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

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.

1 | ```
lassoNPN(Data_mat)
``` |

`Data_mat` |
A n by p data matrix, where each row represents one observation |

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.

Estimated coefficients matrix by lasso

Other estimators such as Dantzig selector or square-root lasso can also be used. See detailed discussion in Liu (2017) and Zhang (2017).

Qingyang Zhang

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()

1 2 | ```
Data1=read.table(system.file("extdata","Data1.txt",package="DNetFinder"),header=FALSE)
est_coefNPN=lassoNPN(Data1)
``` |

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