| TLLiNGAM {TransGraph} | R Documentation |
Learning linear non-Gaussian DAG via topological layers.
Description
Learning linear non-Gaussian DAG via topological layers.
Usage
TLLiNGAM (X, hardth=0.3, criti.val=0.01, precision.refit = TRUE,
precision.method="glasso", B.refit=TRUE)
Arguments
X |
The n * p sample matrix, where n is the sample size and p is data dimension. |
hardth |
The hard threshold of regression. |
criti.val |
The critical value of independence test based on distance covariance. |
precision.refit |
Whether to perform regression for re-fitting the coefficients in the precision matrix to improve estimation accuracy, after determining the non-zero elements of the precision matrix. The default is True. |
precision.method |
Methods for Estimating Precision Matrix, which can be selected from "glasso" and "CLIME". |
B.refit |
Whether to perform regression for re-fitting the coefficients in structural equation models to improve estimation accuracy, after determining the parent sets of all nodes. The default is True. |
Value
A result list including:
- A
The information of layer.
- B
The coefficients in structural equation models.
Author(s)
Ruixuan Zhao ruixuanzhao2-c@my.cityu.edu.hk, Xin He, and Junhui Wang
References
Zhao, R., He X., and Wang J. (2022). Learning linear non-Gaussian directed acyclic graph with diverging number of nodes. Journal of Machine Learning Research.
Examples
library(TransGraph)
# load example data from github repository
# Please refer to https://github.com/Ren-Mingyang/example_data_TransGraph
# for detailed data information
githublink = "https://github.com/Ren-Mingyang/example_data_TransGraph/"
load(url(paste0(githublink,"raw/main/example.data.singleDAG.RData")))
true_adjace = example.data.singleDAG$true_adjace
t.data = example.data.singleDAG$X
res.single = TLLiNGAM(t.data)
Evaluation.DAG(res.single$B, true_adjace)$Eval_result