GGMM: Learning high-dimensional Gaussian Graphical Models with...

In equSA: Learning High-Dimensional Graphical Models

Description

Gaussian Graphical Mixture Models for learning a single high-dimensional network structure from heterogeneous dataset.

Usage

GGMM(data, A, M, alpha1 = 0.1, alpha2 = 0.05, alpha3 = 0.05, iteration = 30, warm = 20)

Arguments

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 ψ-learning 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 ψ-partial correlation coefficient, the default value is 0.1.
alpha2	The significance level of ψ-partial correlation coefficient screening for estimating the adjacency matrix, see equSA, the default value is 0.05.
alpha3	The significance level of integrative ψ-partial 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 burn-in iterations, the default value is 20.

Value

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.

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.

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.

Examples

 
 
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$Adj)
## plot the Recall-Precision curve
plot(Est$RecPre[,1], Est$RecPre[,2], type="l", xlab="Recall", ylab="Precision")  

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