Supervised Kernel-Based Longitudinal PCA (skl-PCA)
Project description
This package implements Supervised Kernel-based Longitudinal Principal Components Analysis (skl-PCA) for predictor dimension reduction in longitudinal models. The software was written by members of the Mindstrong Health Data Science team:
Patrick Staples, PhD
Min Ouyang, PhD
Bob Dougherty, PhD
Greg Ryslik, PhD, FCAS, MAAA
Paul Dagum, MD, PhD
Please contact us at datascience@mindstronghealth.com.
NOTE: If you use this software in your work, please cite the following paper:
Patrick Staples, Min Ouyang, Robert F. Dougherty, Gregory A. Ryslik, and Paul Dagum (2018). Supervised Kernel PCA For Longitudinal Data. http://arxiv.org/abs/1808.06638.
Installation
The easiest way to install the package is via easy_install or pip:
$ pip install sklPCA
This should also take care of the dependencies (numpy, scipy, pandas, and sklearn).
Usage
See examples.py for examples of simulated data, predictor reduction, fitting, and cross-validated model performance.
Copyright & License
Copyright (c) 2018, Mindstrong Health. GNU Affero General Public License.
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