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grpregOverlap

grpregOverlap fits the regularization paths of linear, logistic, Poisson, or Cox models with overlapping grouped covariates based on the latent group lasso approach (Jacob et al., 2009). Latent group MCP/SCAD as well as bi-level selection methods, namely the group exponential lasso(Breheny, 2015) and the composite MCP (Huang et al., 2012) are also available. This package serves as an extension of R package grpreg (by Dr. Patrick Breheny patrick-breheny@uiowa.edu) for grouped variable selection involving overlaps between groups.

News:

  • this package now works for survival analysis (Cox model) by specifying "family = cox".
  • this package on GitHub has been updated to Version 2.2-0. See details in NEWS.

Installation:

  • the stable version: install.packages("grpregOverlap")
  • the latest version: devtools::install_github("YaohuiZeng/grpregOverlap")

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Install

install.packages('grpregOverlap')

Monthly Downloads

44

Version

2.2-0

License

GPL-3

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Last Published

December 31st, 2016

Functions in grpregOverlap (2.2-0)

grpregOverlap-internal

Internal functions
grpregOverlap-package

Penalized regression models with overlapping grouped variables.
grpregOverlap

Fit penalized regression models with overlapping grouped variables
pathway.dat

Gene expression and pathway information of p53 cancer cell lines
plot.cv.grpregOverlap

Plots the cross-validation curve from cross-validated object
cv.grpregOverlap

Cross-validation for choosing regularization parameter lambda
expandX

Expand design matrix according to grouping information
cv.grpsurvOverlap

Cross-validation for choosing regularization parameter lambda for Cox models.
overlapMatrix

Compute a matrix indicating overlaps between groups
incidenceMatrix

Compute the incidence matrix indicating group memebership
plot.grpregOverlap

Plot object "grpregOverlap"
predict.grpregOverlap

Model predictions based on a fitted object
predict.grpsurvOverlap

Model predictions based on a fitted grpsurvOverlap object.
summary.cv.grpregOverlap

Summarizing inferences based on cross-validation
select.grpregOverlap

Select an value of lambda along a regularization path