eclust v0.1.0

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Environment Based Clustering for Interpretable Predictive Models in High Dimensional Data

Companion package to 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>. This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.

This package is under active development

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:

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.

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.

Functions in eclust

 Name Description 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 No Results!