SimGraDat: Simulate Incomplete Data for Gaussian Graphical Models

In IROmiss: Imputation Regularized Optimization Algorithm

Description

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

Usage

SimGraDat(n = 200, p = 100, type = "band", rate = 0.1)

Arguments

n	Number of observations, default of 200.
p	Number of covariates, default of 100.
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.
rate	Missing rate, the default value is 0.1.

Value

data	nxp Gaussian distributed data with missing.
A	pxp adjacency matrix used for generating data.

Author(s)

Bochao [e-mail] and Faming Liang

References

Liang, F., Song, Q. and Qiu, P. (2015). An Equivalent Measure of Partial Correlation Coefficients for High Dimensional Gaussian Graphical Models. J. Amer. Statist. Assoc., 110, 1248-1265.

Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.

Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Regularized Optimization Algorithm for High-Dimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B.

Examples

library(IROmiss)
SimGraDat(n = 200, p = 100, type = "band", rate = 0.1)

IROmiss documentation built on March 26, 2020, 5:56 p.m.