Gotwalt, C. (2018). Model Validation Strategies for Designed Experiments Using Bootstrapping Techniques With Applications to Biopharmaceuticals. JMP Discovery Conference - Europe. https://community.jmp.com/t5/Discovery-Summit-Europe-2018/Model-Validation-Strategies-for-Designed-Experiments-Using/ta-p/51286

Ramsey, P., Levin, W., Lemkus, T., & Gotwalt, C. (2021). SVEM: A Paradigm Shift in Design and Analysis of Experiments. JMP Discovery Conference - Europe. https://community.jmp.com/t5/Abstracts/SVEM-A-Paradigm-Shift-in-Design-and-Analysis-of-Experiments-2021/ev-p/756634

Ramsey, P., & McNeill, P. (2023). CMC, SVEM, Neural Networks, DOE, and Complexity: It’s All About Prediction. JMP Discovery Conference.

Author(s)

Maintainer : Andrew T. Karl akarl@asu.edu (ORCID)