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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 (2017+) Preprint. 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

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.

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Version

Install

install.packages('eclust')

Monthly Downloads

275

Version

0.1.0

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Sahir Bhatnagar

Last Published

January 26th, 2017

Functions in eclust (0.1.0)

plot.similarity

Function to generate heatmap
s_mars_separate

Fit Multivariate Adaptive Regression Splines on Simulated Data
s_pen_clust

Fit Penalized Regression Models on Simulated Cluster Summaries
s_response

Generate True Response vector for Linear Simulation
s_response_mars

Generate True Response vector for Non-Linear Simulation
s_modules

Simulate Covariates With Exposure Dependent Correlations
plot.eclust

Plot Heatmap of Cluster Summaries by Exposure Status
s_mars_clust

Fit MARS Models on Simulated Cluster Summaries
simdata

Simulated Data with Environment Dependent Correlations
s_pen_separate

Fit Penalized Regression Models on Simulated Data
u_extract_selected_earth

Get selected terms from an earth object
u_fisherZ

Calculate Fisher's Z Transformation for Correlations