Fit the regularization path of linear, logistic or Cox models with overlapping grouped covariates based on the latent group lasso approach. Latent group MCP/SCAD as well as bi-level selection methods, namely the group exponential lasso and the composite MCP are also available. This package serves as an extension of R package 'grpreg' (by Dr. Patrick Breheny <email@example.com>) for grouped variable selection involving overlaps between groups.
|Author||Yaohui Zeng, Patrick Breheny|
|Date of publication||2016-12-31 08:47:57|
|Maintainer||Yaohui Zeng <firstname.lastname@example.org>|
cv.grpregOverlap: Cross-validation for choosing regularization parameter lambda
cv.grpsurvOverlap: Cross-validation for choosing regularization parameter lambda...
expandX: Expand design matrix according to grouping information
grpregOverlap: Fit penalized regression models with overlapping grouped...
grpregOverlap-internal: Internal functions
grpregOverlap-package: Penalized regression models with overlapping grouped...
incidenceMatrix: Compute the incidence matrix indicating group memebership
overlapMatrix: Compute a matrix indicating overlaps between groups
pathway.dat: Gene expression and pathway information of p53 cancer cell...
plot.cv.grpregOverlap: Plots the cross-validation curve from cross-validated object
plot.grpregOverlap: Plot object "grpregOverlap"
predict.grpregOverlap: Model predictions based on a fitted object
predict.grpsurvOverlap: Model predictions based on a fitted 'grpsurvOverlap' object.
select.grpregOverlap: Select an value of lambda along a regularization path
summary.cv.grpregOverlap: Summarizing inferences based on cross-validation
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.