equSAR: An equvalent mearsure of partial correlation coeffients

In equSA: Learning High-Dimensional Graphical Models

Description

Infer networks from Gaussian data by ψ-learning algorithm.

Usage

equSAR(iData,iMaxNei,ALPHA1=0.05,ALPHA2=0.05,GRID=2,iteration=100)

Arguments

iData	a nxp data matrix.
iMaxNei	Neiborhood size in correlation screening step, default to n/log(n), where n is the number of observation.
ALPHA1	The significance level of correlation screening. In general, a high significance level of correlation screening will lead to a slightly large separator set S_{ij}, which reduces the risk of missing some important variables in the conditioning set. Including a few false variables in the conditioning set will not hurt much the accuracy of the ψ-partial correlation coefficient.
ALPHA2	The significance level of ψ screening.
GRID	The number of components for the ψ scores. The default value is 2.
iteration	Number of iterations for screening. The default value is 100.

Details

This is the main function of the package that fit the Gaussian Graphical Models and obtain the ψ scores and adjacency matrix.

Value

A list of two elements:

Adj	pxp adjacency matrix of the generated graph.
score	Estimated ψ score matrix which has 3 columns. The first two columns denote the pair indices of variables i and j and the last column denote the calculated ψ scores for this pair.

Author(s)

Bochao Jia and Faming Liang

References

Liang, F., Song, Q. and Qiu, P. (2015). An Equivalent Measure of Partial Correlation Coefficients for High Dimensional Gaussian Graphical Models. J. Amer. Statist. Assoc., 110, 1248-1265.

Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.

Examples

library(equSA)
data <- GauSim(100,100)$data
equSAR(data)

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