| lassoGGM {DNetFinder} | R Documentation |
Estimating the regression coefficients in GGMs with lasso
Description
The function "lassoGGM" computes the lasso estimates of the regression coefficents in GGMs for constructing the test statistic.
Usage
lassoGGM(Data_mat)
Arguments
Data_mat |
A n by p data matrix, where each row represents one observation |
Details
The tuning parameter in the lasso regression is chosen as in Liu (2017).
Value
The estimated coefficient matrix by lasso
Note
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
lassoNPN()
Examples
Data1=read.table(system.file("extdata","Data1.txt",package="DNetFinder"),header=FALSE)
est_coefGGM=lassoGGM(Data1)