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cvplogistic (version 2.1-0)

Majorization Minimization by Coordinate Descent Algorithm for Concave Penalized Logistic Regression for High Dimensional Data

Description

The package uses majorization minimization by coordinate descent (MMCD) algorithm to compute the solution surface for concave penalized logistic regression models. The SCAD and MCP (default) are two concave penalties considered in this implementation. The package provides three types of solutions surfaces, one computed along the regulation parameter kappa (default), the one along the penalty parameter lambda, and the one computed using a hybrid algorithm. The package also provides three tuning parameter selection methods, one based on AIC, one based on BIC and one based on k-fold cross-validated area under ROC curve. Other algorithms such as the adaptive rescaling approach and local linear approximation approach are also provided for the MCP penalty as optional choices.

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Install

install.packages('cvplogistic')

Monthly Downloads

45

Version

2.1-0

License

GPL (>= 2)

Maintainer

Last Published

June 5th, 2012

Functions in cvplogistic (2.1-0)