equSA (version 1.2.1)

SimHetDat: Simulate Heterogeneous Data for Gaussian Graphical Models

Description

Simulate Heterogeneous data with a band structure, which can be used in GGMM(data,...) for estimating the structure of the Gaussian graphical network.

Usage

SimHetDat(n = 100, p = 200, M = 3, mu = 0.3, type = "band")

Arguments

n

Number of observations for each group, default of 100.

p

Number of covariates for each observation, default of 200.

M

Number of latent groups for the simulated dataset choose 2 or 3, default of 3.

mu

The mean difference among groups. If \(M=3\), the mean of three groups are \(-mu,0,mu\), respectively. If \(M=2\), the mean of two groups are \(0,mu\), respectively.

type

type=="band" which denotes the band structure, with precision matrix $$ C_{i,j}=\left\{\begin{array}{ll} 0.5,&\textrm{if $\left| j-i \right|=1, i=2,...,(p-1),$}\\ 0.25,&\textrm{if $\left| j-i \right|=2, i=3,...,(p-2),$}\\ 1,&\textrm{if $i=j, i=1,...,p,$}\\ 0,&\textrm{otherwise.} \end{array}\right. $$

Value

data

\(n\)x\(p\) Heterogeneous Gaussian distributed data.

A

\(p\)x\(p\) adjacency matrix used for generating data.

label

The group indices for each observation.

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References

Jia, B. and Liang, F. (2018). Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data. Accept by ICSA Springer Book.

Examples

# NOT RUN {
library(equSA)
SimHetDat(n = 100, p = 200, M = 3, mu = 0.5, type = "band")
# }