equSA (version 1.1.5)

JGGM: Joint estimation of Multiple Gaussian Graphical Models

Description

Infer networks from Multiple Gaussian data from differnt groups using our proposed algorithm.

Usage

JGGM(data,ALPHA1=0.05,ALPHA2=0.01)

Arguments

data

a list of nxp data matrices. n can be different for each dataset but p should be the same.

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 \(\psi\)-partial correlation coefficient.

ALPHA2

The significance level of \(\psi\) screening.

Value

A list of two elements:

A

An array of multiple adjacency matrices of networks which is a Mxpxp array. M is the number of dataset groups, p is the dimension of variables in each group.

score

Estimated integrative \(\psi\) scores matrix for all pairs of different datasets. The first two columns denote the pair indices of variables i and j and the rest columns denote the Estimated integrative \(\psi\) scores for this pair in different groups.

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Details

This is the function that can jointly estimate multiple GGMs which can integrate the information throughtout all datasets. The method mainly consists three steps: (i) separate estimation of \(\psi\)-scores for each dataset, (ii) identifies possible changes of each edge across different groups and integrate the \(\psi\) scores across different groups simultaneously and (iii) apply multiple hypothesis test to identify edges using integrated \(\psi\) scores.

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.

Jia, B. and Liang, F. (2017) Joint Estimation of Multiple Gaussian Graphical Models via Multiple Hypothesis Tests (preparing)

Examples

# NOT RUN {
#library(equSA)
#data(SR0)
#data(TR0)
#data_all <- vector("list",2)
#data_all[[1]] <- SR0
#data_all[[2]] <- TR0
#JGGM(data_all,ALPHA1=0.05,ALPHA2=0.01)
         
# }