equSA (version 1.2.1)

plearn.struct: Infer network structure for mixed types of random variables.

Description

Learning graphical model structure 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.struct(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 \(n\)x\(p\). 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 parent and children screening in \(p\)-learning algorithm. The default value is 0.1.

alpha2

The significant level of moral graph 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.

Value

A list of two objects.

Adj

The estimated adjacency matrix of undirect network.

score

The estimated \(z\)-scores for all pair of variables.

Details

This is the function that implements the \(p\)-learning algorithm for learning the undirect network structure for mixed types of random variables.

References

Jia, B., and Liang, F. (2018) Joint Estimation of Restricted Mixed Graphical Models. manuscript.

Examples

# NOT RUN {
 library(equSA)
 data.graph <- DAGsim(n = 200, p = 100, type="AR(2)", p.binary = 50)$data
 plearn.struct(data.graph, alpha1 = 0.1, alpha2 = 0.02)
# }
# NOT RUN {



# }