
Gaussian Graphical Mixture Models for learning high-dimensional network with simulated heterogeneous data.
GGMM(data, A, M, alpha1 = 0.1, alpha2 = 0.05, alpha3 = 0.05, iteration = 30, warm = 20)
The number of heterogeneous groups.
The significance level of correlation screening in the
The significance level of
The significance level of integrative
The number of total iterations, the default value is 30.
The number of burn-in iterations, the default value is 20.
The output of Recall and Precision values of our proposed method.
The estimated group indices for each observation.
The BIC scores for determining the number of groups
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.
Liang, F., Jia, B., Xue, J., Li, Q., and Luo, Y. (2018). An Imputation Regularized Optimization Algorithm for High-Dimensional 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 High-Dimensional Heterogeneous Data. Accept by ICSA Springer Book.
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library(GGMM)
library(huge)
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.
huge.plot(Est$Adj)
## plot the Recall-Precision curve
plot(Est$RecPre[,1], Est$RecPre[,2], type="l", xlab="Recall", ylab="Precision")
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