README.md

In sahirbhatnagar/eclust: Environment Based Clustering for Interpretable Predictive Models in High Dimensional Data

eclust

The 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.

Breifly, 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.

Installation

You can install the development version of eclust from GitHub with:

install.packages("pacman")
pacman::p_install_gh("sahirbhatnagar/eclust")

Vignette

See the online vignette for example usage of the functions.

Credit

This package is makes use of several existing packages including:

• glmnet for lasso and elasticnet regression
• earth for MARS models
• WGCNA for topological overlap matrices

Related Work

1. Park, M. Y., Hastie, T., & Tibshirani, R. (2007). Averaged gene expressions for regression. Biostatistics, 8(2), 212-227.
2. 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.

Contact

• Issues: https://github.com/sahirbhatnagar/eclust/issues
• Pull Requests: https://github.com/sahirbhatnagar/eclust/
• e-mail: [e-mail]

Latest news

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.

sahirbhatnagar/eclust documentation built on May 29, 2019, 12:58 p.m.