# 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

 1 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 Jiajbc409@gmail.com 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

 1 2 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.