eclust package implements the methods developped in the paper:
An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) DOI:10.1101/102475.
eclust is a two-step procedure: 1a) a clustering stage where variables are clustered based on some measure of similarity, 1b) a dimension reduction stage where a summary measure is created for each of the clusters, and 2) a simultaneous variable selection and regression stage on the summarized cluster measures.
You can install the development version of
eclust from GitHub with:
See the online vignette for example usage of the functions.
This package is makes use of several existing packages including:
glmnetfor lasso and elasticnet regression
earthfor MARS models
WGCNAfor topological overlap matrices
- Park, M. Y., Hastie, T., & Tibshirani, R. (2007). Averaged gene expressions for regression. Biostatistics, 8(2), 212-227.
- Bühlmann, P., Rütimann, P., van de Geer, S., & Zhang, C. H. (2013). Correlated variables in regression: clustering and sparse estimation. Journal of Statistical Planning and Inference, 143(11), 1835-1858.
- Issues: https://github.com/sahirbhatnagar/eclust/issues
- Pull Requests: https://github.com/sahirbhatnagar/eclust/
- e-mail: email@example.com
You can see the most recent changes to the package in the NEWS.md file
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.