plot.sim

Visualize the covariance matrix, the empirical covariance matrix, the adjacency matrix and the graph pattern of the true graph structure.

Provides implementations of a family of Lasso variants,
including Dantzig Selector, LAD Lasso, SQRT Lasso, and Lq Lasso, for
estimating high-dimensional sparse linear models. We adopt the
alternating direction method of multipliers and convert the original
optimization problem into a sequence of L1-penalized least-squares
minimization problems that are efficiently solved by linearization and
multi-stage screening. In addition to sparse linear model estimation, we
provide extensions of these methods to sparse Gaussian graphical model
estimation, including TIGER and CLIME, using either L1 or adaptive
penalties. Missing values can be tolerated for Dantzig selector and
CLIME. Computation is memory-optimized using sparse matrix output. For
more information, see
<https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.

Tuo Zhao

flare

Family of Lasso Regression

Xingguo Li

Lie Wang

Xiaoming Yuan

Han Liu

plot.sim function

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

Arguments

Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu 
Maintainer: Tuo Zhao &lt;tourzhao@gatech.edu&gt;

Author

Plot Function for "sim" — plot.sim

<dl>

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

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

</dl>

Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu 
Maintainer: Tuo Zhao &lt;tourzhao@gatech.edu&gt;

plot.sim: Plot Function for "sim"

Description

Usage

Arguments

Author

See Also