regnet

<p>The dataset containing the X matrix, binary response Y and the true coefficient vector beta</p>

datasets

Network-based regularization has achieved success in variable selections for
high-dimensional biological data, due to its ability to incorporate the correlations
among genomic features. This package provides procedures for fitting network-based
regularization, minimax concave penalty (MCP) and lasso penalty for generalized linear
models. This first version, regnet0.1.1, focuses on binary outcomes. Functions for
continuous, survival outcomes and other regularization methods will be included in the
forthcoming upgraded version.

Jie Ren

Network-Based Regularization for Generalized Linear Models

regnet function

<p>An object of class <code>list</code> of length 3.</p>

regnet: A simulated dataset for testing logistic regression.

Description

Usage

Arguments

Format