SimHetDat: Simulate Heterogeneous Data for Gaussian Graphical Models

In equSA: Learning High-Dimensional 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}=≤ft\{\begin{array}{ll} 0.5,&\textrm{if $≤ft| j-i \right|=1, i=2,...,(p-1),$}\\ 0.25,&\textrm{if $≤ft| j-i \right|=2, i=3,...,(p-2),$}\\ 1,&\textrm{if $i=j, i=1,...,p,$}\\ 0,&\textrm{otherwise.} \end{array}\right.

Value

data	nxp Heterogeneous Gaussian distributed data.
A	pxp adjacency matrix used for generating data.
label	The group indices for each observation.

Author(s)

Bochao [e-mail] and Faming Liang

References

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

Examples

library(equSA)
SimHetDat(n = 100, p = 200, M = 3, mu = 0.5, type = "band")

equSA documentation built on May 6, 2019, 1:06 a.m.