ridge

ridge function estimates the coefficients for a linear model using Ridge regression.

Implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.

YingHong Chen

ProxReg

Linear Models for Prediction and Classification using Proximal
Operators

Ferran Reverter Comes

Esteban Vegas Lozano

ridge function

<dl><dt>data</dt>
<dd>name of the dataset</dd>
<dt>y</dt>
<dd>name of dependent variables</dd>
<dt>x</dt>
<dd>name of independent variable</dd>
<dt>lambda</dt>
<dd>a numeric value or a numeric vector to penalize the squared residual</dd></dl>

Arguments

Ridge regression — ridge

<dl>

<dt>data</dt>
<dd>name of the dataset</dd>


<dt>y</dt>
<dd>name of dependent variables</dd>


<dt>x</dt>
<dd>name of independent variable</dd>


<dt>lambda</dt>
<dd>a numeric value or a numeric vector to penalize the squared residual</dd>

</dl>

ridge: Ridge regression

Description

Usage

Value

Arguments

Examples