equSA (version 1.2.1)

combineR: Combine two networks.

Description

Combine two networks to a single one.

Usage

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

Arguments

Data1

a \(n_1\)x\(p\) data matrix.

Data2

a \(n_2\)x\(p\) 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 \(\psi\)-partial correlation coefficient.

ALPHA2

The significance level of \(\psi\) screening for integrative estimation of \(\psi\) scores.

Value

A

\(p\)x\(p\) Adjacency matrix of the combined graph.

%% ...

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

# NOT RUN {
# }
# NOT RUN {
library(equSA)
data(SR0)
data(TR0)
combineR(SR0,TR0)
# }
# NOT RUN {
         
# }