| Evaluation.DAG {TransGraph} | R Documentation |
Evaluation function for the estimated DAG.
Description
Evaluation function for the estimated DAG.
Usage
Evaluation.DAG(estimated.adjace, true.adjace, type.adj=2)
Arguments
estimated.adjace |
The target data, a n * p matrix, where n is the sample size and p is data dimension. |
true.adjace |
The auxiliary data in K auxiliary domains, a list with K elements, each of which is a nk * p matrix, where nk is the sample size of the k-th auxiliary domain. |
type.adj |
The type of adjacency matrix. 1: the entries of matrix contains just two value, 0 and 1, which indicate the existence of edges; 2 (default): the matrix also measures connection strength, and 0 means no edge. |
Value
A result list including Recall, FDR, F1score, MCC, Hamming Distance,and estimated error of adjacency matrix on F-norm.
Author(s)
Ruixaun Zhao ruixuanzhao2-c@my.cityu.edu.hk.
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.