plearn.moral: Learning Moral graph based on p-learning algorithm.

In equSA: Learning High-Dimensional Graphical Models

Description

Construct moral graph of Bayeisan network for mixed types of random varaibles based on p-learning algorithm. Each variable in the dataset can be either binary or Gaussian distributed.

Usage

plearn.moral(data, gaussian.index = NULL, binary.index = NULL,
alpha1 = 0.1, alpha2 = 0.02, restrict = FALSE, score.only=FALSE)

Arguments

data	The data matrix, of dimensions nxp. Each row is an observation vector and each column is a variable.
gaussian.index	The index vector of Gaussian nodes. The default value is NULL. If not specified, the system will automatically determine the index for each variable.
binary.index	The index vector of binary nodes. The default value is NULL. If not specified, the system will automatically determine the index for each variable.
alpha1	The significant level of correlation screening in p-learning algorithm. The default value is 0.1.
alpha2	The significant level of partial correlation screening in p-learning algorithm. The dafault value is 0.02.
restrict	Should edge restriction applied? (logical). If TRUE, we assume that there should be no edge among binary variables. The default is FALSE.
score.only	If TRUE, it only reports z-scores for all pair of variables. The default is FALSE.

Details

This is the function that implements the p-learning algorithm for learning moral graph of Bayesian Network with mixed type of random variables.

Value

A list of two objects.

moral.matrix	The estimated adjacency matrix of moral graph.
score	The estimated z-scores for all pair of variables.

Author(s)

Suwa Xu Bochao Jia and Faming Liang

References

Xu, S., Jia, B., and Liang, F. (2018). Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data. Submitted.

Examples

 library(equSA)
 data.graph <- DAGsim(n = 200, p = 100, type="AR(2)", p.binary = 50)$data
 plearn.moral(data.graph, alpha1 = 0.1, alpha2 = 0.02)

equSA documentation built on May 6, 2019, 1:06 a.m.