diffR: Detect difference between two networks.

In equSA: Learning High-Dimensional Graphical Models

Description

Detecting significant different edges between two networks.

Usage

diffR(Data1,Data2,ALPHA1=0.05,ALPHA2=0.05)

Arguments

Data1	a n_1xp data matrix.
Data2	a n_2xp data matrix.
ALPHA1	The significance level of correlation screening for each dataset. 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 for integrative estimation of ψ scores.

Value

A	pxp adjacency matrix of the combined graph.

Author(s)

Bochao [e-mail] 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(SR0)
data(TR0)
diffR(SR0,TR0)

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