DNetFinder-package | R Documentation |
Provides a modified hierarchical test (Liu (2017) <doi:10.1214/17-AOS1539>) for detecting the structural difference between two Semiparametric Gaussian graphical models. The multiple testing procedure asymptotically controls the false discovery rate (FDR) at a user-specified level. To construct the test statistic, a truncated estimator is used to approximate the transformation functions and two R functions including lassoGGM() and lassoNPN() are provided to compute the lasso estimates of the regression coefficients.
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Qingyang Zhang
Maintainer: Qingyang Zhang <qz008@uark.edu>
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.
lassoGGM(), lassoNPN(), DNetGGM(), DNetNPN()
library(flare) library(DNetFinder) 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) 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_coefGGM=lassoGGM(Data1) est_coefNPN=lassoNPN(Data1) est_DNGGM=DNetGGM(Data1,Data2,BetaGGM1,BetaGGM2,alpha=0.1) est_DNNPN=DNetNPN(Data1,Data2,BetaNPN1,BetaNPN2,alpha=0.1)
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