We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.
|Author||Li Chen, Jun Chen|
|Date of publication||2015-07-19 09:52:47|
|Maintainer||Li Chen <firstname.lastname@example.org>|
coef.cv.glmgraph: Retrieve coefficients from a fitted "cv.glmgraph" object.
coef.glmgraph: Retrieve coefficients from a fitted "glmgraph" object.
cv.glmgraph: Cross-validation for glmgraph
glmgraph: Fit a GLM with a combination of sparse and smooth...
glmgraph-package: Fit a GLM with a combination of sparse and smooth...
plot.cv.glmgraph: Plot the cross-validation curve produced by cv.glmgraph
plot.glmgraph: Plot coefficients from a "glmgraph" object
predict.cv.glmgraph: make prediction from a fitted "cv.glmgraph" object.
predict.glmgraph: Model predictions based on a fitted "glmgraph" object.
print.cv.glmgraph: print a glmgraph object