plot.sim

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

Provides a general framework for
high-dimensional undirected graph estimation. It integrates
data preprocessing, neighborhood screening, graph estimation,
and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is
applied to help relax the normality assumption. In the graph
estimation stage, the graph structure is estimated by
Meinshausen-Buhlmann graph estimation or the graphical lasso,
and both methods can be further accelerated by the lossy
screening rule preselecting the neighborhood of each variable
by correlation thresholding. We target on high-dimensional data
analysis usually d >> n, and the computation is
memory-optimized using the sparse matrix output. We also
provide a computationally efficient approach, correlation
thresholding graph estimation. Three
regularization/thresholding parameter selection methods are
included in this package: (1)stability approach for
regularization selection (2) rotation information criterion (3)
extended Bayesian information criterion which is only available
for the graphical lasso.

Tuo Zhao

huge

High-Dimensional Undirected Graph Estimation

Haoming Jiang

Xinyu Fei

Han Liu

Kathryn Roeder

John Lafferty

Larry Wasserman

Xingguo Li

plot.sim function

<dl><dt>x</dt>
<dd>An object with S3 class <code>"sim"</code></dd>
<dt>...</dt>
<dd>System reserved (No specific usage)</dd></dl>

Arguments

Plot function for S3 class "sim" — plot.sim

<dl>

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


<dt>...</dt>
<dd>System reserved (No specific usage)</dd>

</dl>

plot.sim: Plot function for S3 class "sim"

Description

Usage

Arguments

See Also