AICc

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', <arXiv:1308.5623>.

Matt Taddy

gamlr

Gamma Lasso Regression

AICc function

<dl> <dt>object</dt>
<dd>Some model object that you can call <code>logLik</code> on (such as a <code>gamlr</code> or <code>glm</code> fit).</dd><dt>k</dt>
<dd>The usual <code>AIC</code> complexity penalty. <code>k</code> defaults to 2.</dd></dl>

Arguments

Matt Taddy <a href="/link/mataddy%40gmail.com?package=gamlr&version=1.13-8" data-mini-rdoc="gamlr::mataddy@gmail.com">mataddy@gmail.com</a>

Author

Corrected AIC — AICc

<dl>

 <dt>object</dt>
<dd>Some model object that you can call <code>logLik</code> on (such as a <code>gamlr</code> or <code>glm</code> fit).</dd>

<dt>k</dt>
<dd>The usual <code>AIC</code> complexity penalty. <code>k</code> defaults to 2.</dd>

</dl>

Matt Taddy <a href='mailto:mataddy@gmail.com'>mataddy@gmail.com</a>

AICc: Corrected AIC

Description

Usage

Value

Arguments

Author

Details

References

See Also