l_CV

the function realizes K-Fold Cross validation for ridge/Lasso regression
to help to choose the lambda that minimise the RSS

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

l_CV function

<dl><dt>data</dt>
<dd>name of the dataset</dd>
<dt>y</dt>
<dd>name of the dependent variables</dd>
<dt>x</dt>
<dd>name of the independent variable</dd>
<dt>lambda</dt>
<dd>a number or a vector of lambda-value to be evaluated in the regression</dd>
<dt>k</dt>
<dd>integer, which indicates how many training and test set will be splited from the dataset</dd>
<dt>mode</dt>
<dd>1: ridge regression; 2: lasso regression</dd>
<dt>binary</dt>
<dd>logical, if TRUE, the dependent variable is binary</dd>
<dt>step</dt>
<dd>maximum number of steps</dd>
<dt>bound</dt>
<dd>threshold for binary dependent variable</dd>
<dt>fista</dt>
<dd>logical, if TRUE, the FISTA algorithm is used</dd>
<dt>tol</dt>
<dd>tolerance for convergence, it is 10^-7 by default</dd></dl>

Arguments

K-Fold Cross validation for L1/L2 regression — l_CV

<dl>

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


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


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


<dt>lambda</dt>
<dd>a number or a vector of lambda-value to be evaluated in the regression</dd>


<dt>k</dt>
<dd>integer, which indicates how many training and test set will be splited from the dataset</dd>


<dt>mode</dt>
<dd>1: ridge regression; 2: lasso regression</dd>


<dt>binary</dt>
<dd>logical, if TRUE, the dependent variable is binary</dd>


<dt>step</dt>
<dd>maximum number of steps</dd>


<dt>bound</dt>
<dd>threshold for binary dependent variable</dd>


<dt>fista</dt>
<dd>logical, if TRUE, the FISTA algorithm is used</dd>


<dt>tol</dt>
<dd>tolerance for convergence, it is 10^-7 by default</dd>

</dl>

l_CV: K-Fold Cross validation for L1/L2 regression

Description

Usage

Value

Arguments

Examples