plot_network

Visualization of network structures.

The goal of this package is to user-friendly realizing Gaussian
graphical model-based heterogeneity analysis.
Recently, several Gaussian graphical model-based heterogeneity
analysis techniques have been developed. A common methodological limitation
is that the number of subgroups is assumed to be known a priori, which
is not realistic. In a very recent study (Ren et al., 2022), a novel approach
based on the penalized fusion technique is developed to fully
data-dependently determine the number and structure of subgroups in
Gaussian graphical model-based heterogeneity analysis. It opens the door for utilizing
the Gaussian graphical model technique in more practical settings. Beyond
Ren et al. (2022), more estimations and functions are added, so
that the package is self-contained and more comprehensive and can
provide ``more direct'' insights to practitioners (with the
visualization function). Reference:
Ren, M., Zhang S., Zhang Q. and Ma S. (2022). Gaussian Graphical
Model-based Heterogeneity Analysis via Penalized Fusion.
Biometrics, 78 (2), 524-535.

Mingyang Ren

HeteroGGM

Gaussian Graphical Model-Based Heterogeneity Analysis

Sanguo Zhang

Qingzhao Zhang

Shuangge Ma

plot_network function

<dl><dt>summ</dt>
<dd>A list, the summary of the resulting network structures.</dd>
<dt>num_subgroup</dt>
<dd>Int/vector, the subgroup numbering.</dd>
<dt>plot.mfrow</dt>
<dd>Figure Layout.</dd>
<dt>vertex.size</dt>
<dd>The vertex size.</dd>
<dt>vertex.label.cex</dt>
<dd>The vertex label size.</dd>
<dt>vertex.label.dist</dt>
<dd>The distance of vertex labels.</dd>
<dt>edge.width</dt>
<dd>The edge width.</dd>
<dt>l</dt>
<dd>Node Coordinates.</dd></dl>

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plot_network: Visualization of network structures.

Description

Usage

Value

Arguments