example.data

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

example.data function

<p>A list including:
data: The 600 x 20 matrix, the design matrix.
L0: The subgroup to which each sample truly belongs.
Mu0: The true mean parameters of 3 subgroups.
Theta0: The true precision matrices of 3 subgroups.
n_all: The total sample size.
K0: The true number of subgroups 3.</p>

Format

Some example data — example.data

Some example data

example.data: Some example data

Description

Arguments

Format

Examples