The package uses majorization minimization by coordinate descent (MMCD) algorithm to compute the solution surface for concave penalized logistic regression model. The SCAD and MCP (default) are two concave penalties considered in this implementation. For the MCP penalty, the package also provides the local linear approximation by coordinate descant (LLA-CD) and adaptive rescaling algorithms for computing the solutions. The package also provides a Lasso-concave hybrid penalty for fast variable selection. The hybrid penalty applies the concave penalty only to the variables selected by the Lasso. For all the implemented methods, the solution surface is computed along kappa, which is a more smooth fit for the logistic model. Tuning parameter selection method by k-fold cross-validated area under ROC curve (CV-AUC) is implemented as well.
|Author||Dingfeng Jiang <email@example.com>|
|Date of publication||2013-03-18 10:29:02|
|Maintainer||Dingfeng Jiang <firstname.lastname@example.org>|
|License||GPL (>= 2)|
cv.cvplogistic: Tuning parameter selection by k-fold cross validation for...
cv.hybrid: Tuning parameter selection by k-fold cross validation for...
cvplogistic: Majorization minimization by coordinate descent for concave...
hybrid.logistic: A Lasso-concave hybrid penalty for logistic regression
path.plot: Plot the solution path for the concave penalized logistic...
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.