DNetGGM: Testing for the structural difference between two GGMs

In DNetFinder: Estimating Differential Networks under Semiparametric Gaussian Graphical Models

Testing for the structural difference between two GGMs

Description

The function "DNetGGM" tests for the structural difference between two Gaussian graphical models with false discovery rate control.

Usage

DNetGGM(Data_mat1,Data_mat2,Beta_mat1,Beta_mat2,alpha)

Arguments

Data_mat1	An n1 by p data matrix for the first GGM, where each row represents one observation
Data_mat2	An n2 by p data matrix for the second GGM, where each row represents one observation
Beta_mat1	A p-1 by p coefficient matrix for the first GGM, where each column contains the regression coefficients of one variable on the other p-1 variables.
Beta_mat2	A p-1 by p coefficient matrix for the second GGM. See Beta_mat1 for details.
alpha	User-specified FDR level

Details

The multiple testing procedure asymptotically controls the false discovery rate. See Liu (2017) for details.

Value

Estimated differential network, where "1" represents a differential edge and "0" represents a common edge (or no edge) between two GGMs.

Note

Besides lasso, other estimators such as Dantzig selector or square-root lasso can also be used. See detailed discussion in Liu (2017) and Zhang (2017).

Author(s)

Qingyang Zhang

References

Li, X., Zhao, T., Yuan, X., Liu, H. (2015). The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R. Journal of Machine Learning Research, 16:553-557

Liu, H., Lafferty, J., Wasserman, L. (2009). The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. Journal of Machine Learning Research, 10:2295-2328

Liu, W. (2017). Structural Similarity and Difference Testing on Multiple Sparse Gaussian Graphical Models. Annals of Statistics, 45(6):2680-2707

Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B, 58(1):267-288

Zhang, Q. (2017). Structural Difference Testing on Multiple Nonparanormal Graphical Models with False Discovery Rate Control. Preprint.

See Also

DNetNPN()

Examples

Data1=read.table(system.file("extdata","Data1.txt",package="DNetFinder"),header=FALSE)
Data2=read.table(system.file("extdata","Data2.txt",package="DNetFinder"),header=FALSE)
BetaGGM1=read.table(system.file("extdata","BetaGGM1.txt",package="DNetFinder"),header=FALSE)
BetaGGM2=read.table(system.file("extdata","BetaGGM2.txt",package="DNetFinder"),header=FALSE)
est_DNGGM=DNetGGM(Data1,Data2,BetaGGM1,BetaGGM2,alpha=0.1)

DNetFinder documentation built on March 7, 2023, 7:13 p.m.