plot.logit

Plot coefficient paths across regularization parameters.

Fast tools for fitting sparse generalized linear models with convex
penalties (lasso) and concave penalties (smoothly clipped absolute
deviation and minimax concave penalty). Computation uses multi-stage convex
relaxation and pathwise coordinate optimization with warm starts,
active-set updates, and screening rules. Core solvers are implemented in
C++, and coefficient paths are stored as sparse matrices for memory
efficiency.

Tuo Zhao

picasso

Sparse Learning with Convex and Concave Penalties

Jason Ge

Xingguo Li

Haoming Jiang

Mengdi Wang

Tong Zhang

Han Liu

Gael Guennebaud

Benoit Jacob

Eigen Library Authors

plot.logit function

<dl> <dt>x</dt>
<dd>An object with S3 class <code>"logit"</code>.</dd> <dt>...</dt>
<dd>Arguments to be passed to methods.</dd></dl>

Arguments

Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao 
Maintainer: Tuo Zhao &lt;tourzhao@gatech.edu&gt;

Author

Plot Method for an Object with S3 Class "logit" — plot.logit

<dl>

 <dt>x</dt>
<dd>An object with S3 class <code>"logit"</code>.</dd>

 <dt>...</dt>
<dd>Arguments to be passed to methods.</dd>

</dl>

Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao 
Maintainer: Tuo Zhao &lt;tourzhao@gatech.edu&gt;

plot.logit: Plot Method for an Object with S3 Class `"logit"`

Description

Usage

Value

Arguments

Author

See Also