plot.cv.sparsenet

Plots the cross-validation curves for each value of <code>gamma</code> in one figure, as a function of the <code>lambda</code> values used.

 lasso

 subset 

Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.

Trevor Hastie

sparsenet

Fit Sparse Linear Regression Models via Nonconvex Optimization

plot.cv.sparsenet function

<dl> <dt>x</dt>
<dd>fitted <code>"cv.sparsenet"</code> object</dd> <dt>...</dt>
<dd>Other graphical parameters to plot</dd></dl>

Arguments

Rahul Mazumder, Jerome Friedman and Trevor Hastie
Maintainer: Trevor Hastie &lt;hastie@stanford.edu&gt;

Author

plot the cross-validation curves produced by cv.sparsenet — plot.cv.sparsenet

<dl>

 <dt>x</dt>
<dd>fitted <code>"cv.sparsenet"</code> object</dd>

 <dt>...</dt>
<dd>Other graphical parameters to plot</dd>

</dl>

plot.cv.sparsenet: plot the cross-validation curves produced by cv.sparsenet

Description

Usage

Arguments

Author

Details

References

See Also

Examples