Description Usage Arguments Value Author(s) References Examples
Gaussian Graphical Mixture Models for learning a single highdimensional network structure from heterogeneous dataset.
1 
data 
nxp mixture Gaussian distributed dataset. 
A 
pxp true adjacency matrix for evaluating the performance. 
M 
The number of heterogeneous groups. 
alpha1 
The significance level of correlation screening in the ψlearning algorithm, see R package equSA for detail. In general, a high significance level of correlation screening will lead to a slightly large separator set, which reduces the risk of missing important variables in the conditioning set. In general, including a few false variables in the conditioning set will not hurt much the accuracy of the ψpartial correlation coefficient, the default value is 0.1. 
alpha2 
The significance level of ψpartial correlation coefficient screening for estimating the adjacency matrix, see equSA, the default value is 0.05. 
alpha3 
The significance level of integrative ψpartial correlation coefficient screening for estimating the adjacency matrix of GGMM method, the default value is 0.05. 
iteration 
The number of total iterations, the default value is 30. 
warm 
The number of burnin iterations, the default value is 20. 
RecPre 
The output of Recall and Precision values of our proposed method. 
Adj 
pxp Estimated adjacency matrix. 
label 
The estimated group indices for each observation. 
BIC 
The BIC scores for determining the number of groups M. 
Bochao Jiajbc409@gmail.com and Faming Liang
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, 12481265.
Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961977.
Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Regularized Optimization Algorithm for HighDimensional Missing Data Problems and Beyond. Submitted to Journal of the Royal Statistical Society Series B.
Jia, B. and Liang, F. (2018). Learning Gene Regulatory Networks with HighDimensional Heterogeneous Data. Accept by ICSA Springer Book.
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library(equSA)
result < SimHetDat(n = 100, p = 200, M = 3, mu = 0.5, type = "band")
Est < GGMM(result$data, result$A, M = 3, iteration = 30, warm = 20)
## plot network by our estimated adjacency matrix.
plotGraph(Est$Adj)
## plot the RecallPrecision curve
plot(Est$RecPre[,1], Est$RecPre[,2], type="l", xlab="Recall", ylab="Precision")

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