lassoNPN: Estimating the regression coefficients in NPNGMs with lasso

In DNetFinder: Estimating Differential Networks under Semiparametric Gaussian Graphical Models

Estimating the regression coefficients in NPNGMs with lasso

Description

The function "lassoNPN" computes the lasso estimates of the regression coefficents in NPNGMs for constructing the test statistic. The regression is based on a truncated (Winsorized) estimator for the transformation functions in NPNGMs.

Usage

lassoNPN(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). The truncation parameter in the Winsorized estimator is chosen as in Liu et al. (2009) to well balance the variance and bias.

Value

Estimated coefficients 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

lassoGGM()

Examples

Data1=read.table(system.file("extdata","Data1.txt",package="DNetFinder"),header=FALSE)
est_coefNPN=lassoNPN(Data1)

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