It proposes a novel variable selection approach taking into account the correlations that may exist between the predictors of the design matrix in a high-dimensional linear model. Our approach consists in rewriting the initial high-dimensional linear model to remove the correlation between the predictors and in applying the generalized Lasso criterion. For further details we refer the reader to the paper <arXiv:2007.10768> (Zhu et al., 2020).

Variable Selection for Highly Correlated Predictors

package-ubuntu-20.04-r-4.0.5#cran#WLasso-1.0

<p>It contains an example of a response variable of a linear model.
</p>

<p>The format is:
num [1:50, 1] 6.027 0.656 3.305 -1.922 -4.943 ...
</p>

package-ubuntu-20.04-r-4.0.5#cran#WLasso-1.0#docs#Y

Y

Description

Usage

Format

Examples

WLasso

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