DNetGGM {DNetFinder} | R Documentation |
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 |
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)