obj_function

Returns the elastic net objective function value.

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.

Trevor Hastie

glmnet

Lasso and Elastic-Net Regularized Generalized Linear Models

Jerome Friedman

Rob Tibshirani

Balasubramanian Narasimhan

Kenneth Tay

Noah Simon

Junyang Qian

James Yang

obj_function function

<dl><dt>y</dt>
<dd>Quantitative response variable.</dd>
<dt>mu</dt>
<dd>Model's predictions for <code>y</code>.</dd>
<dt>weights</dt>
<dd>Observation weights.</dd>
<dt>family</dt>
<dd>A description of the error distribution and link function to be
used in the model. This is the result of a call to a family function.</dd>
<dt>lambda</dt>
<dd>A single value for the <code>lambda</code> hyperparameter.</dd>
<dt>alpha</dt>
<dd>The elasticnet mixing parameter, with \(0 \le \alpha \le 1\).</dd>
<dt>coefficients</dt>
<dd>The model's coefficients (excluding intercept).</dd>
<dt>vp</dt>
<dd>Penalty factors for each of the coefficients.</dd></dl>

Arguments

Elastic net objective function value — obj_function

<dl>

<dt>y</dt>
<dd>Quantitative response variable.</dd>


<dt>mu</dt>
<dd>Model's predictions for <code>y</code>.</dd>


<dt>weights</dt>
<dd>Observation weights.</dd>


<dt>family</dt>
<dd>A description of the error distribution and link function to be
used in the model. This is the result of a call to a family function.</dd>


<dt>lambda</dt>
<dd>A single value for the <code>lambda</code> hyperparameter.</dd>


<dt>alpha</dt>
<dd>The elasticnet mixing parameter, with \(0 \le \alpha \le 1\).</dd>


<dt>coefficients</dt>
<dd>The model's coefficients (excluding intercept).</dd>


<dt>vp</dt>
<dd>Penalty factors for each of the coefficients.</dd>

</dl>

obj_function: Elastic net objective function value

Description

Usage

Arguments