DNetNPN: Testing for the structural difference between two NPNGMs

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/DNetNPN.R

Description

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

Usage

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

Arguments

Data_mat1

An n1 by p data matrix for the first NPNGM, where each row represents one observation

Data_mat2

An n2 by p data matrix for the second NPNGM, where each row represents one observation

Beta_mat1

A p-1 by p coefficient matrix for the first NPNGM, 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 NPNGM. See Beta_mat1 for details.

alpha

User-specified FDR level

Details

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

Value

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

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

DNetGGM()

Examples

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Data1=read.table(system.file("extdata","Data1.txt",package="DNetFinder"),header=FALSE)
Data2=read.table(system.file("extdata","Data2.txt",package="DNetFinder"),header=FALSE)
BetaNPN1=read.table(system.file("extdata","BetaNPN1.txt",package="DNetFinder"),header=FALSE)
BetaNPN2=read.table(system.file("extdata","BetaNPN2.txt",package="DNetFinder"),header=FALSE)
est_DNNPN=DNetNPN(Data1,Data2,BetaNPN1,BetaNPN2,alpha=0.1)

DNetFinder documentation built on May 2, 2019, 10:23 a.m.